Ámbito de Plan Especial

In this study, the integrative agent-centred (IAC) framework (Feola and Binder, 2010) was used as a basis for designing a structured survey to collect semi-quantitative data for a range of explanatory variables that potentially drive shrimp farmer behaviour in Chanthaburi Province. The IAC framework is agent-centred and supports the

understanding of farmers' behaviour consistently with the perspective of agricultural systems as complex social–ecological systems. It combines different behavioural drivers, bridges between micro and macro levels, and depicts a potentially varied model of human agency (Feola and Binder 2010). The IAC framework has previously been used to study farmer behaviour in relation to production intensity in agricultural systems (Feola and Binder 2010, 2010b) and was thus deemed suitable for supporting the

research design for this study. The IAC framework is based on: (i) an explicit and well- motivated behavioural theory; (ii) an integrative approach; and (iii) feedback processes between agents’ behaviour and system’s dynamics.

The components of the IAC framework are based on the integration of the Structuration Theory (Giddens 1984) and the Theory of Interpersonal Behaviour

(Triandis 1980). In combination, these two theories permit an improved approach to the investigation of farmer behaviour because they frame the feedback processes occurring between the farmer and the system and are thus well suited to investigating dynamic processes in the context of SES (Galt 2008).

The Structuration Theory (ST) states that actors influence and are influenced by social structures. The Theory of Interpersonal Behaviour (TIB) is a psychological

theoretical framework developed to explain individuals' ‘interpersonal’ behaviour. The TIB states that intentions, habit, physiological arousal and contextual factors influence an agent’s behaviour. The TIB is believed to be more comprehensive compared to other behavioural theories (Jackson 2004) because it includes a wide array of potentially relevant drivers of behaviour, thus making it appropriate for studying decision making in complex systems such as in agriculture (Scoones et al. 2007).

The IAC framework guides selection of potential explanatory factors to be

considered in the analysis of farmer behaviour and the identification of the relationships among them. The questions in the survey corresponded to different classes of

behavioural drivers outlined in the IAC framework (Figure 3). These included:

Contextual factors (i.e. facilitating conditions or barriers), Habit (the frequency of past behaviour), Expectations (beliefs about the outcomes, their probability and their value), Subjective culture (social norms, roles, values), and Affect (the feelings associated with the act). Each of the behavioural drivers were measured through one or more questions in the survey (see Appendix for full survey). Each survey employed the same structured set of questions and no prompt questions were used during the surveys.

Figure 5.3. The IAC Framework (Feola and Binder 2010).

To enable consistency in the data across study sites of Khlung and Laem Sing, and to make the timeframe as close as possible to the survey time, the questions referred to specific timeframes of either one production cycle, one year, or two years, as relevant depending on the question. The survey design aimed to generate data from shrimp farmers working across a range of shrimp farm intensity types, from low-intensity traditional polyculture systems to more technologically advanced intensive shrimp monoculture, so that data could be compared across farm management intensity categories.

Fieldwork was conducted between February and May 2017. A total of 102 shrimp farmers and farm workers were surveyed. Respondents were selected to provide a wide geographical cover across the survey area, and a relevant sample of the shrimp farmers in the area, avoiding biases associated with particular locations and shrimp farm sizes. Respondents were sought systematically by visiting farms and houses along the coastal Province area, and through snowball sampling (Goodman 1961). All surveys were conducted on an individual shrimp farmer basis to ensure that the responses reflected

personal information. In 6 of the 102 cases, the owner of the shrimp farm did not live on the farm, or was only present occasionally, and therefore the farm operator was

interviewed instead. These surveys were subsequently removed from the sample.

5.2.4 Data analysis

In order to characterize the socio-economic context of farmers farming at different levels of intensity and to be able to then compare the behaviour of shrimp farmers across farm intensity types, survey respondents were first classified into farm intensity types based on technical similarity within groups with regard to production intensity. Survey data were used to characterize the socio-economic (including demographic and market related) factors associated with each level of farming intensity (Table 1). Three production intensity proxy variables were used to define farm intensity type: ‘shrimp yield (kg ha crop)’, ‘shrimp stocking density (PL m2_{)’, and ‘number of shrimp crops}

produced per year’. The grouping of farms under each of the three key variables was based on FAO farm type classifications (extensive ‘low intensity’, semi-intensive ‘medium intensity’, and intensive ‘high intensity’) for the two principal brackish water shrimp species cultured in the study region, P. monodon (Black tiger shrimp; FAO 2018c) and L. vannamei (White shrimp; FAO 2018b).

For the three production intensity proxy variables, the minimum and maximum values for each species were first calculated separately for each individual pond. Minimum and maximum values were then assigned to one of the three production intensity classifications (‘low’, ‘medium’, or ‘high’ intensity). Where minimum and maximum values fell between two intensity categories (for example, minimum = ‘medium intensity’ and maximum = ‘high intensity’), then the mean of the variable was used. If ponds of a farm fell in more than one of the intensity categories (for example, 5 ponds for ‘high intensity’ and 1 pond for ‘medium intensity’), then the farm was

allocated to the modal farm type (i.e. ‘high intensity’ in the example).

The survey was designed to produce either quantitative responses (e.g. farm size, number of employed labour), or semi-quantitative data whereby all of the answers to qualitative questions were placed on a either a 3- or 5-point scale. For example, when asked “approximately what proportion of your shrimp survived on your last harvest?”, the interviewee was given a choice of 5 answers ranging from 1= none to 5= all. As a result, all of the data generated from the survey was numerical and therefore no thematic analysis was conducted.

Following identification of the three farm intensity types, survey responses which related to the internal and external behavioural drivers (Figure 3) were compared between farm intensity types. Where differences in responses were found between farm intensity types, the significance level of the difference was statistically tested using the non-parametric Kruskal-Wallis (K-W) H test, followed by the Dunn post hoc multiple comparisons test, where appropriate. Drivers that were found to be statistically different were treated as the determinants of adopting a particular shrimp farming production intensity. All statistical analysis was performed using the software R. Differences at the 0.05 level were considered significant.

5.3 Results