Bridging Perspectives

People’s ability to change their social and economic circumstances may be constrained by various forms of social, cultural and political domination. Thus to consider a social actor’s particular lifeworld in which the research is embedded assists in the understanding of how and why different trajectories of change occur or are hindered and how those changes fundamentally affect livelihood opportunities and constraints. In seeking to fulfill this condition this thesis adopted an actor-oriented approach to the study of rural livelihoods. A comprehensive livelihoods study requires grasping how social reality is being historically constituted. That means to understand how the interaction of modes of production and symbolical reproduction produces the socio-space. Research is here integrated to action through the facilitation of farmer groups. The overall aim of the groups was to prompt agency, as essential conditions to build more resilient livelihoods. The smallholder farmers in the Mabalane District of Mozambique are located in a remote semi-arid area. Their livelihoods customarily depend at least as much on livestock as on (mostly) rain-fed food crops. Increased climate variability exerts pressure on the already vulnerable production system. An extensive 10-month duration of participant observation divided into 3 periods of fieldwork structured the situated multi-method approach that drew on a set of interview categories. The actor-oriented appraisal of livelihoods worked in building a mutually shared definition of the situation. This reflection process was taken up by the facilitation of the farmer groups, one in Mabomo and one in Mungazi, which used an inquiry iteratively combining individual interviews and facilitated group meetings. Integration of action and reflection was fundamental for group constitution as spaces for communicative action. They needed to be self-organized and to achieve understanding intersubjectively, as well as to base action on cooperation and coordination. Results from this approach focus on how learning as collaboratively generated was enabled, or at times hindered, in (a) selecting meaningful options to test; (b) in developing mechanisms for group functioning; and (c) in learning from steering the testing of options. The study of livelihoods looked at how the different assets composing livelihoods are intertwined and how the increased severity of dry spells is contributing to escalated food insecurity. The reorganization of the social space, as households moved from scattered homesteads to form settlements, further exerts pressure on the already scarce natural resource-based livelihoods. Moreover, this process disrupted a normative base substantiating the way that the use of resources is governed. Hence, actual livelihood strategies and response mechanisms turn to diversification through income-generating activities that further increase pressure on the resource-base in a rather unsustainable way. These response mechanisms are, for example, the increase in small-livestock keeping, which has easier conversion to cash, and charcoal production. The latter results in ever more precarious living and working conditions. In the majority of the cases such responses are short-term and reduce future opportunities in a downward spiral of continuously decreasing assets. Thus, by indicating the failure of institutions in the mediation of smallholders’ adaptive capabilities, the livelihood assessment in Mabomo and Mungazi sheds light on the complex underlying structure of present day social vulnerability, linking the macro-context to the actual situation. To assist in breaking this state of “subordination”, shaped by historical processes, weak institutions and food insecurity, the chosen approach to facilitation of farmer groups puts farmer knowledge at the center of an evolving process of intersubjective co-construction of knowledge towards emancipation.

Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).