A discussion support model for a regional dairy-pasture system with an example from Reunion island

Reunion Island, situated in the Indian Ocean, presents a unique case study for modelling regional bio-economic parameters of the dairy industry. It is a good example of a closed system for several parameters of the model such as movement of animals, labour, consumption and available land. The existence of several agro-ecological zones from tropical to temperate, and various different types of terrain and vegetation presents another unique opportunity to study the impact of these features on the dairy industry. The present study models the dairy sector at a regional (island) level to study the impact of new or adapted agricultural policies in relation to changes in subsidy levels, price fluctuations and environmental policies (mainly nitrogen management). The model can be used to generate a number of scenarios to explore the effects of various policy measures, such as fixing the stocking rate according to EU norms, increasing or decreasing the milk subsidy, intensification (such as an increase in milk production to the allotted quota of 40 million litres/yr) and varying labour/price constraints (such as a reduction in labour hours or an increase or decrease in the milk price). The model is being utilized by the local dairy cooperative as a discussion support tool to study the implications at the regional scale of expanding the sector and assessing its economic, environmental and social impac

Integrating Geo-information Models with Participatory Approaches

In this thesis we demonstrate some methods to integrate biophysical data with socio-economic variables with applications in agricultural land use analysis. Part of Nizamabad District of Andhra Pradesh State in India is considered for developing and testing the methods developed. First the study area is stratified as a pre-field work exercise for a focused land use analysis. Stratification of the land into categories on the basis of land use analysis objectives, such as crop management improvement, crop selection and conservation helped focus on these distinct areas with different analysis requirements. The relations between ‘land’ as a biophysical factor and its ‘use’ as a socio-economic factor were analysed using GIS techniques to spatially differentiate these categories. Two categories viz., Crop Management Improvement and Crop Selection were analysed further. Identifying yield-limiting factors in support of planning and extension agencies is the focus of study in areas identified for Crop Management Improvement. While traditional yield gap studies compared yields at research stations and in farmers’ fields, we considered yield variability among farmers’ fields in similar socio-economic and environmental conditions. In this situation, the yield gaps are mainly due to differences in management practices. What if?- scenarios, generated using the multiple goal optimisation modelling tool, were integrated with a stakeholder communication matrix (SCM) in the Crop Selection areas. SCM indicates the level of communication and information-sharing among key stakeholders in the district. The multiple goal model considered the aspirations of various stakeholders and the matrix presented the communication and information-sharing dynamics, understanding of which is essential for participatory land use analysis. Integration of the goal model with the SCM allowed identification of the possible bottlenecks in the implementation of the model results, allowing resource managers to initiate curative measures where required. Fuzzy modelling of farmers’ perceptions of land suitability emphasised the need for biophysical planners to consider the views of farmers while formulating land viii use options. The preference of farmers for crops was based on variables such as cropping season, soils and water availability. The study explores similarities and contrasts in the way scientists and farmers perceive land suitability

Fuzzy modeling of farmers' knowledge for land suitability classification

In a case study, we demonstrate fuzzy modeling of farmers' knowledge (FK) for agricultural land suitability classification using GIS. Capture of FK was through rapid rural participatory approach. The farmer respondents consider, in order of decreasing importance, cropping season, soil color, soil texture, soil depth and slope as factors of suitability of their land for certain crops. Multi-class fuzzy sets using S-membership functions were generated for soil texture, soil depth and slope because of correlation or equivalence between farmers' definitions and scientific classifications of such land characteristics. In contrast, binary fuzzy relations, which are also fuzzy sets, were generated for cropping season and soil color because farmers' perceptions of such land characteristics are intrinsically binary. Despite variations in individual farmers' perceptions of land suitability, 12 unique FK rules for classifying land suitability were defined by hierarchical grouping of such different perceptions based on decreasing importance of factors. The FK rules form inference engines in combining fuzzy factor maps using appropriate fuzzy operators to create agricultural land suitability maps. Suitability maps resulting from application of Fuzzy AND and Fuzzy OR operators were found consistent with the FK rules. The FK-based suitability maps indicate either agreement or conflict with a land resource development plan (LRDP) for the case study area. Results of the study indicate usefulness of fuzzy modeling in FK-based classification of agricultural land suitability, which could provide useful information for optimum land-use planning

Enhancement of area-specific land-use objectives for land development

Maps of land-use classes and soil series were analysed to identify areas having specific priorities with respect to agricultural land-use analysis. Remote sensing data supported by field investigations was used to generate land-use and soil maps. Present relationships between soils and associated land cover/use are analysed and patterns in these relationships are identified using GIS techniques. Relationships observed on the basis of a priori knowledge of the area and the available statistics are compared and these relationships in the field and through interviews with farmers are correlated. This allows three land-use analysis objectives to be formulated: crop management improvement; crop selection; and conservation. The results can be used to focus the efforts of planning and extension services in the area. The method was tested using a participatory rural appraisal in eighteen villages in which the areas for the three land-use analysis objectives were identified. The findings are that the areas identified for crop management improvement require knowledge about management practices for a specific crop to optimize yield and water use. Most areas identified for crop selection are occupied by smallholder subsistence farmers with insufficient water for irrigation, and a lack of contact with the extension service. In these areas, identifying suitable crops to minimize risk and allow subsistence for the resource-poor farmers may be the priority. In areas identified for conservation the question to be addressed is whether to grow a crop at all, or to encourage alternative activities

Enhancement of area - specific land - use objectives for land development

Maps of land-use classes and soil series were analysed to identify areas having specific priorities with respect to agricultural land-use analysis. Remote sensing data supported by field investigations was used to generate land-use and soil maps. Present relationships between soils and associated land cover/use are analysed and patterns in these relationships are identified using GIS techniques. Relationships observed on the basis of a priori knowledge of the area and the available statistics are compared and these relationships in the field and through interviews with farmers are correlated. This allows three land-use analysis objectives to be formulated: crop management improvement; crop selection; and conservation. The results can be used to focus the efforts of planning and extension services in the area. The method was tested using a participatory rural appraisal in eighteen villages in which the areas for the three land-use analysis objectives were identified. The findings are that the areas identified for crop management improvement require knowledge about management practices for a specific crop to optimize yield and water use. Most areas identified for crop selection are occupied by smallholder subsistence farmers with insufficient water for irrigation, and a lack of contact with the extension service. In these areas, identifying suitable crops to minimize risk and allow subsistence for the resource-poor farmers may be the priority. In areas identified for conservation the question to be addressed is whether to grow a crop at all, or to encourage alternative activities