Remotely-sensed data coupled with GIS-derived biophysical data have been key components in land use studies during the past decades. Natural Resource Managers relied on biophysically-oriented 'top down' approaches for the design of land and water management systems as a basis for regional planning. However, it is increasingly realised that the systems originating from these approaches of ten have limited success with land users. To generatepractically applicable and attractive options for farmers, consideration of the aspirations of land users and their involvement from the plan formulation to the implementation stages is essential. The challenge for biophysical scientists involved in traditional land evaluation and land use planning therefore is the integration of socio-economic characteristics with biophysical data for land use analysis 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 tooI, 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 goalmodelconsidered 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 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. The research feedback workshop conducted in the study area with the stakeholders was useful in terms of eliciting views on the relevance of the researchtethe users. The enthusiastic participation of the users, the demand for extending the study spatiallyteneighbouring districts and for the software developed te generate scenarios was encouraging.
