Statistics for Spatial Data.

The objective of this paper is to determine the ability of farmers in Africa to detect climate change, and to ascertain how they have adapted to whatever climate change they believe has occurred. The paper also asks farmers whether they perceive any barriers to adaptation and attempts to determine the characteristics of those farmers who, despite claiming to have witnessed climate change, have not yet responded to it. The study is based on a large-scale survey of agriculturalists in 11 African countries. The survey reveals that significant numbers of farmers believe that temperatures have already increased and that precipitation has declined. Those with the greatest experience of farming are more likely to notice climate change. Further, neighboring farmers tell a consistent story. There are important differences in the propensity of farmers living in different locations to adapt and there may be institutional impediments to adaptation in some countries. Although large numbers of farmers perceive no barriers to adaptation, those that do perceive them tend to cite their poverty and inability to borrow. Few if any farmers mentioned lack of appropriate seed, security of tenure, or market accessibility as problems. Those farmers who perceive climate change but fail to respond may require particular incentives or assistance to do what is ultimately in their own best interests. Although experienced farmers are more likely to perceive climate change, it is educated farmers who are more likely to respond by making at least one adaptation.

/pdf/the-perception-of-and-adaptation-to-climate-change-in-africa-7ud1nj8572.pdf

The perception of and adaptation to climate change in Africa

Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.

/pdf/multiobjective-evolutionary-algorithms-to-identify-highly-4g00yfahq1.pdf

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

From land cover change to land function dynamics: a major challenge to improve land characterization

http://www.ku.ac.ke/schools/environmental/images/stories/docs/communicating_complexity.pdf

Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development

http://www.smallholderdairy.org/publications/Journal%20publications/st/Staal%20et%20al-2002-location%20&%20uptake%20295-304.pdf

T. deWolff

Papers

Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya