The majority of the large number of existing land use models lack a proper validation, often because of data problems. Moreover, despite recognition of the necessity to incorporate a multi-scale analysis, scale dependencies are normally not considered during validation. In this paper, a multi-scale land use change modelling framework, conversion of land use and its effects (CLUE), is calibrated for Costa Rica and validated at five spatial resolutions for Honduras and Costa Rica. Both countries experienced locally very strong actual land use changes. Calibration runs show that the model is very sensitive to changes in the autonomous development parameter, which defines the influence of the finest resolution. Validation results are very satisfactory for both countries. Especially, changes in major land use types are reproduced with the model. Changes in localised land use types are more difficult to project. The magnitude of gains and magnitude of losses are slightly underestimated in all cases. The multi-scale validation demonstrates that results improve strongly, and exponentially, with decreasing spatial resolution. Strong reduction of the number of observations results in a correlation between actual and modelled changes that approximates the perfect value of 1. The study demonstrates that the CLUE modelling framework can reproduce changes as they took place in Central America in the 1970s and 1980s, and shows how conclusions can differ depending on the scale at which validation is performed.

We examine the geographic dimensions of food consumption in Ecuador, which has one of the highest rates of chronic infant undernutrition in Latin America. We use statistical and spatial analyses to examine the distribution of * Corresponding Author.2 food consumption and food poverty and to test and generate hypotheses of food poverty estimates at the district level. Results show that the food poor are concentrated in certain locations with a significant cluster identified in the central Andean region. Geographically weighted regression shows that the processes underlying food poverty in Ecuador are also spatially variable. While our results lend support for nationwide land tenure reforms, in the central Andes these must take into account productivity constraints and communal ownership. Improvements in transport infrastructure will likely decrease levels of food poverty country-wide but could be most beneficial in the extreme south and in the province of Esmeraldas. Investment in rural enterprise development should be encouraged in all regions.

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SUMMARYThis paper seeks to establish the concept that the analysis of high temporal resolution meteorological data adds value to the investigation of the effect of climatic variability on the prevalence and severity of agricultural pests and diseases. Specifically we attempt to improve disease potential maps of root rots in common beans, based on a combination of inherent susceptibility and the risk of exposure to critical weather events. We achieve this using simulated datasets of daily rainfall to assess the probability of heavy rainfall events at particular times during the cropping season. We then validate these simulated events with observations from meteorological stations in East Africa. We also assess the utility of remotely sensed daily rainfall estimates in near real time for the purposes of updating the risks of these events over large areas and for providing warnings of potential disease outbreaks. We find that simulated rainfall data provide the means to assess risk over large areas, but there are too few datasets of observed rainfall to definitively validate the probabilities of heavy rainfall events generated using rainfall simulations such as those generated by MarkSim. We also find that selected satellite rainfall estimates are unable to predict observed rainfall events with any power, but data from a sufficiently dense network of rain gauges are not available in the region. Despite these problems we show that remotely sensed rainfall estimates may provide a more realistic assessment of rainfall over large areas where rainfall observations are not available, and alternative satellite estimates should be explored.

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