One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields.

Economic development and the successful transformation ofagriculture have been at the core of impressive change in countriessuch as China, India, Indonesia, Brazil, Mexico, and Argentina. This transformation has relied on substantial and effective investment inagriculture, and, in particular, building capacity in all aspects of agricultural change – from technology development and transfer through infrastructural development and the processing of agricultural commodities into consumer products.

Georeferenced data are a key factor in many decision-making systems. However, their interpretation is user and context dependent so that, for each situation, data analysts have to interpret them, a time-consuming task. One approach to alleviate this task, is the use of semantic annotations to store the produced information. Annotating data is however hard to perform and prone to errors, especially when executed manually. This difficulty increases with the amount of data to annotate.