Editorial

This number delivers articles with several perspectives, including the consideration of the production, taking into account the consumers' demands, the use of modelling tools to predict future events and the use of technologies to intensify production.

Likewise, the production and quality of raw cane sugar when introducing more efficient technolog y in the use of energy, but which degrade the quality and consequently the competitiveness of the raw cane sugar producers if implemented using merely environmental criteria. In the same line, the use of fertigation for forage production during dry seasons includes an economic and environmental evaluation of the practice. It is clear that focusing on primary production is not the only criterion nowadays; aspects such as environmental, social and economic sustainability of the operation must be considered. The use of mathematic modelling systems that comprehend environmental, social and economic aspects is essential to improve our understanding of the interrelations between society, environment and production, resulting from productive intensification. The development of these analysis tools clearly means a challenge to accelerate the development of a holistic approach to farming activities, coming closer to the everyday reality of the producers; this must aim at tuning up our research and rural development efforts and improving the life quality of the stakeholders.

In this occasion, I will particularly address the need to include the use of modern tools such as sensors and models of process, cultivation and productive systems simulation in our farming experimentation. These tools, combined with expertise, must provide information which -collected and presented to the final user as a decision making system- will have impact on the stakeholders and result in the reduction of the possible negative consequences or the capitalization of the positive aspect that such event could generate.

There is a wide range of cultivation simulation models whose results are not always close enough to the actual performance of the cultivation in different environments present in a rough topography or which simply have not undergone the due adjustment and validation for many crops, environments and productive systems of Colombian agriculture. Presently, there is an increasing trend worldwide to use farming predictive tools using a mathematic modelling of crops and livestock production, and remote sensors; these have been successfully applied in many industrial crops. Although every model has strengths and targets, there are two main objectives that could be the focus for the development and optimization of the models: the first one is to reduce the level of uncertainty on the performance of certain crops in conditions in which they have not been tested before; the second one, once the crop has been established, to determine how its performance would be considering the future scenarios foreseen by the climatic change models. In the first objective, the amount of information required to successfully model the performance of a crop needs long-term sustained efforts; in addition, multi-disciplinary teams should be established, with a great component of physiological experimentation and greater understanding of the processes that take place in the soil. The level of predictability would be refined much more every time (more efforts and more resources) whenever the performance evaluation of particular varieties is required. The second objective depends considerably on the refining of the predictions on future changes in specific regions. However, the local climatic variability is that experienced by plants and animals day by day and represents the stress level that affects their performance.

The growing complexity of the models intending to simulate cross-cutting processes represents a much bigger challenge; yet, its development is essential to increase our understanding of the interrelations that take place in productive systems. Farmers, especially small ones, depend on the productive system (multi-species and in many cases multi-strata) and very seldom on the production of monocrops. The optimization of these is of main importance to improve the life quality of the producers. Today, the tools developed in computer analysis, data storage, use of remote sensors which facilitate the catchment of images related to the performance of plants, must be combined to tune up the integrated mathematic models and improve their level of prediction of the functioning of the system.