There is a knowledge gap in agriculture. For instance, there is no way to tell with precision what is the outcome of cutting N fertilizer by a quarter on important outcomes such as yield, net return, greenhouse gas emissions or groundwater pollution. Traditionally, the way to generate knowledge in agriculture has been to conduct research with the experimental method where experiments are conducted in a controlled environment with trials replicated in space and time. While this method has proven its potential to generate knowledge, it has also shown limitations in terms of speed and amount of resources required. Indeed, at the current pace of agricultural impacts on the biosphere, it is likely that traditional experimental research won't be able to generate the knowledge required in a timely fashion. A paradigm shift is needed to shorten the time between the detection of a problem and the access to a reliable solution. One possible avenue is to use another scientific approach, notably the observational method, which relies on a large number of observations to draw conclusions. The advent of communication and information technologies in agriculture opens new possibilities, notably to conduct observational research with big datasets. By observing farm inputs and outputs contextualized with soil, climate, and weather data, there is a tremendous potential to improve farm input use efficiency by adjusting prescriptions to each and every location in every field of every farm. It is most likely that to keep up with the rapid pace of agricultural impacts on the environment, observational science needs to be implemented at a global scale.