By Bert Rijk
Agriculture is worldwide a large consumer of freshwater, nutrients and land. Resources that are all getting scarcer in the near future, therefore agriculture needs to increase its efficiency by producing more outputs with less inputs. This can be achieved through precision agriculture, a combination of technique and ICT to aid agricultural efficiency. However, precision agriculture is currently fractured over island of data with their own data type, being soil, water, plant, nutrients or climate for example. Therefore, this thesis analyses the potential for integration of these (sensor)data into a crop model in order to aid in farm decision support. We managed to calibrate and validate the LINTUL-3 crop model for potatoes on two experimental fields in the south of the Netherlands. Potato crop data from a fertilization experiment executed in 2010 was used for calibration and the LINTUL model was validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with plot measured yield. Furthermore, we analysed the potential of assimilation of Leaf Area Index (LAI) into the LINTUL-3 model through the ‘updating’ assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor techniques shows promising results for precision agriculture application and thereby for reduction of the footprint agriculture has on the world’s resources.
Keywords: Crop model, sensor data, remote sensing, close sensing, data assimilation, integration, decision support