Crop models and their associated errors

In any climate change research there is a certain aspect of uncertainty associated with the climate models used, this uncertainty is often much larger than the uncertainty associated with the crop model used (Mereu et al., 2015). Having said that Lobell suggests that ignoring errors, when adopting a crop model, can distort crop sensitivity to rainfall by a factor of 2 or more.

Watson and Challinor (2012) undertook a research paper in which they manipulate the source of error in a General Large Area Model (GLAM) to measure the influence of RMSE of Groundnut yield. The comparison of error associated with climate data (including mean temperature and rainfall initially inputted into the model) with crop yield inputs for calibration purposes to establish which created the largest Root Mean Square Error. Rainfall posed to be the have the most significant influence on the skill of the model. This is quite expected as rainfall is often the limiting factor within semi-arid regions. In particular misrepresentation of inter seasonal variation of rainfall (and temperature) creates more error than that generates through systematic bias of climate simulations. However due to the nature of climate it is important to remember the generalisation of these results from the study site, Gujarat India needs high consideration.

RMSE was increased by 143% upon the manipulation of yield data, because of this the paper concludes both potential and actual crop yield measurements should be incorporated as decreasing this gap between potential and actual yields are ever the focus of agriculture practices. Without this observed data (used within the calibration process) it is impossible to estimate the total error associated with simulations produced by the model and have confidence in results.

Figure 1: demonstrates the RMSE of each of the parameters under scenario A in the study. The ranges shows the greatest error is associated with incorrect precipitation data.

Therefore it is essential that these errors are incorporated as the sensitivity of crops to climatic factors can have implications for the overall assessment of climate change on food security and consequently the adaption strategies implemented (Challinor 2009).