Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement.

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998-2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7-12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6-26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Taking account of uncertainties in digital land suitability assessment.

Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the 'error free' assessment, where a prediction of 'unsuitable' was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.