Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia

Quantifying the effects of varietal types × management on the spatial variability of sorghum biomass across US environments

“…4.2.2 | G×E×M drivers of simulated sorghum biomass Spatial gradients in global solar radiation, mean temperature, and rainfall, and their corresponding stress factors (ir, tp, and sw) influenced sorghum biomass under both irrigated and rainfed conditions (Figure 7; Figure S12). Similar multiscale environmental crop response characterizations have been previously reported for corn in the United States (Jin et al, 2017) and New Zealand (Teixeira et al, 2017), for sunflower (Helianthus annuus L.) in France (Casadebaig et al, 2020), and for wheat (Triticum aestivum L.; Chenu et al, 2013) and potatoes (Solanum tuberosum L.; Ojeda et al, 2021b;Ojeda et al, 2020) in Australia. However, in this manuscript we combined environmental clustering based on three crop stressors (ir, tp, and sw), correlation and variance partitioning analysis to assess the biomass sorghum variability at a large scale in potential growing environments of the United States.…”

“…The model's fitness-for-purpose for this paper was further supported by the spatial patterns of sorghum biomass in the potential production areas for energy sorghum in the United States that align with field experimental data across the country (Figure 5). Lack of specificity in model inputs is a crucial source of uncertainty in crop yield estimations (Fleisher et al, 2017;Ojeda et al, 2021aOjeda et al, , 2021bOjeda et al, , 2021c. Part of the prediction error found in this study (Figure 3) is explained by the fact that field experiments used for model validation were not specifically designed for modeling purposes, and therefore, there were gaps for model inputs such as a lack of measured soil data to initialize the model or crop management records.…”

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

“…In the case of ERA5, the fine details related to topography cannot be seen, but the spatial structure of temperature in all the three reference databases are similar: an increasing Northwest-Southeast gradient can be recognized though, with quite different mean values. Differences of the average solar radiation intensity, average daily mean temperature and precipitation sum for the vegetation period (April-October) for 1981-2010 based on the investigated databases were presented on maps according to Ojeda et al (2021) (Figure 2). The CARPATCLIM database was used as a benchmark for the comparisons.…”

“…Hoffmann et al (2015) and Zhao et al (2015) investigated the climate data aggregation effect, while Folberth et al (2016), Grosz et al (2017), Coucheney et al (2018) and Maharjan et al (2019) investigated the soil data aggregation effect on specific model outputs. Recently, Ojeda et al (2020Ojeda et al ( , 2021 assessed the combined data aggregation effect of climate and soil on APSIM model outputs. Tao et al (2018) presented the contribution of crop model structure and parameters to model output uncertainty in climate change impact assessments.…”

“…Due to the different data processing approaches and methodologies as well as the differences in the underlying network of observation stations that provides base data for the gridded database, the available soil and weather data sources provide different data for the very same crop model inputs. Obviously, differences in the applied input databases could be a significant source of uncertainty in crop model outputs (Ojeda et al 2020(Ojeda et al , 2021. The question is the relative significance of this uncertainty compared to others such as the uncertainty related to the spatial heterogeneity of soil or the interannual variability of weather.…”