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[Display omitted] • We determine and quantify the main drivers of potato yield variability under irrigated conditions. • We used correlation analysis, decomposition of variability sources and maps to assess potato yield variability. • Using APSIM, three planting dates × four irrigation strategies were simulated. • Crop management factors explained the greatest potato yield variability when compared to environmental factors. • We identified a need to discriminate the drivers of potato yield variability regionally. The uncertainties associated with crop model inputs can affect the spatio-temporal variance of simulated yields, particularly under suboptimal irrigation. The aim of this study was to determine and quantify the main drivers of irrigated potato yield variance; as influenced by crop management practices as well as climate and soil factors. Using a locally calibrated crop model (APSIM), three planting dates × three irrigation strategies (plus a non-water limited treatment) were simulated using 30 years of historical weather data across potato production areas in Tasmania, Australia. We used (i) correlation analysis, (ii) variance decomposition and (iii) maps to visualise the spatial decomposition of variance of potato yield. Our results showed that the implementation of potential irrigation compensated for the impact of planting date on climate drivers of simulated yield and changed the most important yield driving factors from irrigation and planting date to global solar radiation (r = 0.69−0.81). Under early-planting, we found positive correlations of simulated yield vs. global solar radiation (r up to 0.75 under high and medium irrigation) and between the simulated yield and rainfall (r up to 0.55 under low irrigation). In general, a mix of negative and positive correlations were found for minimum and maximum temperature depending on soil type. Using variance decomposition analysis, we found that crop management factors explained the greatest yield variance depending on soil type related to plant available water capacity (PAWC). For soils with high PAWC (>200 mm), most variance was explained by global solar radiation (56–62 %) followed by planting date (43–47 %). However, when PAWC values decreased from 242 mm to 94 mm, the contribution of global solar radiation and planting date were reduced from 62 % to 5.8 % and from 47 % to 4.4 %, respectively, and the contribution of irrigation strategy increased from 0.4%–32.5%. We identified a need to quantify and differentiate the variance contribution of environmental and crop management factors on crop yield and within these factors, discriminate the key drivers of yield variance at regional scale. [ABSTRACT FROM AUTHOR]

[Display omitted] • We validate APSIM-Potato across several soil-climate-management combinations. • We evaluated the difference of yield and irrigation using input/output aggregation. • PAWC and source of water were the main drivers of differences due to aggregation. • Maximum differences were found for rainfed yield and irrigation requirement. • We highlight the need to quantify biases on model outputs due to data aggregation. Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (D Y and D I W R for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha−1) and IWR (137 mm). D Y variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions. [ABSTRACT FROM AUTHOR]

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO 2 concentrations (e[CO 2 ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO 2 Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO 2 ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO 2 ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO 2 ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour., (© 2020 John Wiley & Sons Ltd.)

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 (DAE c ) and soil (DAE s ) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAE c and DAE s 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)