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

Ojeda, Jonathan J.; Rezaei, Ehsan Eyshi; Remenyi, Tomas A.; Webb, Mathew; Webber, Heidi; Kamali, Bahareh; Harris, Rebecca M. B.; Brown, Jaclyn N.; Kidd, Darren; Mohammed, CL; Siebert, Stefan; Ewert, Frank; Meinke, Holger

doi:10.1016/j.scitotenv.2019.135589

Cited by 26 publications

(13 citation statements)

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“…Accordingly, the finer resolution soil data had to be upscaled. Following the recommendations of Ojeda et al (2020) a dominant soil type method is used for this purpose. For every 10×10 km grid cells one representative cell out of the 10.000 constituting 100×100 m subcells of the DOSoReMI grid has been selected.…”

Section: Soil Databasementioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Fodor

Pásztor

Szabó

et al. 2021

International Journal of Digital Earth

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Gridded model assessments require at least one climatic and one soil database for carrying out the simulations. There are several parallel soil and climate database development projects that provide sufficient, albeit considerably different, observation based input data for crop model based impact studies. The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases, respectively covering an area of nearly 100.000 km 2 with 1104 grid cells. Spatial, temporal, climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations. The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes. Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run. Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations.

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Section: Soil Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Fodor

Pásztor

Szabó

et al. 2021

International Journal of Digital Earth

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“…Potential yield, consequently, is usually well simulated using GWD for annual crops (Battisti et al, 2019;Van Wart et al, 2013a), however, it depends on the regional environment characteristics (Bai et al, 2010;Ojeda et al, 2020;Van Wart et al, 2013a). Monteiro et al (2018) showed that sugarcane potential yield predicted by a generic FAO-AEZ model when using NASA GWD (1º x 1º grid cell) presented low errors (RMSE < 30 t/ha) in the majority of Brazilian territory…”

Section: Discussionmentioning

confidence: 99%

“…Other issues such as topography (Ojeda et al, 2020;White et al, 2011) and aggregation of soil and climate data (Hoffmann et al, 2016;Ojeda et al, 2020) are also recognized as sources of uncertainties and should be evaluated before simulating sugarcane and other crops performances in a spatial scale in countries with continental dimensions, like Brazil.…”

Section: Discussionmentioning

confidence: 99%

Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil

Dias¹

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Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil Brazil is the largest sugarcane producer in the world and the production systems across the country are very complex with large spatial and temporal variations in yields. This is a result of large Genotype × Environment × Management (G × E × M) interactions that ultimately affect crop performance. Moreover, likely changes in the climate conditions are expected in the future imposing additional challenges to sugarcane production. Process-based crop modelling is scientifically accepted as a way to understand those interactions. Upon that, this thesis aimed to integrate field experiments datasets with the APSIM-Sugar model for better understanding the G × E × M interactions in Brazilian cropping systems. The capability of the model was first evaluated with default settings and then modifications and traits were proposed to effectively predict G (varieties) differences. With APSIM-Sugar upgraded, an up-to-date projection of future climate impacts on sugarcane yields across Brazil was performed. The sugarcane dataset came from large field experiments carried out between 2012 and 2017 at two tropical sites in Brazil: Guadalupe, in the state of Piauí (PI), and São Romão, in the state of Minas Gerais (MG), where several varieties were cultivated under non-limiting (potential) conditions. These data were analysed and employed to characterise variety traits for use in APSIM-Sugar. Substantial changes were required to enable the model to simulate canopy development and stalk yield appropriately. The outcomes from the modelling studies showed that the model is now able, although yet empirically, to account for ageing processes, known as the reduced growth phenomenon, to get better yield predictions in high yielding environments, as well as the ability to distinguish between varieties when it comes to yield gains above ~ 150 t/ha (~ 40 t/ha in dry mass). The findings suggest it is reasonable to hypothesise that the APSIM-Sugar modifications and traits are plausible and are an important step for unravelling G × E × M interactions to improve the performance of the sugarcane industry. Another step of this thesis, before applying APSIM-Sugar, was to evaluate gridded weather datasets (NASA/POWER and other developed for Brazil referred to 'XAVIER') as alternative climate inputs in the model. The results indicated these data sources should be employed with caution when the purpose is to simulate sugarcane yield because of the poor performance of gridded rainfall, at least for Center-South Brazil. Lastly, a climate change impact assessment was performed with APSIM-Sugar considering new features and traits for 10 Brazilian sites, being six under rainfed and four under fully-irrigated conditions. Five global climate models were selected to represent basic classes of climate changes: relatively cool/wet; cool/dry; middle; hot/wet; and hot/dry, for four future scenarios. These c...

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Potato Modeling

Ahmed¹,

Fatima²,

Iqbal³

et al. 2020

Systems Modeling

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

Cited by 26 publications

References 89 publications

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

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

Sugarcane variety trait modelling: evaluating and improving the APSIM-Sugar model for simulating crop performance under current and future climates across Brazil

Potato Modeling

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