Journal: PLOS One

Article Title: AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

doi: 10.1371/journal.pone.0337705

Figure Lengend Snippet: AquaCropPlotter is designed to facilitate processing, visualization and analysis of outputs from AquaCrop simulations. The workflow consists of four main steps: upload data, combine data, plot and analysis.

Article Snippet: Example AquaCrop output data that can be used with AquaCropPlotter are available at Mendeley Data (DOI: 10.17632/ynz4zt54r4.1 ).

Techniques:

Journal: PLOS One

Article Title: AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

doi: 10.1371/journal.pone.0337705

Figure Lengend Snippet: The user interface for uploading AquaCrop output files, with examples of (A) Standard “GUI” or (B) Stand-alone “Plug-in” output data uploaded. Users can select files to upload in batch and the files will be automatically processed and displayed in the list.

Article Snippet: Example AquaCrop output data that can be used with AquaCropPlotter are available at Mendeley Data (DOI: 10.17632/ynz4zt54r4.1 ).

Techniques:

Journal: PLOS One

Article Title: AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

doi: 10.1371/journal.pone.0337705

Figure Lengend Snippet: (A) Crop cycle lengths (days) predicted from AquaCrop simulations of maize grown at three locations in the Republic of Moldova (Soroca, Chisinau, and Cahul) over the course of the century under two climate change scenarios (RCPs 2.6 and 8.5). Different sowing dates (shown at the top of each column) were evaluated to assess the effect of these adaptation solutions. Data are shown as scatter plots with LOESS smooth lines to show the trend. Values shown represent averages from simulations using three global climate models. (B) Outputs from linear regression analysis of crop cycle length over time, performed on each group of the dataset as described above.

Article Snippet: Example AquaCrop output data that can be used with AquaCropPlotter are available at Mendeley Data (DOI: 10.17632/ynz4zt54r4.1 ).

Techniques:

Journal: PLOS One

Article Title: AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

doi: 10.1371/journal.pone.0337705

Figure Lengend Snippet: (A) Exposure to temperature stress affecting stomatal transpiration (stomatal stress) expressed as the percentage of days crops are exposed to the stress (% days) predicted from AquaCrop simulations of maize grown at three locations in the Republic of Moldova (Soroca, Chisinau, and Cahul) over the course of the century under two climate change scenarios (RCPs 2.6 and 8.5). Different sowing dates (shown at the top of each column) were evaluated to assess the effect of these adaptation solutions. Data are shown as scatter plots with LOESS smooth lines to show the trend. Values shown represent averages from simulations using three global climate models. (B) Outputs from linear regression analysis of stomatal stress over time, performed on each group of the dataset as described above.

Article Snippet: Example AquaCrop output data that can be used with AquaCropPlotter are available at Mendeley Data (DOI: 10.17632/ynz4zt54r4.1 ).

Techniques:

Journal: PLOS One

Article Title: AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

doi: 10.1371/journal.pone.0337705

Figure Lengend Snippet: (A) Evapotranspiration water productivity (WpET, kg yield produced per m 3 water evapotranspired) predicted from AquaCrop simulations of maize grown at three locations in the Republic of Moldova (Soroca, Chisinau, and Cahul) over the course of the century under two climate change scenarios (RCPs 2.6 and 8.5). Different sowing dates (shown at the top of each column) were evaluated to assess the effect of these adaptation solutions. Data are shown as scatter plots with LOESS smooth lines to show the trend. Values shown represent averages from simulations using three global climate models. (B) Outputs from time window summary analysis of water productivity (WpET). The mean and standard deviation (SD) are calculated from 20 years time windows, performed on each group of the dataset as described above.

Article Snippet: Example AquaCrop output data that can be used with AquaCropPlotter are available at Mendeley Data (DOI: 10.17632/ynz4zt54r4.1 ).

Techniques: Produced, Standard Deviation