Fig 1.
Location of the case study, the Pas, Miera and Ason in watersheds in northern Spain.
Reprinted from [67] under a CC BY license, with permission from ISPRS Journal of Photogrammetry and Remote Sensing, original copyright 2014.
Fig 2.
Distribution of livestock and pastureland types in the case study.
Reprinted from [69] under a CC BY license, with permission from GDAM (Global Administrative Areas), original copyright 2018–2021.
Table 1.
Comparison between R and k.IM language for the “Potential above ground biomass caused by growth” model.
Fig 3.
Dataflow of PaL namespaces related to climatic growth limitations, vegetation life’s cycle, livestock grazing and nitrogen cycle.
Table 2.
Description of PaL namespaces related to climatic growth limitations, vegetation life cycle, livestock grazing and nitrogen cycle.
Fig 4.
Dataflows of “Solar Radiation over Vegetation (rg2)” and “Solar Radiation limiting factor causing Vegetation Growth (fr)”.
The figure shows the interaction between them and the models that act as inputs. A total of six models are involved in this process.
Fig 5.
Dataflow of “Nitrogen in living above ground biomass caused by cattle solid manure” model (excretion namespace).
“Nitrogen in living above ground biomass caused by cattle solid manure” is composed of three models related to namespaces of livestock mass, nitrogen and ingestion.
Fig 6.
Modelled results of vegetation limiting factors for May 2018.
All outputs range from 0 (no vegetation growth) to 1 (maximum vegetation growth). (A) Atmospheric temperature limitation. (B) Soil moisture limitation. (C) Soil nitrogen limitation. (D) Solar radiation incidence limitation. Reprinted from [67] under a CC BY license, with permission from ISPRS Journal of Photogrammetry and Remote Sensing, original copyright 2014.
Table 3.
Examples of parameters and values in the Puerto model.
Fig 7.
Modelled results of vegetation growth.
(A) Potential Growth causing AboveGround Biomass model in grams/day and (B) Growth causing AboveGround Biomass model in grams/day (Cantabrian case study, May 2018). Reprinted from [67] under a CC BY license, with permission from ISPRS Journal of Photogrammetry and Remote Sensing, original copyright 2014.
Fig 8.
Puerto can display results as images produced in R software (in Spanish language by default); this example shows the 5-yearly mean of scrubland vegetation growth (blue line) and livestock vegetation ingested (white bar) by month and type of vegetation. General title: Monthly growth (bar) and ingestion (line) of scrubland vegetation (5-year mean). Graph title: Abbreviated scientific name of scrub species. A: Erica Vagans, B: Calluna vulgaris, C: Euphorbia Polygalifolia, D: Genista legionensis, E: Genista occidentalis, F: Juniperus alpina, G: Rubus ulmifolius, H: Ulex gallii, I: Vaccinium myrtillus. X-axis: Grams of solid material by square meters and month. Y-axis: Month from January to December. White bar: Scrubland vegetation growth. Vertical line in each bar indicates the range between maximum and minimum scrubland vegetation growth during the 5 year by month. Blue line: Livestock vegetation ingested.
Fig 9.
Modelled results of livestock mass variation.
(A) Cattle and (B) Mares in kg/day. Reprinted from [67] under a CC BY license, with permission from ISPRS Journal of Photogrammetry and Remote Sensing, original copyright 2014.
Fig 10.
Weight variation of 14 livestock groups in different plots during one year.
Fig 11.
Nitrogen leaching output model in grams of nitrogen mass.
Reprinted from [67] under a CC BY license, with permission from ISPRS Journal of Photogrammetry and Remote Sensing, original copyright 2014.
Fig 12.
Output of the model “Potential above ground biomass caused by growth” and its model dependencies.
Reprinted from OpenLayers (https://openlayers.org) under a 2-Clause BSD, with permission from OpenLayers, original copyright 2005 to present.
Fig 13.
Information related to each model output or dependency.
Fig 14.
Dataflow of “Above ground biomass caused by growth” model created on the fly as the model runs.
(A) and (B) show dataflow in detail and its boxes.
Fig 15.
Example of additional dataflow component views.
The individual dataflow components are shown by clicking on the boxes. (A) Information related to resources (i.e., raster data, vectorial), (B) information related to a look-up table where each ontology is linked to the identifier of the resource, (C) information related to a model equation. In this case, it is a conditional expression that indicates: when main vegetation (com) is equal to secondary vegetation (com2), then the result is the solar radiation (rg), otherwise, the result is rg multiplied by 0.2.
Fig 16.
Report describing the model, created on the fly as the model runs.