While the processing service framework, mechanisms for communicating with models, and em- ulation tools have all been tested with a small number of models during development, testing has considered contrived examples, which may not accurately represent models found in the real world. To enable more realistic usability testing, the framework and tools were used to deploy a case study workflow as part of the UncertWeb project.
The FERA case study aims to explore the effect of climate change on crop yields, and gather an impression of the future agricultural landscape (Johnson et al., 2010). Two English regions are considered in this case study — the East Anglian Chalk National Character Area (NCA) in the south east, and the Yorkshire Wolds NCA in the north east, selected by FERA as they are two complementary and well-studied agricultural regions. The workflow will predict current and future crop yields under uncertain climate scenarios, which can help to inform policy making regarding land management practices, farming behaviours and food availability.
5.2.1 Models
There are three main model components in the FERA case study. These models will be orches- trated in a workflow, described in Section 5.2.2, to help discover whether climate change might
have an effect on crop yields in England. The models are:
• a land capability classification model (generating crop transition probabilities); • a field use simulator (simulating yearly cropping patterns);
• a crop simulation model (producing estimates of crop yield).
The first model, a land capability classifier, has been developed by Jill Johnson at FERA. This model, known as the Land Capability Classification System (LCCS), accepts a set of field texture observations which describe the type of soil in each field of interest, for example ‘clayey’ or ‘loamy’. Field texture observations are accompanied by historical crop observations, which specify the crops grown in each field for a given set of years. LCCS adopts a Markov process approach to generate an uncertain crop transition matrix for each field texture, which contains a set of probabilities — each representing the chance of a field containing one crop type transitioning to another crop type in the following year.
For the second model, a field use simulator, the LandSFACTS model1 developed by the Macaulay Land Use Research Institute was selected. LandSFACTS uses a combination of stochas- tic and rule-based processes to create scenarios of crop or land use within fields and regions. A user must specify a set of probabilities representing crop or land use changes, and can also provide a number of spatial and temporal constraints. LandSFACTS will run field or region level simula- tions for a given number of years, and generate outputs containing the simulated crop or land use for each field or region, for each simulated year.
AquaCrop2, was selected for the final yield calculation model. The model, developed by the Food and Agriculture Organization of the United Nations (FAO), simulates crop development based on several parameters, and calculates crop yields based on these development simulations. A user can specify crop characteristics, soil properties, and climate data, including temperature, rainfall and evapotranspiration. AquaCrop requires a different set of characteristic parameters for each crop type. Acquiring such parameters is a time consuming process, and involves consultation with experts. As a result of these challenges, yield estimates in this workflow will only be produced for fields simulated to contain wheat. Crop characteristics for wheat were gathered by FERA, including ranges where parameters were uncertain.
AquaCrop LandSFACTS Land Capability Classification System Field simulation contains wheat? Yes Yield not calculated No Historical field use data Field area observations Soil properties Field texture observations Climate data Crop characteristics Transition matrices Simulated crop allocations Wheat yield estimates
5.2.2 Workflow
The model components are composed in a workflow, shown in Figure 5.1. Once appropriate data sources are added, the workflow can help to answer the question of whether climate change might effect the production of wheat in England.
Workflow inputs consist of historical field use data, field texture (soils) and area observations, crop characteristics, climate data, and soil properties. The majority of this data must be retrieved by the workflow orchestrator, but some, for example the crop characteristics for wheat, have al- ready been gathered by FERA and thus will remain fixed. Workflow outputs consist of predicted wheat yields for each year of the simulated field use, for each field simulated to contain wheat.
The workflow commences with historical field use data and field texture observations being sent to LCCS. The resulting output, uncertain transition matrices for each texture, form part of the input to the next model, LandSFACTS. However, these transition matrices contain Dirichlet distributions, and LandSFACTS only accepts transition matrices with fixed probabilities. There- fore, Monte Carlo must be performed by sampling from the Dirichlet distributions and executing LandSFACTS for each sample. This also introduces an extra workflow input, the number of sam- ples to draw from the uncertain transition matrices.
LandSFACTS is passed a transition matrix sample, and field texture and area observations. By default, LandSFACTS runs for a period of five years, and runs three simulations. At this stage in the workflow, even though LandSFACTS runs for five years, there is no specific date attached to these years. From the resulting simulated field uses, the workflow orchestrator must select those which contain wheat to send to AquaCrop.
AquaCrop is run for each field, for each year where it has been simulated to contain wheat. Climate and soil properties are selected based on the location of the field. The climate data deter- mines the specific date of the simulation, with the earliest data being assumed to be from the same year as the first in the LandSFACTS simulation. Climate data for both current and future scenarios were generated by the UKCP09 weather simulator. This data is uncertain, and therefore must be analysed accordingly. Uncertainty on the weather data is represented as realisations, allowing us to run AquaCrop using several of these realisations. A combination of the uncertainty introduced by the transition matrices, LandSFACTS, and the climate data will require AquaCrop to be run thousands of times — indicating that the model may be a possible candidate for emulation.
Finally, the workflow orchestrator must combine the output results from multiple simulations.
1http://www.macaulay.ac.uk/LandSFACTS/ 2http://www.fao.org/nr/water/aquacrop.html
For each field, for each year, if the field was simulated to contain wheat, several yield realisations will exist. Executing the workflow with both current and future climate data will allow the dif- ferences in wheat yields to be compared, accounting for uncertainty in the generated transition matrices, field use simulator, and climate data. Once the workflow has completed, it would be possible to spatially aggregate the field level results on a regional basis, allowing FERA to analyse and compare overall results for the East Anglian Chalk NCA and Yorkshire Wolds NCA.