Application and Evaluation of a Simple Crop Modelling Framework: A Case Study for Spring Barley, Winter Wheat and Winter Oilseed Rape over Ireland

Globally, croplands represent a significant contributor to climate change, through both greenhouse gas emissions and land use changes associated with cropland expansion. They also represent locations with significant potential to contribute to mitigating climate change through alternative land use management practices that lead to increased soil carbon sequestration. In spite of their global importance, there is a relative paucity of tools available to support field- or farm-level crop land decision making that could inform more effective climate mitigation practices. In recognition of this shortcoming, the Simple Algorithm for Yield Estimate (SAFY) model was developed to estimate crop growth, biomass, and yield at a range of scales from field to region. While the original SAFY model was developed and evaluated for winter wheat in Morocco, a key advantage to utilizing SAFY is that it presents a modular architecture which can be readily adapted. This has led to numerous modifications and alterations of specific modules which enable the model to be refined for new crops and locations. Here, we adapted the SAFY model for use with spring barley, winter wheat and winter oilseed rape at selected sites in Ireland. These crops were chosen as they represent the dominant crop types grown in Ireland. We modified the soil-water balance and carbon modules in SAFY to simulate components of water and carbon budgets in addition to crop growth and production. Results from the modified model were evaluated against available in situ data collected from previous studies. Spring barley biomass was estimated with high accuracy (R-2 = 0.97, RMSE = 95.8 g center dot m(-2), RRMSE = 11.7%) in comparison to GAI (R-2 = 0.73, RMSE = 0.44 m(2)center dot m(-2), RRMSE = 10.6%), across the three years for which the in situ data was available (2011-2013). The winter wheat module was evaluated against measured biomass and yield data obtained for the period 2013-2015 and from three sites located across Ireland. While the model was found to be capable of simulating winter wheat biomass (R-2 = 0.71, RMSE = 1.81 t center dot ha(-1), RRMSE = 8.0%), the model was found to be less capable of reproducing the associated yields (R-2 = 0.09, RMSE = 2.3 t center dot ha(-1), RRMSE = 18.6%). In spite of the low R-2 obtained for yield, the simulated crop growth stage 61 (GS61) closely matched those observed in field data. Finally, winter oilseed rape (WOSR) was evaluated against a single growing season for which in situ data was available. WOSR biomass was also simulated with high accuracy (R-2 = 0.99 and RMSE = 0.52 t center dot ha(-1)) in comparison to GAI (R-2 = 0.3 and RMSE = 0.98 m(2)center dot m(-2)). In terms of the carbon fluxes, the model was found to be capable of estimating heterotrophic respiration (R-2 = 0.52 and RMSE = 0.28 g center dot C center dot m(-2)center dot day(-1)), but less so the ecosystem respiration (R-2 = 0.18 and RMSE = 1.01 g center dot C center dot m(-2)center dot day(-1)). Overall, the results indicate that the modified model can simulate GAI and biomass, for the chosen crops for which data were available, and yield, for winter wheat. However, the simulations of the carbon budgets and water budgets need to be further evaluated-a key limitation here was the lack of available in situ data. Another challenge is how to address the issue of parameter specification; in spite of the fact that the model has only six variable crop-related parameters, these need to be calibrated prior to application (e.g., date of emergence, effective light use efficiency etc.). While existing published values can be readily employed in the model, the availability of regionally derived values would likely lead to model improvements. This limitation could be overcome through the integration of available remote sensing data using a data assimilation procedure within the model to update the initial parameter values and adjust model estimates during the simulation.