The development of MISCANFOR, a new Miscanthus crop growth model: towards more robust yield predictions under different climatic and soil conditions

Abstract

Miscanthus  giganteus (Greef et Deu.), a perennial rhizomatous grass, native of SE Asia, has been trialed Europe-wide as a potential bio-energy crop. Plant growth models have been developed to match previously reported field experiments. These models have been used to extrapolate Miscanthus yields to other environments. Although the models use similar process descriptions, the parameters used to match the experimental data vary from site to site. This paper describes the development of universal process descriptions that use genotype-specific parameters to predict yields in a wide range of environments. Using these, we develop a new model, MISCANFOR, from an existing model MISCANMOD by improving process descriptions for light interception by the canopy and the impact of temperature and water stress on radiation use efficiency. Genotypespecific process descriptions for plant growth phase, photo-period sensitivity, thermal time, temperature dependant radiation-use efficiency, drought and frost kill predictions, nutrient repartition to the rhizome, and moisture content at harvest are added. Predictions made with MISCANFOR are compared with MISCANMOD for 36 experimental data sets for a wide variety of soils and climatic conditions in Europe. MISCANFOR matches field experiments with an r 2 5 0.84 compared with 0.64 for MISCANMOD, building confidence that the new model will be better able to predict Miscanthus yields for other areas and future IPCC climate scenarios. This model has identified photoperiod sensitivity in addition to drought resistance and frost tolerance as parameters for crop improvement to extend the range of climatic conditions under which this crop can be grown economically.

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

“…Yield mass is calculated according to meteorological and soil data (Hastings et al, 2009). Meteorological inputs to the model are mean temperature, temperature range, precipitation and cloud cover; soil inputs are field capacity and wilt point.…”

“…Potential evapotranspiration is calculated using the Thornthwaite equation (Thornthwaite, 1948), with a Penman adjustment factor (Hastings et al, 2009). Downregulation terms for evapotranspiration, radiation use efficiency and leaf area index are calculated according to available soil water using an Aslyng discontinuous linear process description (Aslyng, 1965;Hastings et al, 2009). The modelled crop is also subject to drought and frost kill (Hastings et al, 2009).…”

“…Meteorological inputs to the model are mean temperature, temperature range, precipitation and cloud cover; soil inputs are field capacity and wilt point. Radiation is calculated from the latitude and time of year by the method described in the SWAT Theoretical Documentation (Neitsch et al, 2002), including a cloud correction factor (Hastings et al, 2009). Potential evapotranspiration is calculated using the Thornthwaite equation (Thornthwaite, 1948), with a Penman adjustment factor (Hastings et al, 2009).…”

“…Radiation is calculated from the latitude and time of year by the method described in the SWAT Theoretical Documentation (Neitsch et al, 2002), including a cloud correction factor (Hastings et al, 2009). Potential evapotranspiration is calculated using the Thornthwaite equation (Thornthwaite, 1948), with a Penman adjustment factor (Hastings et al, 2009). Downregulation terms for evapotranspiration, radiation use efficiency and leaf area index are calculated according to available soil water using an Aslyng discontinuous linear process description (Aslyng, 1965;Hastings et al, 2009).…”

“…The use of datasets based on different forms of interpolation (Hijmans et al, 2005) or climate predictions (Southworth et al, 2000) makes it particularly important to understand the sensitivity of model outputs to the choice of data inputs due to the potential range of input values for ostensibly the same conditions. This study explores the effects of different meteorological and soil datasets on the crop growth model Miscanfor (Hastings et al, 2009) over a fixed time period and land area. The use of different datasets for the same reported environmental conditions should provide a guide to the possible range of model predictions for a given time and area range.…”