Modelling crop growth and yield under the environmental changes induced by windbreaks 1. Model development and validation

Abstract

Yield advantages of crops grown behind windbreaks have often been reported, but underlying principles responsible for such changes and their long-term consequences on crop productivity and hence farm income have rarely been quantified. Physiologically and physically sound simulation models could help to achieve this quantification. Hence, the APSIM systems model, which is based on physiological principles such as transpiration efficiency and radiation use efficiency (termed here APSIMTE), and the Soil Canopy Atmosphere Model (SCAM), which is based on the Penman–Monteith equation but includes a full surface energy balance, were employed in developing an approach to quantify such windbreak effects. This resulted in a modified APSIM version (APSIMEO), containing the original Penman equation and a calibration factor to account for crop- and site-specific differences, which were tested against field data and simulations from both the standard APSIMTE and SCAM models. The APSIMEO approach was tested against field data for wheat and mungbean grown in artificial enclosures in south-east Queensland and in south-east Western Australia. For these sheltered conditions, daily transpiration demand estimates from APSIMEO compared closely to SCAM. As the APSIMEO approach needed to be calibrated for individual crops and environments, average transpiration demand for open field conditions predicted by APSIMEO for a given site was adjusted to equal that obtained using APSIMTE by modifying a calibration parameter β. For wheat, a β-value of 1.0 resulted in best fits for Queensland, while for Western Australia a value of 0.85 was necessary. For mungbean a value of 0.92 resulted in the best fit (Qld). Biomass and yields simulated by APSIMTE and the calibration APSIMEO for wheat and mungbean grown in artificial enclosures were generally distributed around the 1:1 line, with R2 values ranging from 0.92 to 0.97. Finally, APSIMEO was run at 2 sites using long-term climate data to assess the likely year-to-year variability of windbreak effects on crop yields. Assuming a 70% reduction in wind speed as representing the maximum potential windbreak effect, the average yield improvement for the Queensland site was 13% for wheat and 3% for mungbean. For wheat at the WA site the average yield improvement from reduced wind speed was 5%. In any year, however, effects varied from negative, neutral to positive, highlighting the highly variable nature of the expression of windbreak effects. This study has shown how physical and biological modelling approaches can be combined to aid our understanding of systems processes. Both the environmental physics perspective and the biological perspective have shortcomings when issues that sit at the interface of both approaches need to be addressed. While the physical approach has clear advantages when investigating changes in physical parameters such as wind speed, vapour pressure deficit (VPD), temperature or the energy balance of the soil–plant–atmosphere continuum, it cannot deal with complex, biological systems adequately. Conversely, the crop physiological approach can handle such biological interactions in a scientific and robust way while certain atmospheric processes are not considered. The challenge was not to try and capture all these effects in 1 model, but rather to structure a modelling approach in a way that allowed for inclusion of such processes where necessary.

Impact of shelter on crop microclimates: a synthesis of results from wind tunnel and field experiments

“…Three windbreaks of different porosities (β) were created using different density meshes. [The general symbol for porosity is β: β O for optical porosity and β A for porosity determined using wind speeds measured upwind and in the bleed flow; β is used throughout this paper to refer to the porosity of the model windbreaks used in the wind tunnel and should not be confused with the same symbol used in Meinke et al (2002) which is a calibration factor. ]…”

The Australian National Windbreaks Program: overview and summary of results

“…The APSIM model was adapted to predict the effects of wind shelter on crop yields. Validation of this modelling approach was limited to the measured results from the artificial shelter sites as none of the field studies found an unequivocal yield response to shelter (Meinke et al 2002;Carberry et al 2001). Hence, there is some uncertainty in the model's validity.…”

“…Crop growth simulations using APSIM. The philosophy and architecture of APSIM (Agricultural Production Systems Simulator) are detailed in Meinke et al (2002). APSIM was used to predict crop growth and final yields for the field sites (Esperance barley, Roseworthy wheat, Atherton maize) and artificial shelters (Esperance wheat, Rutherglen wheat and Warwick wheat and mung bean).…”

Modelling crop growth and yield under the environmental changes induced by windbreaks. 2. Simulation of potential benefits at selected sites in Australia

“…APSIM setup APSIM (version 1.54), using crop modules for maize, mungbean and wheat, was used to predict the growth, development and yield of maize, mungbean and wheat crops grown at the sites nominated in Table 1. Meinke et al (2002) described in detail how APSIM was specified to simulate crop performance behind windbreaks. Briefly, the following assumptions were employed: (i) As APSIM crop modules have been developed and tested around Australia using a transpiration efficiency (TE) approach to determine daily transpiration demand (Carberry et al 1996;Meinke et al 1998), crop yields simulated at each site using this approach (APSIM TE ) were regarded as 'baseline yields' for the purposes of this analysis.…”

“…In the first paper in this series, Meinke et al (2002) described how the cropping systems model, APSIM (Agricultural Production Systems Simulator) (McCown et al 1996), was specified to simulate crop growth under the environmental changes induced by windbreaks. APSIM simulation of potential evapotranspiration and crop growth under altered wind speed conditions was respectively tested against predictions from the SCAM micrometeorological model (Raupach et al 1997) and limited crop data from artificial shelter experiments .…”