Putting mechanisms into crop production models

Abstract

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

Modelling the response of yields and tissue C : N to changes in atmospheric CO<sub>2</sub> and N management in the main wheat regions of western Europe

“…4), although the overall agreement across treatments was acceptable, keeping in mind the assumptions that had to be made when deriving the observation-based values. Likewise, growing-season green leaf [N], which together with LAI is essential for predicting photosynthesis (Boote et al, 2013), was also well reproduced (results not shown). By contrast, [C] as well as [N] in the dead leaf pool were either over-or underestimated (see Fig.…”

“…A mechanistic representation of the CO 2 response (as well as other processes) has been argued to be critical when modelling crop responses to climate change (Yin, 2013), as recently shown for state-of-the-art crop models (Boote et al, 2013). In contrast to crop models, which are optimised to simulate yields, DVMs are tools for exploring and predicting the coupled dynamics of ecosystem functioning, climate-carbon cycle interactions and biome distributions (Friedlingstein et al, 2013;McGuire et al, 2001;Prentice et al, 2007).…”

“…For the simulation of crop productivity, traditional crop models typically rely on empirical scaling factors to modify the radiation-use efficiency based on measurements in CO 2 fertilisation experiments (Boote et al, 2013). A mechanistic representation of the CO 2 response (as well as other processes) has been argued to be critical when modelling crop responses to climate change (Yin, 2013), as recently shown for state-of-the-art crop models (Boote et al, 2013).…”

“…Kelley et al, 2013;Rounsevell et al, 2014). For these types of questions, a chief challenge remains in representing phenology and growth responses to fertiliser application and climate in a way that is suitable for large-scale models, but still reproduces realistic results (Boote et al, 2013;Lokupitiya et al, 2009). In LPJ-GUESS, crop phenological events, and especially the growth of the leaves throughout the growing-season were captured (Fig.…”

“…FACE experiments (Boote et al, 2013;Rosenzweig et al, 2014). Adapting a more mechanistic representation of the CO 2 response by replacing a radiation-use efficiency formulation with a coupled photosynthesis and stomatal conductance description (as in LPJ-GUESS) increased the overall model performance of the DSSAT model and its ability to capture responses to the combined effects of different factors affecting plant productivity (Boote et al, 2013).…”