Cultivating C4 crops in a changing climate: sugarcane in Ghana

Abstract

Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa B Sultan, P Roudier, P Quirion et al.Potential forcing of CO2, technology and climate changes in maize (Zea mays) and bean(Phaseolus vulgaris) yield in southeast Brazil L C Costa, F Justino, L J C Oliveira et al. Abstract Over the next few decades, it is expected that increasing fossil fuel prices will lead to a proliferation of energy crop cultivation initiatives. The environmental sustainability of these activities is thus a pressing issue-particularly when they take place in vulnerable regions, such as West Africa. In more general terms, the effect of increased CO 2 concentrations and higher temperatures on biomass production and evapotranspiration affects the evolution of the global hydrological and carbon cycles. Investigating these processes for a C4 crop, such as sugarcane, thus provides an opportunity both to extend our understanding of the impact of climate change, and to assess our capacity to model the underpinning processes. This paper applies a process-based crop model to sugarcane in Ghana (where cultivation is planned), and the São Paulo region of Brazil (which has a well-established sugarcane industry). We show that, in the Daka River region of Ghana, provided there is sufficient irrigation, it is possible to generate approximately 75% of the yield achieved in the São Paulo region. In the final part of the study, the production of sugarcane under an idealized temperature increase climate change scenario is explored. It is shown that doubling CO 2 mitigates the degree of water stress associated with a 4 • C increase in temperature.

SimSphere model sensitivity analysis towards establishing its use for deriving key parameters characterising land surface interactions

“…The a priori bounds used for the parameters in the SA correspond to the first version of the parameter ranges considered in the uncertainty analysis (i.e., derived from expert knowledge). As cited by Wang et al (2005), for sensitivity analyses, Bouman (1994) advises using parameter ranges as broad as possible within the limits of the model validity domain. Once the parameters' a priori bounds have been set, ensemble runs are performed with all the parameter sets.…”

“…Crop models are generally used to simulate sugarcane production at site scale, with specific parameters (Cheeroo-Nayamuth et al, 2000). Land surface models (LSM) are rather used to estimate the spatial distribution of crop productivity under different soil and climatic conditions, over a region or even over the globe, but with a simpler and generic description of sugarcane plants (Black et al, 2012;Cuadra et al, 2012;Lapola et al, 2009). Agro-LSM models stand at the interface between plot-scale crop models and global LSMs.…”

ORCHIDEE‐STICS, a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology

“…To our knowledge, three agro‐LSMs include sugarcane (Table ): Agro‐IBIS, LPJml, JULES (Black et al ., ; Surendran Nair et al ., ). A highly simplified sugarcane new crop functional type was added in LPJml (Lapola et al ., ) and sugarcane has also been included to the Agro‐IBIS and JULES models with a different approach, by adding a new module with specific parameters and allocation rules (Black et al ., ; Cuadra et al ., ). However, none of these studies included a thorough evaluation of the sensitivity of the models to the many parameters, or their calibration.…”

JULES-BE: representation of bioenergy crops and harvesting in the Joint UK Land Environment Simulator vn5.1

“…Future versions of JULES will use the Reduced Ecosystem Demography (RED) approach which represents separate mass classes within a PFT (Moore et al, 2018). Alternatively, an approach could be implemented similar to that of Black et al (2012), in which three PFTs are used to represent different age classes of sugarcane, although this would not be compatible with dynamic vegetation. Given these difficulties, and the fact that JULES is a global model, accurate average yields with reduced variability compared to observations is likely to be an acceptable compromise for most applications of JULES-BE.…”

“…Therefore, this study fits best with the DGVM approach, which allows analysis of the impacts of bioenergy on climate and land surface processes. JULES has been used to model bioenergy systems before (Hughes et al, 2010;Black et al, 2012;Oliver et al, 2015), at site level, but these approaches have not been integrated into JULES's DGVM, TRIFFID, which links plant productivity to soil carbon and the global carbon cycle. The improved representation of harvesting and yield we present here is unique because https://doi.org/10.5194/gmd-2019-175 Preprint.…”