Estimation of transpiration fluxes from rainfed and irrigated sugarcane in South Africa using a canopy resistance and crop coefficient model
Introduction
Sugarcane is grown in tropic and sub-tropic zones, where water is often scarce. Due to increasing ethanol and sugar demands, the area under sugarcane is rapidly growing worldwide (Rudorff et al., 2010). Sugarcane water consumption is relatively high. Large scale increases in sugarcane farming compared to other crops may increase overall catchment evapotranspiration and reduce streamflow. Efficient irrigation systems and good on-farm water management practices are crucial elements to optimize its water consumption. The extent of commercial rainfed and irrigated sugarcane systems in South Africa provides an opportunity to evaluate current methods for computing sugarcane over a wide range of conditions, and to derive a general model for this purpose. A valuable data set for evaluation of existing methodologies was derived from two studies in commercial rainfed (Jarmain and Everson, 2002) and irrigated (Jarmain et al., 2014) sugarcane fields in South Africa.
can be measured by means of a Bowen ratio system (e.g. Grantz and Meinzer, 1991, Inman-Bamber and McGlinchey, 2003, Jarmain and Everson, 2002), eddy covariance (e.g. Cabral et al., 2012, Pakoktom et al., 2013), a Surface Renewal system (Jarmain et al., 2014), weighing lysimeters (Olivier and Singels, 2012) or other systems. Although these methods are inappropriate for routine measurements, they are used for evaluation of indirect estimates. For example, Inman-Bamber and McGlinchey (2003) compared measurements from a Bowen ratio system with estimates by the direct Penman Monteith (PM) equation using a constant value for sugarcane surface resistance and fixed crop height for deriving aerodynamic resistances. They indicated that prediction errors of in a version of the direct PM equation could be rectified by changing the surface resistance. They compared their measurements with grass reference estimates derived from the standardized PM equation by the United Nations Food and Agriculture Organization (FAO56) and the combination of crop coefficient for sugarcane as proposed by Allen et al. (1998). They confirmed the values of 0.4 at the initial stage and 1.25 at mid-stage in FAO56 to be correct. The value of 0.7 for the end stage was not supported but they suggested a value of 1.25 for an adequate water supply throughout crop development. Olivier and Singels (2012) evaluated the effect of two crop residue layers and no residue cover on , measured with weighing lysimeters in South Africa. They calculated values from measurements and estimates. Averaged values for the initial stage were 0.31 for no residue cover (Bare), 0.25 for soil covered by a light layer of cane tops (Tops) and 0.18 for soil covered by a heavy layer of tops and dead leaves (Trash). These values were lower than the value of 0.4 proposed in FAO56. Averaged values for mid-stage were 1.12 for Bare, 1.01 for Tops and 1.13 for Trash, which were lower than the FAO56 value of 1.25. Averaged values for the end stage were 1.1 for Bare, 0.8 for Tops and 0.78 for Trash, which were higher than the value of 0.7 proposed in FAO56. Thus, in the absence of locally measured coefficients, generic values for are often used in hydrological studies and water management (ignoring the specific environmental conditions to which the generic applies).
Estimation of may be improved by decoupling the transpiration component from the soil evaporation component, as transpiration is disconnected from the soil physical conditions related to E. Allen et al. (1998) in their standard FAO56 approach also provide a procedure based on a two-layer crop coefficient model, which incorporates a crop basal coefficient and a coefficient for soil evaporation . This method is limited to well-watered conditions, and measurement of T for crops under water stress conditions (including rainfed) are likely to be overestimated. Allen et al. (1998) offer a method for calculating transpiration of water stressed crops by applying a water stress coefficient . This requires soil moisture to be determined for computation of T under dynamic growing conditions. A simple soil water balance such as CropWat (Smith, 1992) can be applied to describe the dynamics of soil moisture following rainfall and irrigation applications. Alternatively, soil moisture needs to be measured in situ with sensors (Dorigo et al., 2013) or by satellite (Scott et al., 2003).
Alternative to generic two-layer crop coefficient model, T can be predicted by the one-layer PM equation with a variable canopy resistance that responds to all environmental conditions (i.e. temperature, vapor pressure deficit, solar radiation, soil moisture) and to crop development (Dolman, 1993, Teixeira et al., 2008). The latter could potentially be more accurate under varying environmental and water limiting conditions than the generic FAO56 basal coefficient method with sugarcane crop coefficients. This is at least a hypothesis worth testing. The widely accepted Jarvis-Stewart model (Jarvis, 1976, Stewart, 1988) was used in this study to estimate in the PM equation. This model has the drawback that it needs to be calibrated for different types of forests (Matsumoto et al., 2008) and crop types (cotton and wheat: Garatuza-Payan et al., 1998; grass and millet: Hanan and Prince, 1997; maize and vineyard: Li et al., 2014). We believe, however, that once the Jarvis-Stewart model variables are calibrated for a crop such as sugarcane, it could be implemented for irrigated and rainfed fields over a range of climatic conditions. Another great advantage of resistance type of models over crop coefficients is that they help to compute instantaneous energy balance fluxes, which are used in climate, numerical weather prediction and remote sensing studies.
The idea of parameterizing sugarcane T fluxes with resistances is not entirely new. Grantz and Meinzer (1991) calculated for instance the relationship between rc and environmental conditions (photon flux density and humidity) for irrigated sugarcane. McGlinchey and Inman-Bamber (1996) calculated the surface resistance from the relation of the bulk stomata resistance and leaf area index for irrigated sugarcane. The current study combined environmental stress functions and LAI to calculate an hourly variable rc value. We calibrated the parameters of the environmental stress functions and LAI development at both hourly and daily intervals. Since we are looking for a general method for predicting sugarcane water demand and crop water productivity for rainfed and irrigated fields, the accuracy of T estimates derived from a direct one-layer PM equation were compared with that of the more classical FAO56 basal coefficient method.
Section snippets
Description of sites
Field data for this study were collected in South Africa in a commercial rainfed sugarcane field in the province of KwaZulu-Natal and in a commercial irrigated sugarcane field in the province of Mpumalanga (Fig. 1).
Water consumption of sugarcane
The FAO56 specifies the generic Kc of sugarcane of initial stage (0.4), mid-stage (1.25) and end stage (0.75) for water-unstressed conditions (Ks = 1). The generic Kc values of mid and end stage were adjusted for specific weather conditions as described by Allen et al. (1998). The weather-adjusted Kc values of mid-stage were 1.24 for rainfed sugarcane and 1.21 for irrigated sugarcane. The weather-adjusted values of end stage were 0.72 for rainfed sugarcane and 0.70 for irrigated sugarcane. Fig. 3
Conclusions
Because of the well tested bio-physical parameterization under a range of climatic conditions and landscapes, the Jarvis-Stewart model is widely used in climate and remote sensing models for calculating in different ecosystems. The FAO56 methodologies are very common due to their simplicity. While the FAO56 methods are sound, crop ET can be predicted more accurately with a variable canopy resistance instead of a variable crop coefficient. Moreover, the ability to compute T is important for
Acknowledgments
The authors would like to thank the University of KwaZulu-Natal (UKZN), the South African Sugarcane Research Institute (SASRI), the Department of Water Affairs and Forestry and the Water Research Commission (WRC) and the Department of Agriculture, Forestry and Fisheries of South Africa for contributing funding to the field measurements associated with the work. Results presented here where generated as part of the WRC project K5/2079//4 “Water use efficiency of irrigated agricultural crops
References (50)
- et al.
A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method
Agric. Water Manage.
(2006) - et al.
Modelling surface resistance from climatic variables?
Agric. Water Manage.
(2000) A multiple-source land surface energy balance model for use in general circulation models
Agric. For. Meteorol.
(1993)- et al.
Regulation of transpiration in field-grown sugarcane: evaluation of the stomatal response to humidity with the Bowen ratio technique
Agric. For. Meteorol.
(1991) - et al.
Stomatal conductance of west-central supersite vegetation in HAPEX-Sahel: measuremnts and empirical models
J. Hydrol.
(1997) - et al.
Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements
Field Crops Res.
(2003) - et al.
Dependence of stomatal conductance on leaf chlorophyll concentration and meteorological variables
Agric. For. Meteorol.
(2005) - et al.
Responses of surface conductance to forest environments in the Far East
Agric. For. Meteorol.
(2008) - et al.
Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements
Agric. For. Meteorol.
(2010) - et al.
Integrating soil water monitoring technology and weather based crop modelling to provide improved decision support for sugarcane irrigation management
Comput. Electron. Agric.
(2014)