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Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738

Water SA vol.35 n.1 Pretoria Jan. 2009


A catchment-scale irrigation systems model for sugarcane part 1: model development



NG MoultI; NL LeclerI, II, *; JC SmithersI; DJ ClarkI

ISchool of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa
IISouth African Sugarcane Research Institute, Private Bag X02, Mount Edgecombe, 4300, South Africa




In South Africa, the demand for water exceeds available supplies in many catchments. In order to justify existing water requirements and to budget and plan in the context of growing uncertainty regarding water availability, a model to assist in the assessment and management of catchment water supply and demand interactions, and the associated impacts on the profitability of irrigated sugarcane, has been developed. The model, ACRUCane, operates as a submodel within the ACRU agrohydrological model and simulates the water budget of a field of irrigated sugarcane. The water budget is based on the integration of several widely accepted algorithms and concepts, accounts for different irrigation system types performing at different levels of uniformity and different water management strategies. Furthermore, it can simulate a wide variety of water availability scenarios and constraints through its link with ACRU simulated hydrology. The crop yield algorithms used in the model were verified using data from three different irrigation trials with widely varying irrigation treatments, where the model was shown to adequately distinguish the impacts of different watering strategies on crop yields. A description of the model algorithms and results from verification studies are presented in this paper. Application of the model is presented in a companion paper.

Keywords: ACRUCane, irrigation systems, water management, crop modelling, hydrology, water resources, sugarcane




The demand for water exceeds available supplies in many catchments in South Africa (NWRS, 2004). Since a substantial amount of water is assigned to irrigated agriculture (Ascough and Kiker, 2002), farmers are facing increasing pressure to use water more effectively and to justify existing water use. In order to justify existing water requirements and to budget and plan in the context of growing uncertainty regarding water availability, a tool to assist in the assessment and management of catchment water supply and demand interactions, and the associated impacts on the profitability of irrigated sugarcane, is needed.

While there have been many useful model developments for sugarcane and water resources management, none of these provide all the necessary decision support information in an integrated fashion. Therefore, the development of a catchment-scale irrigation systems model was initiated. The model capabilities required to provide adequate decision support information were identified by Moult (2005) as being the following:

  • Modelling the soil water balance at a field scale for irrigated areas and at a catchment scale for non-irrigated areas,
  • Linking an accurate estimation of crop water requirement for an irrigated area with the availability of water at a catchment scale,
  • Explicitly accounting for the impact of the performance of different irrigation systems, including non-uniform water applications, on the hydrology and, ultimately, on the sugarcane yield of an irrigated area, and
  • Assessing the impact of different supply constraints on sugarcane yield, and estimating both sugarcane and sucrose yield.

The objective of this paper is to describe the concepts and development of a model to meet the above requirements and to report on verification of sugarcane and sucrose yields simulated by the model.



The development of the conceptual model involved a review of the available models with respect to their potential to meet the objectives of the project. During the reviewing process concepts and algorithms pertinent to the development of the model were selected such that, when integrated, they would meet the stated model requirements. The models reviewed included: SWB (Campbell and Diaz, 1988). CANEGRO (Inman-Bamber, 1991), ACRU (Schulze, 1995), APSIM (McCown et al., 1996), CANESIM (Singels et al., 1998) and ZIMsched 2.0 (Lecler, 2003). The FAO Irrigation and Drainage Paper No. 56 (FAO 56, Allen et al., 1998) was also reviewed as it is fundamental to the water budget used in ZIMsched 2.0 and SWB. Full reviews of these models are provided by Moult (2005). A conclusion of the review process was that despite their respective strengths, none of these models and associated algorithms incorporated all the desired system processes in an integrated fashion. In particular none were able to represent the link between catchment hydrology, water availability, irrigation demand, non-uniform irrigation water applications and associated crop yields The development of the ACRUCane model is described in the following section.

Model description

The water budget in ACRUCane is based primarily on a unique integration and refinement of robust algorithms from FAO 56 (Allen et al., 1998) and the ACRU model (Schulze, 1995) and is very similar to the water budget used in ZIMsched 2.0 (Lecler, 2003)

The ACRU model is a catchment-scale agrohydrological model capable of simulating catchment hydrology and many different water supply scenarios (Lecler et al., 1995). Consequently, the ACRU model was used to simulate runoff from the catchment and water storage which was linked to a smaller sub-model, developed to simulate the water budget of an irrigated field of sugarcane and the associated yields when irrigated with different types of irrigation systems. This model is referred to as ACRUCane. A description of the fundamental aspects of the water budget for ACRUCane follows.


Wetting events occur as a result of either rainfall or irrigation, both of which can potentially generate runoff. Runoff from the irrigated area is simulated using an equation developed by the Soil Conservation Service (USDA, 1985) and adapted for use in South Africa by Schmidt and Schulze (1987) and Schulze et al. (1995).


Q = surface runoff depth (mm)
PI = daily wetting amount (mm), i.e. rainfall and/or irrigation
c = coefficient of initial abstraction
S = potential maximum water retention of the soil, taken as the soil water deficit below porosity, prior to a wetting event (mm)

Rainfall and/or irrigation that do not generate runoff are assumed to infiltrate into the soil immediately after the wetting event has occurred. Rates of infiltration are not simulated in ACRUCane. Once in the soil profile, water leaves the soil through either evapotranspiration or deep percolation.


Evapotranspiration from the cropped surface is determined using the dual crop coefficient methodology described by Allen et al. (1998).


ETc = evaporation from a cropped surface (mm)
Kcb = basal crop coefficient
Ke = coefficient controlling evaporation from the soil
ET0 = reference evaporation from a hypothetical short- grass surface (mm)

Using dual crop coefficients allows the separation of evaporation from a cropped surface into two processes, namely, transpiration (Et in mm) and evaporation from the soil (Es in mm). Treating these two processes separately is important because prior to the development of significant canopy cover, water losses are dominated by evaporation from the soil surface. Accurate estimation of Es is important as it can be highly variable and is dependent on the wetting fraction and wetting frequency of the soil (Lecler, 2003).


If, at the end of the day, the soil moisture content of the root zone (the portion of soil occupied by the roots) is above the field capacity (FC) of the soil, then drainage of the profile is initiated. The fraction of moisture above the FC that drains from the soil profile is dependent on the soil textural class and the amount of excess water. Default values for the drainage rate are related to the soil texture, but can be overridden by a user-specified value.

Crop processes

To aid in simulating the water budget, phenological processes such as root growth and canopy development are modelled. Root growth is simulated using a methodology described by Lecler (2003) and accounts for the crop's increasing access to soil moisture during the course of the growing season. Canopy development is simulated using a model described by Singels and Donaldson (2000) and is used to account for the effects of light interception and soil shading on evaporation of water from the soil and increased crop water usage as the crop grows.

Irrigation systems

Different types of irrigation system hardware are accounted for in several ways in ACRUCane. The irrigation system type, e.g. 'drip' irrigation, is associated with system specific attributes such as the fraction of soil wetted by irrigation, and whether or not interception of irrigation water applications is simulated. Included in the required input parameter set is an irrigation uniformity index such as the Distribution Uniformity (DU) to enable the simulation of non-uniform irrigation water applications which occur in practice. This is achieved using multiple water budgets and assuming a normal distribution of irrigation depths as described by Lecler (2003) and Ascough and Lecler (2004). The impacts associated with water management are represented through the simulation of a wide range of irrigation scheduling options.

Irrigation scheduling options

Different irrigation practices impact significantly on the hydrology of the area being irrigated. ACRUCane allows the user a choice of six different modes of irrigation scheduling, which include the four options available in the ACRU model (Schulze, 1995) as well as two additional options. In ACRUCane the irrigation requirement is determined for the scheduling option selected by the user and is then applied at the beginning of each day. Note that in all scheduling options described below, an irrigation requirement is determined. The amount that is actually applied to the crop is limited by water availability from the supply source, and in some cases, the capacity of the irrigation system, i.e. the minimum cycle time.

Option 1 Refill the soil profile at a specified fraction of total available water (TAW)

In this mode of scheduling, a user input fraction of TAW is used to determine the maximum allowable depletion of water in the soil profile. Once this level has been reached, an irrigation requirement is generated to refill the soil profile to the FC, or to a water level below the FC, thus leaving a portion of the soil water store to be filled by precipitation which may occur. It is an efficient form of irrigation as irrigation is only initiated when it is necessary to prevent crop water stress.

Option 2 Apply a fixed amount of water in a fixed cycle

For practical purposes, farmers often irrigate using a fixed cycle and fixed application amount for different times of the year. In ACRUCane, the user selects both the application depth and the interval between applications. The cycle length is assumed to continue throughout the growing season, unless a threshold amount of rainfall occurs which interrupts and causes the irrigation cycle to restart.

Option 3 Apply a variable amount of water in a fixed cycle

This mode of irrigation scheduling results in irrigation applied using a fixed cycle length but with varying amounts or irrigation water applied. The user specifies the cycle length and the system capacity. At the beginning of each cycle the depth of water required to fill the soil profile up to the FC is determined. If the required amount is less than the system capacity then the irrigation requirement is met, otherwise the applied depth is limited by the system capacity. As with Option 1, a certain portion of the soil water store can be left unfilled for potential rainfall events that may supplement irrigation.

Option 4 Duplicate a known irrigation regime

Using this option the user can simulate a known watering regime, which is read into the model from a user prepared hydrometeorological data file.

Option 5 Apply irrigation water using a fixed summer and winter cycle

A fifth scheduling option was created for ACRUCane to enable the model to represent a common irrigation practice of having separate irrigation cycles for the summer and winter seasons. In this option, the user specifies the cycle length, application amount and the starting dates of the summer and winter cycles.

Option 6 Refill the soil profile at specified moisture depletion level

The sixth option added to ACRUCane is similar to Option 1. However, instead of specifying the fraction of TAM at which irrigation is to take place, the user specifies a fixed depletion level in mm below FC at which irrigation is to be triggered. In this way it is possible for the user to apply, say, 25 mm once the soil water has depleted 30 mm below FC, leaving 5 mm to be filled by precipitation.

Water Supply

A variety of water supply options can be simulated by ACRUCane through the ACRU model. These options are illustrated in Fig. 1. The user can thus quantify the impact of different water supply options and constraints on the water budget and ultimately the yield of an irrigated sugarcane crop.




To be able to simulate the impact of different management practices effectively, types of irrigation systems, water supply limitations and environmental conditions on sugarcane yield, it is necessary to estimate both the cane yield and quality of cane. In ACRUCane, several algorithms to simulate sugarcane yield and quality are used to provide a range of comparable outputs.


A conceptually sound radiation-based sucrose yield and biomass accumulation yield algorithm developed for CANEGRO has been included in ACRUCane. This model estimates, inter alia, the sucrose and fibre content of the stalk. The complexities of this model are beyond the scope of this paper and a comprehensive description of this yield model is provided by Singels and Bezuidenhout (2002). Although conceptually superior, this model requires detailed inputs such as daily radiation which may not always be available. For this reason, a conceptually simpler transpiration-based estimated recoverable crystal (ERC) model developed for ZIMsched 2.0 is also included, as it provides a reliable surrogate for sucrose yield.

Estimated recoverable crystal (ERC)

To estimate ERC, an algorithm developed by Doorenbos and Kassam (1979) and modified by De Jager (1994) and Lecler (2003) is used in ACRUCane.


Ya = actual yield of ERC (t·ha-1)
Yp = potential yield of ERC (t·ha-1)
Kyi = yield response factor for the ith growth period
T = simulated actual transpiration (mm)
Tm = simulated maximum transpiration, i.e. with no soil water stress (mm)

Thus with an estimate of the potential yield of ERC or sucrose (Yp) it is possible to determine the actual yield (Ya) by accounting for the impacts of water stress using the ratio of actual to potential transpiration at different times in the growth cycle. The potential sucrose yield is obtained using a modified version of the relationship derived by Thompson (1976), as described by Lecler (2003).

Sugarcane yield

In addition to the ERC yield estimation, two empirical transpiration-based algorithms are included to estimate tons of sugarcane produced per hectare. The first is the Thompson equation (Thompson, 1976):


YT = tons of cane per hectare (t·ha-1)
ΣET = accumulated actual evapotranspiration (mm)

The second is an equation used in the CANESIM model, developed by fitting a second order polynomial to stalk matter and cumulative transpiration simulated by the CANEGRO model for several widely varying climates (Singels et al., 1999):


Yc = tons of cane per hectare (t·ha-1)
ΣT = accumulated actual transpiration (mm)

It must be noted that using transpiration, as opposed to evapotranspiration, is a more desirable option to use as a driver of yield. When using evapotranspiration, evaporation from the soil is inherently included and significant variation, dependent of the wetting fraction and frequency of the irrigation system, can occur prior to extensive canopy development An irrigation system that wets the entire soil surface at a high frequency, such as a centre pivot, would result in high initial evaporation from the soil and thus high evapotranspiration values and simulated yield. However, water loss via soil water evaporation is not used beneficially by the plants and yields simulated under these circumstances can be a