IRRIGATION SCHEDULING IN SUGARCANE BASED ON ATMOSPHERIC EVAPORATIVE DEMAND By S.J. ATTARD 1,4, N.G. INMAN-BAMBER 2,4 and J. ENGELKE 3 1 CSIRO Sustainable Ecosystems, Kalamia Mill, Ayr, Qld 2 CSIRO Sustainable Ecosystems, Davies Laboratory, Townsville, Qld 3 Dept Agriculture, Kununurra, WA 4 CRC for Sustainable Sugar Production, Townsville, Qld Keywords: Irrigation, Scheduling, AED, Water Balance, Model. Abstract Increasing water use efficiency continues to be a significant challenge for the Australian sugar industry. Irrigated agriculture faces ever-increasing pressure to grow more from fewer inputs, and hence at lower cost, to remain viable. Increasing community awareness of environmental issues has raised expectations for irrigators to use bestpractice management to minimise off-site impacts and maximise productivity from a scarce resource such as water. Irrigation water is used most efficiently when crop requirement is defined accurately and water is applied to meet this demand both fully and at precisely the right time. Crop water requirement is based on crop response to water deficits, as well as atmospheric evaporative demand (AED) for water. In this paper, we develop two alternative scheduling techniques that utilise simple tables and computerised systems based on AED, knowledge of crop response to water stress, and soil water holding capacity. An example of the simple scheduling tables developed for growers in the Ord region of Western Australia indicated that irrigation should be as frequent as 10 days prior to the wet season and as infrequent as 21 days after the wet season, depending on soil type and ratoon date. The computerised AED based water balance technique was applied to three growers in the Burdekin, and it showed how scheduling with this technique could have improved the effectiveness of their operations. Introduction Increasing water use efficiency continues to be a significant challenge for the Australian sugar industry. Irrigated agriculture faces ever-increasing pressure to grow more from fewer inputs, and hence at lower cost, to remain viable. Communities, both in Australia and across the world, expect irrigators to use best-practice management to minimise off-site impacts and to maximise productivity from a scarce resource such as water. Irrigation water is used most efficiently when crop requirement is accurately defined. If we know how much water has been removed from the soil profile over a given period, irrigation can then be applied to meet this demand. Matching supply and demand is the best way to ensure sustainability of irrigation systems, both from an economic and environmental point of view (Meyer, 1997). Crop water requirement is based on crop response to water deficits and atmospheric evaporative demand (AED) for water. In full irrigation schemes such as the Burdekin, growers aim
to irrigate before yield accumulation slows down due to excessive soil water deficits. Under such management, crop water use is dictated by crop development and AED rather than soil constraints. Here, we consider crop water requirement only to include water infiltrating the soil, later to be taken up by roots or evaporated from the soil surface. Inefficiencies in irrigation application, caused by water loss through runoff, drainage or in the process of getting water to the crop, are not considered to be part of the crop water requirement. The United Nations Food and Agriculture Organization (FAO) has driven the development of globally applicable methodologies to determine crop water requirement (Smith et al., 1996). Early FAO standards were based on the Class-A pan (Smith et al., 1996), and later, pans and energy balance or aerodynamic models (FAO 24, Doorenbos and Pruitt, 1977). These standards have been revised and updated during the past two decades. Currently, FAO 56 recommends the use of the Penman-Monteith (PM) equation for estimating reference evaporation (ET 0 ) and crop evapotranspiration (ET C ) (Allen et al., 1998; Smith et al., 1996). The Penman-Monteith equation utilises standard weather data to give estimates of evapotranspiration that are more consistent with actual crop water usage than previous measures (Allen et al., 1998). State of the art techniques, such as eddy correlation and Bowen ratio energy balance (BREB), have been used to measure actual crop evapotranspiration (ET C ) for a wide range of crops around the world (Itier and Brunet, 1996). Crop factors (K C ) have been determined using the relationship: K C = ET C / ET 0. Different crops will have different K C coefficients, and K C for a particular crop will vary as the crop develops (Allen et al., 1998). The BREB technique was used to determine K C for the main growing period (K Cmid ) for sugarcane in the Burdekin and in Swaziland (McGlinchey and Inman-Bamber, 2002; Inman-Bamber and McGlinchey, 2003). This work confirmed that K Cmid was equal to 1.25 for sugarcane in FAO 56 and highlighted the robustness of sugarcane crop factors across environments and varieties. In the Australian sugar industry, the point at which soil water may become limiting to yield accumulation has conventionally been determined by stalk elongation measurements and AED has been measured indirectly using minipans (Shannon et al., 1996). This has been a highly successful practical technique for crops that have started stalk elongation. In this paper, we develop two alternative scheduling techniques. The first method relies on simple tables while the second uses a computerised system. Both techniques are based on AED, knowledge of crop response to water stress and soil water holding capacity. We show how AED based scheduling is being used in the Ord to increase irrigation water-use efficiency and how these techniques could be used elsewhere in the sugar industry. Methods Ord irrigation scheduling guidelines Recent research has provided the following four key pieces of information that farmers require for scheduling furrow irrigation of sugarcane in the Ord River irrigation area (ORIA). 1. Plant available water capacity (PAWC), which is a characteristic of soil hydraulic properties and rooting depth, was measured in situ for three major soil types in the ORIA (Muchow et al., 2001).
2. It was noticed that irrigation did not fully wet up the soil because of surface sealing after swelling of the 2:1 lattice clays prevalent in these soils. The amount of water in the profile after irrigation ranged from 75% to 85% of PAWC (Table 1). 3. Irrigation trials indicated that cane yield was not affected until the crop had extracted about 50% of PAWC. From this knowledge, farmers selected two target deficits, one at 50% of PAWC, the other at 40% for irrigation scheduling. 4. The amount of water used by the crop on a daily basis was estimated by the APSIM- Sugarcane model. This model has been found to represent the growth of sugarcane under a wide range of conditions (Keating et al., 1999). However, under Ord conditions, radiation use efficiency and hence crop water use had to be reduced after the crop had reached a certain biomass threshold (30 t/ha or about 80 t/ha of cane) in order to match simulated and measured crop water use and yield. Estimates of daily crop water use were not allowed to exceed 1.25*ET 0 based on BREB research in the Burdekin and in Swaziland (McGlinchey and Inman-Bamber, 2002). Simulations were performed with the APSIM-Sugarcane model using the above information and long-term climatic data from the Kimberly Research Station to generate best-bet (median) irrigation intervals for three different crop start dates and three soil types (Table 1). Soil Type Table 1 The amount of water available for plant use for three soil types with varying PAWC, and assuming irrigation is scheduled at the point where 50% of PAWC remains. A B C D PAWC Plant available water after irrigation with 80% refill (PAWC*80%) Target soil water deficit (PAWC*50%) Water available for use by the plant (B-C) 1 169 135 mm 85 mm 50 mm 2 190 152 mm 95 mm 57 mm 3 225 180 mm 113 mm 67 mm Determination of K C for the Burdekin Having established K C with the BREB for one crop in the Burdekin (Inman-Bamber and McGlinchey, 2003), it was then necessary to determine K C for a range of plant and ratoon crops with and without green cane trash blanketing. The APSIM-Sugarcane model was used to do this. Daily evaporation from the soil and crop was determined for crops planted or ratooned in each month of the year, and the total crop water use (soil evaporation plus transpiration) was divided by ET 0 to determine K C for each stage of development for each crop. K C was not allowed to exceed 1.25. Crop factors were determined for conditions when the soil surface was either dry or wet. Water balance technique The water balance technique applies a ledger style approach to determining the state of the soil water deficit. The process is easily maintained using a computerised spreadsheet model. The cumulative soil water deficit was determined using the relationship: S d = S d-1 + ET C I R
where: S d = soil water deficit at midnight of the current day; S d-1 = soil water deficit at midnight of the previous day; ET C = K C *ET 0, crop evapotranspiration of the current day; I = Irrigation during the current day; R = rainfall during the current day. As each day passes without irrigation or rainfall, the cumulative soil water deficit increases. When the deficit approaches a predetermined target deficit for a particular field, irrigators can consider the next irrigation event. The water balance approach required at least the same key pieces of information required for the ORIA scheduling tables. Items 1 to 3 below were required for each paddock, item 4 for the farm and item 5 for a homogeneous climatic region. 1. Plant available water capacity (PAWC), which is characteristic of soil hydraulic properties and rooting depth. 2. Amount of irrigation infiltrating the root zone. 3. Target soil water deficit or readily available water deficit (RAW deficit). RAW is determined from crop response to water stress and is often roughly 0.5*PAWC (Inman- Bamber et al., 2000). Alternatively, RAW capacity may be known for the soil from stalk measurements (Anon., 1998) or it could be determined using the method described below. 4. Effective rainfall. The grower is in the best position to provide this information using well-sited rain gauges, intuition and experience to estimate how much rainfall has infiltrated the soil. 5. Daily crop evapotranspiration derived from K C and ET 0. The water balance was set up to schedule future irrigations in a number of blocks for one Burdekin grower (grower A) who had no records of past irrigations. It was also set up to compare the water balance schedule with an historical schedule for one block where the grower had good records of irrigation application dates and some application amount records (grower B). At a third site (grower C), RAW was unknown and was determined using automatic stalk and leaf extension measurements (Inman-Bamber and Spillman, 2002). Grower C based the irrigation schedule on experience and maintained detailed records of each irrigation. A water balance was set up to monitor soil water deficit and then assist with the scheduling of future irrigations. All three grower sites were located within five kilometres of automatic weather stations (Campbell Scientific Inc., Logan, UT, USA) which logged rainfall, radiation, temperature, relative humidity and wind speed at hourly intervals. Results ORIA irrigation scheduling guidelines When the results of the ORIA simulations were presented to growers, best-bet intervals were provided for different rainfall categories. Growers preferred to consider only the <10 mm rain category suggesting that they could account for rainfall themselves and adjust irrigation intervals
accordingly. Best-bet irrigation intervals for the North Ivanhoe soil are provided in Table 2. For example, crops harvested on 1 August would receive an initial irrigation soon after harvest, the second irrigation could then occur 40 60 days later. However, these early irrigations are often based on individual farmer management practices, such as fertiliser application and herbicide incorporation. Irrigation begins in earnest in the month of October. Scheduling for October through to January would require irrigation to occur every 10 days, then 13 days for February and March, and so on until July when the crop would begin the dry down period (DD) prior to harvest. Farmers wanting to match AED would attempt to apply 57 mm per irrigation (Table 1, column D). However, in practice there is little grower control over how much water infiltrates the soil since soils will wet up rapidly to 75 to 85% PAWC until the surface seals, thereafter infiltration slows considerably. Table 2 Best-bet irrigation interval (days) for N Ivanhoe soil using a target soil water deficit of PAWC*50%. Harvest date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 Jun 11 13 13 14 DD 40 60 14 11 10 10 10 1 Aug 10 13 13 15 17 21 DD 40 60 10 10 10 1 Oct 10 11 13 13 16 20 21 17 DD 40 60 10 DD = dry down prior to harvest Crop factors for the Burdekin Crop factors were directly related to canopy development and to time after rain or irrigation. During the period of incomplete canopy, K C rose to about 1.1 after rain or irrigation mainly because of evaporation from the wet soil surface. After one day of no rain or irrigation, K C declined to about 0.6 and then reduced by about 0.2 per day until it reached a base level depending on stage of crop development and surface condition (bare or trashed). The K C values for dry soil surface conditions are shown Figure 1. For bare surface conditions K C started at 0.2 and remained constant before rising to a maximum of 1.25. In dry trashed surfaces, K C started at zero and initially increased slowly before rising rapidly to a maximum of 1.25. FAO 56 indicates that K C declines towards the end of the growth cycle. This was disputed in the work of Inman-Bamber and McGlinchey (2003). This does not mean that irrigation should not be reduced before harvest to enhance CCS. 1.4 1.2 1 Kc 0.8 0.6 0.4 0.2 0 Kc 0 Germination Full Canopy Germination Full Canopy Time Time Fig. 1 Generalised crop coefficient curve for the Burdekin for (a) sugarcane plant and burnt ratoons, and (b) green cane trash blanketed ratoons.
Water balance model Grower A An extract of the spreadsheet containing the water balance model developed for two blocks managed by grower A, is shown in Table 3. Information such as block, variety, crop class and (crop) start date, identifies the field being scheduled. Two other properties, PAWC and target deficit (RAW) were derived from measurement in a similar soil type differing only in the depth of the A horizon (Inman-Bamber et al., 2001). Table 3 Extract from grower A s water balance spreadsheet. Block 14 1 Block 15 1 Variety Q127 Variety Q117 Class Plant Class 3R PAWC 120 mm PAWC 120 mm Target deficit 60 mm Target deficit 60 mm Start date 2/04/02 Start date 16/06/02 Date ET 0 Rain Irrigation ETc RAW deficit Sd Irrigation ETc RAW deficit Sd 1/09/2002 3.6 0 100 4.5 0 0 100 3.9 0 0 2/09/2002 3.5 0 4.4 4.4 4.4 2.1 2.1 2.1 3/09/2002 3.1 0 3.9 8.3 8.3 1.6 3.7 3.7 4/09/2002 3.5 0 4.4 12.6 12.6 2.1 5.8 5.8 5/09/2002 3.8 0 4.8 17.4 17.4 2.3 8.1 8.1 6/09/2002 3.5 0 4.3 21.7 21.7 2.1 10.1 10.1 7/09/2002 2.3 0 2.9 24.6 24.6 1.4 11.5 11.5 The two blocks from grower A had different crop start dates. One block was a ratoon crop while the other was a plant crop. ET C for the crop planted in April was generally greater than 4 mm/day (25% greater than ET 0 ) in September since crop canopy was deemed to be complete at this stage. Daily ET C for the crop ratooned in June was less than 3 mm/day, apart from the day of irrigation as evaporation from the soil surface can be greater than ET 0 during or shortly after irrigation. Soil evaporation decreased as the soil surface dried and then, for the June ratoon, ET C fell below ET 0 because of the incomplete crop canopy and relatively low crop factors (0.2 < K C < 1.0). Two balances are shown in the water balance (Table 3). The RAW deficit column shows the cumulative exhaustion of RAW, while accounting for gains through rain or irrigation. The soil water deficit column ( S d ) is the result of water gains and losses from the whole profile which is capable of retaining an amount of water equal to PAWC. S d can vary between 0 and 1 times PAWC whereas RAW deficit, which is the soil water deficit in the effective rooting zone, is constrained between 0 and the target deficit or RAW capacity. Grower B The PAWC and RAW water balances for a plant and subsequent ratoon crop for one block managed by grower B are presented in Figure 2. In this case, PAWC (185 mm) was obtained from
laboratory measurements (P. Charlesworth, unpublished data), while RAW (60 mm) was estimated to be 32% of PAWC. These crops experienced several interesting periods. Firstly, according to the water balance, a period of irrigating too frequently was encountered between 6/12/99 and 30/01/00 since the RAW deficit rarely exceeded 50 mm. During this period, PAWC and RAW deficits were identical. The second period of interest occurred when the plant crop was dried off prior to harvesting. The PAWC deficit (S d ) exceeded the target deficit from mid-may to mid-august. During this period, the PAWC water balance was inaccurate because it failed to take into account that the reduced water in the profile becomes increasingly more difficult for the plant to extract. This did not detract from the water balance scheduling method at this stage because irrigation was withheld intentionally to enhance sucrose accumulation. Inaccuracies in the PAWC deficit affected the water balance after the plant crop was harvested because the first irrigation was only enough to fill RAW capacity. Few Burdekin growers actually know how much irrigation infiltrates the soil and it is generally assumed that irrigation fills PAWC to capacity. If PAWC deficit were in fact at a maximum at the end of the ratoon crop, the 60 mm applied during the first irrigation of the ratoon crop would not have all been readily available to the crop because a certain proportion of the available water is held more tightly by the soil capillaries regardless of how much of the profile is wet up after rain or irrigation. The reason for maintaining the PAWC balance is to remind growers of this point and to be mindful that the target deficit may need to be reduced if they suspect that irrigation did not refill the profile. It was fortunate that two irrigations and some rainfall kept the RAW deficit in the black until sufficient rain to fill the profile occurred in December 2001. However, after this date, irrigations were not frequent enough to prevent complete depletion of RAW capacity on several occasions. If grower B had used the water balance technique to schedule irrigations for this block, irrigation for the plant crop would have been less frequent than actual irrigation during December 1999 to February 2000 and irrigation for the ratoon crop would have been more frequent during January to April 2001.
Rainfall and Irrigation 200 180 160 140 120 100 80 60 40 0 20 40 60 80 100 120 140 160 Sd and RAW deficit 20 0 180 200 13/10/1999 13/12/1999 13/02/2000 13/04/2000 13/06/2000 13/08/2000 13/10/2000 13/12/2000 13/02/2001 13/04/2001 Rain Irrigation Full Point Target deficit Sd RAW deficit Fig. 2 Components of the water balance for a plant and ratoon crop managed by grower B in the Burdekin. The plant crop was harvested on 20 August. Red bars = Rainfall, black bars = irrigation, dark blue line = full point, green line= target deficit, light blue line = RAW deficit, brown line = PAWC deficit. Grower C The results of monitoring daily stalk growth rates and soil water balances over a three-month period in a Q171 A plant crop managed by grower C are shown in Figure 3. 80 0 70 20 60 40 Stalk growth, Irrigation, Rainfall and ETc (mm/day) 50 40 30 60 80 100 Sd and RAW Deficit 20 120 10 140 0 160 3/01/02 17/01/02 31/01/02 14/02/02 28/02/02 14/03/02 28/03/02 11/04/02 Rainfall Irrigation Stalk growth ETc Full Point Target deficit Sd RAW deficit Fig. 3 Components of the water balance for a Q171 A plant crop managed by grower C in the Burdekin. Red bars = Rainfall, black bars = irrigation, pink line = stalk elongation rate, grey line = daily ET C, dark blue line = full point, green line= target deficit, light blue line = RAW deficit, brown line = PAWC deficit.
The RAW deficit was defined as the soil water deficit at which the stalk elongation rate declined to 50% of the maximum. The RAW deficit thus ranged from 40 to 65 mm (Figure 3). This range in RAW was attributed to the fluctuation in daily ET C that ranged between 2.0 mm on cloudy days, up to 11.0 mm on hot sunny days. The crop growth rates tended to fall below the 50% of maximum at a faster rate during periods of high average daily AED. Thus RAW is not a constant as suggested earlier (Shannon et al., 1996; Inman-Bamber et al., 2000). Variations in RAW between summer and winter can be quite large (Inman-Bamber et al., 2002; Thompson, 1976). The water balance simplifies many attributes of the complex soil-plant-atmosphere continuum including variable RAW. A mid-value of 52 mm was selected for RAW (Figure 3). Stalk growth rate increased rapidly after rain and then decreased gradually as soil water deficit increased. During January, the grower irrigated roughly at the correct time to prevent PAWC deficit exceeding the target to any great extent. Stalk growth rate during January generally recovered fully after rain or irrigation. During February, irrigation was insufficient to meet crop demand and RAW depletion was often at the maximum. Stalk growth rates declined below 5 mm/day before rain refilled the profile in mid-february, enabling growth rates to exceed 25 mm/day once again. After this time, large deficits were allowed to develop and stalk elongation eventually ceased. During this period, grower C had scheduled irrigation at 4-day intervals but after being made aware of the reduced stalk growth he decided to irrigate almost every day from March 26 until April 3. Stalk growth rates recovered with daily irrigations, but to a limited extent because of reduced temperatures. Had the grower used the water balance he would have scheduled irrigation more frequently in February and less frequently in March and April. Discussion Both scheduling techniques described in this paper provided the irrigator with the ability to better match AED. The techniques provide two vital pieces of information required for efficient irrigation: i) when to apply the next irrigation, and ii) how much to apply. The ORIA irrigation scheduling guidelines used long-term climate data in conjunction with simulation modelling to develop best-bet irrigation intervals based on soil and irrigation characteristics. There are a number of limitations that need to be understood before using these guidelines. Firstly, irrigators need to assess how long to delay irrigation when rainfall is insufficient to fill the profile or to fully replace irrigation. A delay of one day per 10 mm rainfall is sometimes used. Secondly, if the crop experiences weather conditions significantly different to the long-term average, the guidelines will be less accurate. Despite these limitations, growers can at least match long-term average AED if they follow these guidelines. If this system was fully adopted, water would be saved and yields may be improved as well. Saving irrigations would reduce water diversions from the river systems, and would probably reduce runoff returns to the river and drainage leaks into the underground water system. The ORIA irrigation scheduling guidelines have provided an effective extension tool to quickly update irrigation recommendations based on current research work being conducted in the Ord. Growers have used the tables as a means to modify irrigation scheduling in conjunction with their experience in cane irrigation gained over the past six years. As familiarity with scheduling principles increases, it is feasible that some growers will move to a more precise scheduling method, such as the use of crop factors and the water balance technique. Closer attention to matching crop water demand and supply will also be driven to a large extent by the likely increases in the cost of water.
The current industry standard (minipan) and the water balance technique have similar limitations in that both do not take into account shallow water tables or lodging effects. However, unlike the minipan, the water balance technique does allow irrigators to have an up-to-date soil water balance or deficit recalculated on a daily basis for the entire crop cycle from germination through to harvest. Irrigation or rainfall amounts that only partially fill the profile are easily accommodated within the water balance model. Thus irrigators applying irrigation via furrow, trickle, high pressure or low-pressure systems would all be able to use the water balance technique. The water balance model offers an advantage over the minipan method, which was designed for furrow systems where PAWC is assumed to fill to capacity after each irrigation. The water balance model s ability to accurately predict ET C depends on the level of water stress experienced by the crop. When the crop is relatively free from water stress (S d RAW), soil water is not limiting and ET C is determined by crop and climatic conditions alone. However, as the crop experiences water stress (S d > RAW), the soil/root system limits ET C and the crop and climate component in the water balance model can no longer fully account for crop water use. Scheduling irrigation under these conditions will require more elaborate models that are generally available but not easy to use. The water balance model presented in this paper is designed for use by growers on their computers. The RAW and PAWC balances were both included to alert growers to possible situations when soil water supply may be controlling crop water use rather than crop and climatic factors. The results from the grower C case study highlighted the dynamic nature of RAW. Varying values of RAW could be easily included in the water balance. At present RAW is determined for summer conditions. Irrigating at an equivalent target deficit in winter would not waste irrigation water but it would lead to less than optimum use of rainfall since the profile would be generally wetter than necessary, for example RAW for a Red Kandosol was about 50 mm in summer but was more than 80 mm in winter (Inman-Bamber et al., 2002). Conclusions Irrigation research over the past 30 years has progressed to the stage where accurate and reliable estimates of crop water requirements are readily achieved. The reliability of accurate estimations of reference and crop evapotranspiration has seen the development of irrigation scheduling tools that have been successfully implemented around the world using a number of different crops (Salazar et al., 1996; Werner, 1996) including sugarcane (Singels et al., 1999, now available for South African growers on the web, http://www.sasa.org.za/sasex/irricane/index.htm). Irrigation scheduling based on FAO 56, highlights the potential for simple but reliable, weather-based estimates of crop water requirement to make significant contributions to irrigation efficiencies within the Australian sugar industry. Development of ORIA style guidelines is currently being developed for the Burdekin and could easily be developed for other fully irrigated regions (the Atherton Tablelands, for example). The Australian sugar industry could use the international success of irrigation scheduling technology as another tool for improving productivity. The ability to determine how much water to apply and when plays a significant role when attempting to maximise productivity while minimising inputs and off-site impacts.
Acknowledgments The authors acknowledge the contributions of farmers Jeff Cornford, Bill and Bruce McAllister and the Granshaw Family for their cooperation and collaboration. Also, Michael Spillman, CSIRO Sustainable Ecosystems (Townsville), for his valuable input and assistance with data collection and field and laboratory sampling. The authors also acknowledge Philip Charlesworth, CSIRO Land and Water (Townsville), for the use of unpublished data. This study was funded, in part, by the SRDC and the CRC for Sustainable Sugar Production. REFERENCES Allen, R.G., Pereira, L.S. Raes, D. and Smith, M. (1998). Crop Evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper No. 56. FAO, Rome. Anon. (1998). Irrigation of Sugarcane. BSES/SRDC publication, 68 p. Doorenbos, J. and Pruitt, W.O. (1977). Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No. 24. FAO, Rome. Inman-Bamber, N.G., Zund, P.R. and Muchow, R.C. (2000). Water use efficiency and soil water availability for sugarcane. Proc. Aust. Soc. Sugar Cane Technol., 22: 264 269. Inman-Bamber, N.G., Everingham, Y. and Muchow, R.C. (2001). Modelling water stress response in sugarcane: Validation and application of the APSIM-Sugarcane model. 10 th Australian Agronomy Conference, Hobart, 28 Jan. to1 Feb., 2001. Inman-Bamber, N.G. and Spillman, M.F. (2002). Plant extension, soil water extraction and water stress in sugarcane. Proc. Aust. Soc. Sugar Cane Technol., 24: 246 252. Inman-Bamber, N.G., Baillie, C. and Willcox, J. (2002). Tools for improving efficiency of limited water use in sugarcane. Proc. Irrigation Assoc. Austr., National Conference, Sydney, 21 23 May, 2002, 251 259. Inman-Bamber, N.G. and McGlinchey, M.G. (2003). Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements. Field Crops Research, in review. Itier, B. and Brunet, Y. (1996). Recent Developments and Present Trends in Evaporation Research: A Partial Survey. In: Camp, C.R., Sadler, E.J. and Yoder, R.E. ed. Proceedings of the International Conference, November 3 6, 1996, San Antonio. ASAE, St. Joseph, Michigan, USA, 1 20. Keating, B.A., Robertson, M.J., Muchow, R.C. and Huth, N.I. (1999). Modelling sugarcane production systems I: Description and validation of the APSIM Sugarcane module. Field Crops Research, 61: 253 271. McGlinchey, M.G. and Inman-Bamber, N.G. (2002). Robust estimates of evapotranspiration for sugarcane. Proc. S. Afr. Sug. Technol. Ass. (CD-ROM), 76: 245 249.
Meyer, W.S. (1997). The irrigation experience in Australia lessons for the sugar industry. In: Keating, B. and Wilson, J. ed. Intensive Sugarcane Production: Meeting the Challenge Beyond 2000. Proceedings of the Sugar 2000 Symposium, Brisbane, Australia, 20 23 August 1996. CAB International, Wallingford, UK, 437 454. Muchow, R.C., Triglone, T., Plunkett, G.M., Sherrard, J.H., Wood, A.W. and Engelke, J.H. (2001). Field responses to irrigation in the Ord sugar industry. Proc. Aust. Soc. Sugar Cane Technol., 23: 109 115. Salazar, L.J., Thompson, K.R. and Crane, K. (1996). Computerised irrigation scheduling in the San Luis Valley of Colorado. In: Camp, C.R., Sadler, E.J. and Yoder, R.E. ed. Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 6, 1996, San Antonio. ASAE, St. Joseph, Michigan, USA, 75 80. Shannon, E., McDougall, A., Kelsey, K. and Hussey, B. (1996). Watercheck: A co-ordinated extension program for improving irrigation efficiency on Australian cane farms. Proc. Aust. Soc. Sugar Cane Technol., 18: 113 118. Singels, A., Bezuidenhout, C.N. and Schmidt, E.J. (1999). Evaluating strategies for scheduling supplementary irrigation of sugarcane in South Africa. Proc. Aust. Soc. Sugar Cane Technol., 21: 219 226. Smith, M., Allen, R. and Pereira, L. (1996). Revised FAO Methodology for Crop Water Requirements. In: Camp, C.R., Sadler, E.J. and Yoder, R.E. ed. Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 6, 1996, San Antonio. ASAE, St. Joseph, Michigan, USA, 115 123. Thompson, G.D. (1976). Water use by sugarcane. The South African Sugar Journal, 60: 627 635. Werner, H.D. (1996). Checkbook irrigation tables developed from ag meteorology data. In: Camp, C.R., Sadler, E.J. and Yoder, R.E. ed. Evapotranspiration and Irrigation Scheduling. Proceedings of the International Conference, November 3 6, 1996, San Antonio. ASAE, St. Joseph, Michigan, USA, 81 86.