Value of seasonal forecasting for sugarcane farm irrigation planning

European Journal of Agronomy 104 (2019) 37–48

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European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja

Value of seasonal forecasting for sugarcane farm irrigation planning a,b,⁎

b

b

b

c

Duc-Anh An-Vo , Shahbaz Mushtaq , Kathryn Reardon-Smith , Louis Kouadio , Steve Attard , David Cobonb, Roger Stoneb a b c

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University of Southern Queensland, Institute for Advanced Engineering and Space Sciences, Toowoomba, QLD 4350, Australia University of Southern Queensland, Centre for Applied Climate Sciences, Toowoomba, QLD 4350, Australia AgriTech Solutions, Home Hill, QLD 4806, Australia

ARTICLE INFO

ABSTRACT

Keywords: Seasonal climate forecast Sugarcane Crop modelling APSIM Bio-economic modelling Irrigation planning Decision support Value of information

Seasonal climate forecasts (SCFs) have potential to improve productivity and profitability in the sugar industry. However, they are often underutilised due to insufficient evidence of the economic value of the forecasts, especially when there is a level of uncertainty associated with SCFs. Here, we demonstrate the value of integrating SCFs at various forecast quality (skill) levels into seasonal irrigation planning for sugarcane farming. A seasonal forecast system based on ENSO (El Niño Southern Oscillation) phases was parameterised by forecast quality to predict seasonal precipitation tercile (i.e. wet, neutral and dry) categories. A bio-economic model was developed to determine water-yield-profit relationships. Sugarcane production under different climatic conditions and irrigation scheduling scenarios was simulated using the Agricultural Production Systems sIMulator (APSIM)-Sugar, calibrated using case study information from one of Australia’s major irrigated sugarcane growing regions. We then employed an expected profit approach to achieve an optimal profit, rather than the more conventional optimal yield, for plant and ratoon crops to quantify the potential value of using SCFs in sugarcane irrigation decision making. The results show that using skilled SCF systems in sugarcane irrigation decision making can help growers improve their gross margin compared to that achieved in the absence of climate information (economic value). With a perfect forecast of moderate climatic conditions, an average economic value of up to AUD 27 ha−1 per annum was achieved, while forecasts of moderate wet or dry conditions indicated gains of up to AUD 40 and 43 ha−1 per annum, respectively, and forecasts of extreme wet or dry conditions delivered economic gains of up to AUD 150 and 260 ha−1 per annum, respectively. With the current seasonal climate forecast skill of 60% (based on the ENSO phases forecasting system) in the case study region, an average gain of up to AUD 4.5 ha−1 per annum was realised, with up to AUD 6.2 and 7.1 ha−1 per annum, respectively, for moderate wet and dry forecasting and up to AUD 92 and 43 ha−1 per annum, respectively, for extreme wet and dry forecasting. Improvements in the skill and reliability of SCFs will be important for achieving greater productivity and/or profitability and the wider uptake of climate forecasts in agricultural decision making.

1. Introduction Water is a critical input in agricultural production systems and often in limited supply. Irrigated agriculture, in semi-arid regions of Australia and globally, depends on the availability of reliable water resources, which in turn depend upon rainfall and connectivity to stored water (e.g. in reservoirs) (Mushtaq et al., 2012). Among the many management decisions on farms, optimal use of available irrigation water can be both complex and challenging. Growers have to deal with conflicting targets of minimising water use and maximising yields and profitability while facing uncertainty about future weather and seasonal climate.

⁎

Climatic uncertainty often leads to conservative management strategies, especially with regard to irrigation, that sacrifices productivity in order to reduce the risk of losses in poor years (Jones et al., 2000). Improved and reliable weather and seasonal climate forecasts are key to improving water management decisions and significant effort has gone into modelling the outcomes of different decisions and developing decision support tools to assist decision making on farms. Typically, this includes: (i) using weather forecasts for short-term forecasting of daily reference evapotranspiration to facilitate real-time irrigation decision making (Perera et al., 2014; Yang et al., 2016; Liu et al., 2017; Zhang et al., 2018; Li et al., 2018); (ii) using weather and seasonal climate

Corresponding author at: University of Southern Queensland, Centre for Applied Climate Sciences, Toowoomba, QLD 4350, Australia. E-mail address: [email protected] (D.-A. An-Vo).

https://doi.org/10.1016/j.eja.2019.01.005 Received 12 June 2018; Received in revised form 19 November 2018; Accepted 11 January 2019 1161-0301/ © 2019 Elsevier B.V. All rights reserved.

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forecasts to modify irrigation rules in simulation models, namely a simulation approach (e.g. Wang and Cai, 2009); and (iii) employing weather and seasonal climate forecasts in stochastic-dynamic simulation-optimisation frameworks, namely a simulation-optimisation approach, in which the real weather conditions are progressed as time advances (e.g. Sunantara and Ramirez, 1997; Brown et al., 2010; Jamal et al., 2018). Simulation and simulation-optimisation approaches have been widely used in real-time irrigation scheduling. There have been many studies which have incorporated short-term weather (e.g. Wilks and Wolfe, 1998; Wang and Cai, 2009; Cai et al., 2010; Hejazi et al., 2013; Mishra et al., 2013) and medium-term seasonal climate (e.g. Sunantara and Ramirez, 1997; Wang and Cai, 2009; Brown et al., 2010; Jamal et al., 2018) information and forecasts in irrigation scheduling optimisation, including the optimal timing of irrigation events and the amount of irrigation applied. While the simulation approach is normally limited to perfect forecasts, the simulation-optimisation approach can handle imperfect forecasts, implicitly and explicitly (Jamal et al., 2018). Implicitly, uncertainty is represented through various scenarios for weather and climate variables in the stochastic programming and the optimisation problem corresponding to each scenario is a deterministic problem which is solved separately. Seasonal climate scenarios employed are typically the observed historical climate records and thus take into account the climatology rather than actual seasonal climate forecasts. Explicit stochastic programming, however, can directly handle probability distributions of weather and climate variables including those suggested by forecast systems. There are two issues associated with the simulation-optimisation approach. Firstly, as pointed out by Brown et al. (2010), it normally requires over-simplistic crop models, partly limiting its practical usefulness. Secondly, the optimisation problem needs to be solved repeatedly on a real-time basis, which is a computational burden. Brown et al. (2010) and recently Jamal et al. (2018) addressed the first issue by employing comprehensive crop simulation models—i.e. Agricultural Production Systems sIMulator (APSIM) and Soil Water Atmosphere Plant (SWAP), respectively—in simulation-optimisation frameworks for real-time irrigation decision support. However, comprehensive crop models can escalate the computational burden of the simulation- optimisation approach for which the solution time scales need to be aggregated (for instance, from a daily basis to weekly, monthly or seasonal) to balance between accuracy and efficiency. Specifically for sugarcane cropping in Australia, weather and climate information is often used in irrigation related applications. Significant efforts have been made in using climate information to determine the optimum timing of the next irrigation event, given a hypothetical application, for achieving the best yield (e.g. Everingham et al., 2002; Inman-Bamber et al., 2005; Inman-Bamber et al., 2007). In these simulations, daily data — precipitation, temperature and solar radiation—of historical climate years (i.e. climatology) were incorporated in the APSIM-Sugar model (Keating et al., 1999, 2003; Holzworth et al., 2014) to simulate crop growth through to harvest dates. Everingham et al. (2002) employed a number of irrigation timing strategies and classified the yearly optimum results based on the El Nino-Southern Oscillation Index (SOI) phases (Stone and Auliciems, 1992; Stone et al., 1996) of the current month to determine the optimum time for the next irrigation application. Inman-Bamber et al. (2005; Inman-Bamber et al., 2007) determined the optimum time for the next irrigation application, based on all historical climate records, using an optimisation procedure called ‘Caneoptimiser’ (Inman-Bamber et al., 2002) to apply irrigation when most needed to limit yield loss due to water stress. It can be seen then that most irrigation related applications of seasonal climate forecasts found in the published literature have focused on improving the efficiency of irrigation water use rather than quantifying the economic value (extra gross margin compared to that achieved without using forecasts) of the forecasts. In this work, we aim

to demonstrate the economic value of seasonal climate forecasts using El Niño Southern Oscillation (ENSO) phases as predictors for the optimal planning of seasonal irrigation water use in sugarcane cropping. To achieve the main aim while minimising the computational burden, we focused on seasonal water planning; i.e. achieving optimal seasonal supply of irrigation water in regard to crop water requirements and available seasonal climate forecasts, rather than real-time irrigation decisions. The SCF system is parameterised by forecast quality (i.e. skill) in predicting seasonal precipitation tercile (i.e. wet, neutral, dry) categories, based on historical data. Such a parameterised forecast structure allows analysis of the impact of forecast quality (i.e. from no skill to perfect skill) on the economic value of the forecast, and thus the value of any current forecast system, based on its skill level, can be readily determined. The comprehensive APSIM-Sugar model, calibrated using case study information, was used to simulate crop production under different climatic conditions and irrigation schedules. We then develop a bio-economic model of forecast use explicitly incorporating forecast uncertainty. Optimisation was conducted to secure maximised expected farm profits, rather than the conventional optimal yields, for plant and ratoon crops, which helps quantify the potential economic value of using SCFs as beneficial information in sugarcane seasonal irrigation decision making. The proposed simulation-optimisation approach for irrigation is innovative in its way of accounting for uncertainty. Instead of incorporating uncertainty in the probability distributions of the forecasted variables, the uncertainty here is explicitly represented by systematic forecast quality parameterisation. Moreover, the solution time scale of the present simulation-optimisation approach is seasonal, matching with the time scales of the forecasts and reducing the computation cost. This approach allows optimal seasonal irrigation water to be determined for whole cropping seasons over which the forecasted ENSO phases apply. We anticipate that such an integrated framework can provide a pathway for better communication with end users to support improved use of forecasts in irrigation decision making. 2. Seasonal water planning problem At the beginning of any irrigation season, growers typically need to plan for the required seasonal water supply to crops given the available water allocation despite their uncertain knowledge of the coming seasonal climate. To support this planning, the aim here is to use a simulation-optimisation framework to demonstrate the potential economic value of using seasonal precipitation forecasts to achieve optimal seasonal water supply i.e. maximising expected profits of irrigation water on a seasonal aggregation period. Optimal seasonal water supply is the optimal, seasonally aggregated irrigation water amount resulting from the different irrigation amounts applied at different times to meet crop growth requirements throughout the irrigation season. Focusing on the seasonal time scale, the modelled seasonal water amount is assumed to be divided equally over uniform time periods—a common approach for reducing computational burden in real-time irrigation scheduling (Jamal et al., 2018). The problem of determining the optimal amount of water required thus reduces to optimising equal applied water amounts over uniform irrigation periods. Jamal (2016) recommends that seven days is a good irrigation aggregation period to balance between computing efficiency and accuracy. Here, given our focus is not on real-time irrigation scheduling and the different sugar cane crop stages have variable durations (15 months for plant crop and 13 months for ratoons), we set a ten day uniform irrigation period in the sugarcane growth simulation to capture the variety of irrigation practices. While this is not normal practice, it fits with our aim of investigating the potential for seasonal climate forecasts to contribute to increased farm profitability. At the same time, we acknowledge that more regular (short) irrigation periods might result in better water use efficiency (AnVo et al., 2017). 38

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Table 1 Contingency table between ENSO phase predictors and precipitation tercile outcomes, with aij (i, j {1, 2, 3} ) being the number of observation falling into the category of a forecast i (i = 1 for a La Niña predictor, i = 2 for a Neutral predictor, and i = 3 for an El Niño predictor) and a moderate outcome j ( j = 1 for Wet, j = 2 for a Normal, and j = 3 for Dry).

Predictor = La Niña Predictor = Neutral Predictor = El Niño

Outcome = Wet

Outcome = Normal

Outcome = Dry

a11 a21 a31

a12 a22 a32

a13 a23 a33

Table 2 A forecast system with ENSO phases as predictors and seasonal precipitation terciles as outcomes in terms of forecast quality q . Outcome = Dry

Predictor = La Niña

q

1

1

Predictor = Neutral

1

Predictor = El Niño

1

q 2

q 2

q

q

1

2

q 2

q

1

2

q

q 2

3.2. Bio-economic modelling

A forecast is usually associated with a level of uncertainty and, so, is never perfect. Therefore, any valuation methodology needs to account for the ‘fail’ outcomes of the forecast system and users need to be aware of those fail outcomes in making decisions. We adopt here a way of accounting for forecast uncertainty (Kusunose and Mahmood, 2016) by considering hindcast data of both forecasts and outcomes. Each observation can be categorised by what was forecast and what actually occurred. Using ENSO phases as predictors to forecast the likelihood of seasonal precipitation tercile outcomes (moderate precipitation conditions) as an example, all observed historical climate years over a sample period can be organised into a contingency table (Table 1). Note that though ENSO phases are employed here as predictors the developed model can be applied to any predictor of seasonal precipitation categories. In a similar manner, Table 1 can be modified to produce a contingency forecasting table of extreme wet (85th precipitation percentile) and extreme dry (15th precipitation percentile) outcomes.

3.2.1. Sugarcane growth simulation in APSIM Long-term sugarcane yield simulations were performed using the sugar module in APSIM (version 7.8; Keating et al., 2003; Holzworth et al., 2014); hereafter, APSIM-Sugar. APSIM is a point-scale, modular modelling framework. It incorporates different aspects of soil, water, nitrogen (N), crop growth and development, and their interactions within a crop-soil system driven by daily climate data. Within the sugarcane module, intercepted radiation is used to produce assimilates, which are partitioned into green leaf, green leaf sheaths, structural stalk, roots and sugar. These processes are responsive to radiation and temperature, as well as water and N supply. Pests, diseases and weeds are not simulated and it is assumed that the grower would take all reasonable steps to control these. Details of the model configuration are provided in Section 4.3. Historical daily climate data—maximum and minimum temperature, solar radiation, rainfall—for the Ayr DPI research station (Bureau of Meteorology site number 33,002) for the 1889–2015 period were obtained from the SILO database (http://www.longpaddock.qld.gov.au/ silo), developed by Jeffrey et al. (2001).

3.1. Forecast quality Forecast quality relative to other characteristics of seasonal climate forecasting systems has received the most attention in prior studies. Attention has been focused on quality because the effects of this characteristic on the economic value of climate forecasts are conceptually easy to understand. Forecast quality, however, is not a simple characteristics to describe quantitatively (Chavas and Pope, 1984). Quantitative measures of different aspects of quality—such as probability score (Murphy and Thompson, 1977), entropy (Mjelde, 1986), and variance of the forecast (Katz et al., 1987)—have been employed in previous studies. Of the many measures used to quantify forecast quality, post agreement rate is one of the most commonly used to communicate forecast uncertainty in forecast use models (Kusunose and Mahmood, 2016). Post agreement rate is the rate of correct forecasts; for example, with an El Niño predictor, the post agreement rate is defined by:

=

Outcome = Normal

forecasted outcomes (Table 2). For each of the three potential forecasts, the farmer must decide what his best response will be.

3. Model of forecast use and value assessment

post agreement rate (predictor=El Nin˜o) =

Outcome = Wet

3.2.2. Integrated bio-economic assessment We used a bio-economic model which integrated APSIM-Sugar simulations with profit functions to derive gross margins and yield relationships by systematically varying irrigation levels under different climate forecast categories, an approach similar to that developed by Power and Cacho (2014). The profit function of sugarcane represents the net return after subtracting the input cost (including water cost) from income (An-Vo et al., 2015a; An-Vo et al., 2015b) under different climate conditions, given by

P (W ) = p × Y ( W )

ph × Y (W )

C

(1)

pw × W

where P (W ) is the water-profit function and Y (W ) the associated water-yield production function under the three climate conditions, i.e. dry, normal, and wet; p is the cane price (AUD 39 t−1; adapted from Valle and Martin, 2015); ph is the harvesting cost (AUD t−1); C is the variable cost including the fertiliser cost; and pw is the water cost (AUD ML−1). All costs are presented in Table S1. The comparative cost of production in various Australian sugarcane growing regions is discussed in supplementary section SI.

correct EL Nin˜o forecasts all El Nin˜o forecasts

a11 =q a11 + a12 + a13

A forecast format parameterised by the post agreement rate (hereafter, forecast quality) q that allows for detailed investigation into the quality-value issue is employed in this study. In the case of moderate precipitation conditions forecasting, for the sake of simplicity, we assume that all three ENSO phase predictors have the same forecast quality q and that the errors are evenly distributed. We also suppose that ENSO phase information is provided and known by farmers at the beginning of each irrigation season, and that a farmer’s perception of a forecast’s accuracy is identical to the accuracy of the forecast. That is, if the farmer receives a forecast prior to an irrigation season he understands that any particular forecast will be correct q % of the time, and when it is incorrect, the odds are evenly split between the two non-

3.3. Economic value assessment If the farmer is risk neutral (a common risk preference), the assumption is that he would choose the decision that results in the highest expected profit, conditional on the forecast. For example, for a forecast f , the expected profit of any one alternative decision d is

E [profit|f = i, d] =

39

j

prob (o = j|f = i )

1 Tj

Tj k (d , k=1

j)

(2)

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Fig. 1. The Burdekin sugar cane growing region in Queensland denoted by the green area around Ayr DPI climate station (red star) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

where i {La Nin˜a, Neutral, El Nin˜o} is the predictor set and j {Wet, Normal, Dry} is the corresponding outcome set, o denotes one of the outcomes which can be a moderate or an extreme condition, Tj is the number of historical climate years belong to an outcome j , and k (d , j ) is the economic return in year k as a function of the decision in each of the outcome categories. The decision here is an irrigation water amount and the economic return in each year is a gross margin estimate based on the yield result of calibrated biophysical simulations in APSIM-Sugar. For a forecast system of moderate precipitation outcomes, as presented in Table 2, with the La Niña predictor as an example, we can have: E [profit| f = La Nin˜a,d] = q +

1

q 2

1 T Dry

T Dry k=1

1 T Wet

T Wet k= 1

k (d,Wet)

+

1

q 2

1 T Normal

T Normal k =1

optimal responses in the presence of forecasts (or ‘better’ forecasts), to that resulting from their absence (or a reference forecast). The reference value used here is an expected value of the optimal strategy based on scenarios where the farmer’s knowledge is solely the historical climatology (i.e. no forecast) or, in an equivalent situation, the farmer has access to some forecasts, but they are completely uninformative (e.g. q = 0.33 for a forecast of moderate precipitation conditions). In such a case, we assume that the farmer would have chosen an irrigation water amount d * that solves the following problem:

d * = max d

k (d ,Dry)

E [profit| d ] =

(4)

3.3.1. The expected value of the optimal strategy The expected value of the optimal strategy is the sum of the expected profits from using the best response to each forecast, weighted by the frequency of each forecast.

E [profit]* =

i

prob (f = i) E [profit|f = i, di*]

j

prob (o = j)

1 Tj

Tj k (d

, j)

k=1

Here the farmer identifies the irrigation water amount that yields, on average, the highest expected profit. It is a static strategy in that the farmer will do the same thing every plant cane or ratoon cropping season. Our reference value is the maximum profit result of the problem (6):

The farmer’s best response di* to the i forecast is DE [profit|f = i , d]

(6)

]

Where

k (d,Normal)

(3)

di* = max d

DE [profit| d

E [profit]* =

j

prob (o = j )

1 Tj

Tj k (d

*, j )

k=1

(7)

The value of the forecasts is the difference in expected profit between the optimal dynamic strategies (Eq. (4)) and the optimal static strategy (Eq. (6)).

(5)

Although it is not readily apparent in Eq. (4) above, the optimal strategy—and therefore expected profit—is very much a function of the forecast accuracy measure q, as in Eq. (3), for example.

4. A case study: the Burdekin district The Burdekin sugarcane growing district, located around Ayr in northern Queensland, Australia (Fig. 1), has a low level of annual effective rainfall (450 mm in average) and hence benefits significantly

3.3.2. The value of forecasts and increases in forecast quality The value of forecasts (or value of increases in forecast quality) are obtained by comparing the value stream resulting from the farmer’s 40

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from applied irrigation. In the Burdekin region, where good water storages are available, irrigation is used to meet sugarcane crop water requirements throughout the growing season to maintain cane yields. Information collected from interviews with selected sugarcane farmers and industry experts in the Burdekin district indicates that the current adoption and use of seasonal climate forecasts in irrigation scheduling in this region is limited.

was applied as urea at the start of each crop season at a rate of 130 kg N ha−1, following recommended practices (Thorburn et al., 2011a; Bell et al., 2014); however, it should be noted that variable N fertiliser rates (180–220 kg N ha−1) can be found across the region, with even very large amount of N fertiliser (> 300 kg ha−1) applied in some cases to minimise the chance of crop N stress (Thorburn et al., 2011a). Although irrigation from groundwater sources can supply additional amounts of N to some commercial sugarcane farms in the Burdekin (Thorburn et al., 2003; Thorburn et al., 2011a), this was not included in our simulations. Soils in most sugarcane growing areas in the Burdekin district have silty to medium clay textured surface horizons, with the sub-soil texture varying from light clay to coarse sands (Stewart et al., 2006; Thorburn et al., 2011b). A silty clay loam soil—retrieved from the APSoil database (https://www.apsim.info/Products/APSoil.aspx; Dalgliesh et al., 2012)—was used in our simulations, given its predominance in cane growing areas near the Ayr DPI climate station (Fig. 1). Standard values of soil parameters were used for the simulations (APSoil No 682; supplementary Table S2). Crop parameters for the cultivar Q117 (available in the standard APSIM-Sugar release) were used as the canopy development traits for this cultivar have been satisfactorily characterised (Keating et al., 1999). Given the default cultivars are no longer (or barely) used on farms in the region (QCANESelect™; Sugar Research Australia, 2017) and the lack of data to characterise currently grown sugarcane varieties to the level of detail required to simulate their physiology, a sensitivity analysis was performed on four parameters of the cultivar Q117. These were the fraction of accumulated biomass partitioned to cane (cane_fraction), fraction of accumulated biomass partitioned to sucrose (sucrose_fraction_stalk), minimum stem biomass before partitioning to sucrose commences (min_sstem_sucrose), and green leaf number (green_leaf_no). The intervals of plausible values were sourced from the literature (e.g., Holzworth et al., 2014; Sexton et al., 2014) (supplementary Table S3). The simulated cane yield and commercial cane sugar (CCS) content were compared to historical regional mill data (1942–2014), and the ‘modified’ cultivar with best statistical indicators (i.e., mean absolute error and root mean square error) was selected. A comparison of simulation errors using the default and ‘modified’ cultivars is provided in supplementary Figure S1. Although regional mill data encompass various management practices and different soil types, they provide a more representative data set for the calibration. The new ‘modified’ cultivar was used in the long-term simulations over the 1889–2014 period. The irrigation time and amount was set according to the readily available water (80 mm for silty clay loam soil; Sugar Research Australia, 2014) and the daily crop water use. The latter is estimated based on the daily reference evapotranspiration (ET0) and crop factors. In our simulations, an irrigation water amount of between 5 and 80 mm was applied every 10 days from planting to harvest as discussed in Section 2. APSIM v7.8 provides only runoff from rainfall not irrigation. Although some authors have approximated irrigation runoff by adding the amount of irrigation to rainfall in the climate data file on the day of irrigation (e.g., Connolly et al., 2001; Thorburn et al., 2011a), this approximation was not used since it does not capture the two-dimensional nature of the hydrology of furrow irrigation. Consequently, irrigation runoff was not simulated in our study. This study was designed using particular parameter settings within the biophysical APSIM-Sugar model which has certain limitations. A generalised approach is typically used in APSIM to model morphological and physiological adaptations by plants to water deficit (White and Snow, 2012). As such, APSIM-Sugar likely produces optimistic results. Shortcomings of the APSIM-Sugar model include the simulated responses of leaf area expansion and radiation use efficiency to transpiration efficiency, the modelling of diurnal interactions between transpiration, photosynthesis, vapour pressure deficit and water stress (Inman-Bamber et al., 2012; Inman-Bamber et al., 2016), and the non-

4.1. Seasonal water availability The Burdekin Falls Dam (Fig. 1), with a storage capacity of 1860 G L, is an extremely reliable water source and annual allocations throughout the Burdekin River Irrigation Area (BRIA) are almost always upgraded to 100% within days or weeks of the start of the water year (1 July–30 June). Surface water entitlements for licenced irrigators within the Burdekin region are 8 Ml per hectare per water year. The rules allow carryover of unused water volumes (e.g. from a wetter than normal year) to the following water year. Additional water volumes may also be purchased (temporary trades) through the water market operated by SunWater. Farmers may have access to water held in on farm water storages. Groundwater resources are also available across the catchment, though may be less reliable. Groundwater levels are monitored by the Department of Natural Resources and, in coastal cropping areas where salt water intrusion may become an issue, extraction is halted when the water table falls to threshold levels until rainfall occurs and groundwater levels are recharged. Groundwater quality can also be an issue in the region; this is monitored and blended with surface water if salt levels increase beyond a certain level. 4.2. Irrigation scheduling Burdekin sugarcane growers participating in this study generally schedule irrigation based on water deficit indices, experience and the logistics of their water delivery systems. Measurement/monitoring of soil water deficit or crop growth indices has typically resulted in irrigation on a regular 6–8 day cycle for the peak demand periods. To quote one of the growers interviewed: “You’re looking at crop requirements all the time. So you’re watering for crop requirements regardless of the type of season it’s been.” If it is a wet year, there may be very little irrigation. If dry weather is forecast, the highest priority for irrigation will be the newly planted crop, the ‘plant cane’, which has the biggest yield potential; this is followed by the early ratoons, then the late ratoons. For example, where there are limitations due to the water delivery system and there are three crop stages to be watered—the plant cane, a first and a second ratoon—watering will start with the plant cane, followed by the ratoons; if, halfway through watering the third ratoon, the plant cane or first ratoon is due for a watering again, irrigation of the older ratoon will stop and the cycle will be restarted. With a prolonged hot dry period, irrigation delivery systems may be unable to keep up with crop requirements, inducing moisture deficit; the irrigation schedule can be stretched out to 9–12 days between irrigation events until it rains. In these instances, decisions may need to be made to abandon older less productive ratoons in favour of keeping younger more productive crop stages alive and growing. Estimates from the nearby Mackay region are that, on average, irrigated sugar cane production systems use between 8 and 12 ML/ha/yr, with greater volumes used in drier than normal years to compensate for lower rainfall (Mackay Canegrowers, pers. com.). 4.3. APSIM-Sugar configurations In the model simulation, sugarcane was planted at a stalk density of 10 plants m−2 on 30 April at the beginning of each 6-year crop cycle, which included one plant crop (15 months), 4 ratoon crops (13 months each), and a fallow period between crop cycles. Nitrogen (N) fertiliser 41

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Fig. 2. APSIM-Sugar simulated fresh cane yields (bars) for a six-year crop cycles (plant cane and four ratoons) at Burdekin district, employing historical daily weather data (1889–2015) at Ayr DPI research station and an average irrigation water amount of 40 mm. Observed yields at farm level (2005–2016) and regional level (1942–2014) are also shown. Farm level data were derived from case studies conducted in the Burdekin region (this study); regional yield datasets were supplied by the Canegrowers, Australia (http://www.canegrowers.com.au/).

inclusion of weeds, pest and diseases impacts on cane yield. Full documentation on APSIM-Sugar can be found at: https://www.apsim.info/ Documentation/Model,CropandSoil/CropModuleDocumentation/ Sugar.aspx.

deficit when soil water evaporation is low, when stalk elongation has commenced and when relative humidity is high (Inman-Bamber et al., 1998; Olivier and Singels, 2015). Thus, our results highlight the importance of identifying optimal irrigation water amounts, as well as the timing of application. In economic terms, Ratoon 1 achieved the highest potential gross margins at high irrigation water amounts. Though the plant crop had highest potential yields, it incurred the cost of planting operations and thus resulted in lower gross margins than Ratoon 1. At low irrigation water amounts, similar to the yield responses, Ratoons 2, 3, and 4 achieved similar or greater potential gross margins than the plant crop or Ratoon 1. Comparing optimal irrigation water amounts for maximum yields, the optimal irrigation water amounts for maximum gross margins were likely to reduce with the age of the crop, i.e. 60 mm, 55 mm, 35 mm, 30 mm, and 27.5 mm, respectively, for plant crop, Ratoon 1, Ratoon 2, Ratoon 3, and Ratoon 4. This highlights the importance of an economic approach for sugarcane irrigation water management, especially when the cost of irrigation water is considered. Negative gross margins evident with the smallest irrigation water amounts reinforce the fact that irrigation is critical for viable sugarcane production in the Burdekin district. The biophysical and economical responses for the three climatic conditions—i.e. dry, normal, and wet—also varied for the plant crop and four ratoons (Figs. 3 and 4). As expected, strong influences of climatic conditions on yield and economic returns were observed when irrigation water amounts were limited, i.e. high yield and economic returns in wet conditions, moderate returns in normal conditions, and low returns in dry conditions. At high irrigation water amounts, the influence of climate conditions on yield and economic returns was insignificant except for Ratoon 1 whose yield and economic return in dry conditions was actually greater than those in normal and wet conditions (Figs. 3(b) & 4 (b)). Certain thresholds of irrigation water amounts were evident, beyond which the influence of climatic conditions was limited. For yield responses, these were about 60 mm, 45 mm, 43 mm, 50 mm, and 32 mm for the plant crop, Ratoon 1, Ratoon 2, Ratoon 3, and Ratoon 4, respectively. These thresholds were slightly smaller for gross margin responses, i.e. 55 mm for the plant crop, 43 mm for Ratoon 1, 42 mm for Ratoon 2, 47 mm for Ratoon 3, and 30 mm for Ratoon 4. It is also important to observe crop yield and economic responses at optimal irrigation water amounts for the three climatic conditions which enable quantification of the value of forecasts. At optimal irrigation water amounts, Ratoon 3 exhibited the most significant difference in optimal yield and optimal gross margins in the three climatic conditions, followed by Ratoon 2, Ratoon 1, Ratoon 4, and the plant crop (Fig. 3).

5. Results Crop growth was simulated in APSIM-Sugar employing a prescribed irrigation scheduling strategy, i.e. varying the irrigation water amount at each irrigation from a smallest value of 5 mm to the maximum water holding capacity of the soil type (80 mm for silty clay loam) for the entire season from planting to harvest. The calibrated crop growth simulation results were validated by comparing these to observed cane yields at farm and regional levels (Fig. 2). An increase in observed yields is apparent at the regional level from 1942 to around 1970 (Fig. 2) associated with technology and variety improvements. From 1970 onward, simulated yields capture the observed yields pretty well (Fig. 2). The root mean square error (RMSE) estimated for the period is less than 17% (˜20 t ha−1) relative to the observed regional yields. The error might result from the scale difference between regional observation and point scale simulation, and also the limitation of APSIM-Sugar in accounting for carry-over effects of irrigation (Inman-Bamber et al., 1999a). The latter can lead to an error of ˜11 t ha−1 (Inman-Bamber et al., 1999b). 5.1. Integrated bio-economic-climatic analysis Biophysical and economical responses at the full range of irrigation water amounts differed among the plant crop and four ratoons (Table 3). At high irrigation water amounts (greater than 40 mm, no water limitation), the highest potential yields were achieved with the plant crop and then Ratoon 1, Ratoon 2, Ratoon 3, and Ratoon 4, as expected. In contrast, at small irrigation water amounts less than 40 mm, Ratoons 2, 3, and 4 achieved similar or greater yield levels than those of the plant crop and Ratoon 1. While the optimal irrigation water amounts required to achieve maximum yield for the plant crop and Ratoon 1 were at the soil water holding capacity of 80 mm, the optimal water amounts for Ratoons 2, 3, and 4 were, respectively, 37.5 mm, 30 mm, and 35 mm (Table 3). Irrigation amounts greater than 40 mm did not significantly increase sugarcane yields for Ratoon 2, 3, and 4 in our simulation. Irrigation water use efficiency varies according the cultivar, plant phenology (i.e. development stage), and environmental conditions (soil properties, weather). Indeed, irrigation water use efficiency can be achieved if irrigation is applied to match the soil water 42

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Table 3 Average cane yields and gross margins for plant crop and four ratoons by a full range of irrigation water amount from 5 mm to 80 mm. Bold numbers indicate the maximum average cane yields and gross margins obtained for each crop. Irrigation water amount (mm)

5 10 12.5 15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40 42.5 45 47.5 50 55 60 65 70 75 80

Average cane yield (t ha−1)

Average gross margin (AUD ha−1)

Plant crop

Ratoon 1

Ratoon 2

Ratoon 3

Ratoon 4

Plant crop

Ratoon 1

Ratoon 2

Ratoon 3

Ratoon 4

66.3 77.1 82.2 88.7 97.9 107.7 116.5 124.5 132.0 138.8 145.1 151.0 156.2 161.3 165.8 169.6 173.2 176.1 180.8 184.0 185.7 186.3 186.6 186.8

42.3 60.0 68.9 80.4 95.2 107.6 117.4 125.5 132.8 139.2 144.7 149.4 153.4 156.8 159.9 162.6 164.9 166.8 169.2 170.6 171.2 171.4 171.6 171.7

49.9 63.2 72.7 84.1 100.8 114.1 124.7 133.6 140.5 146.1 150.0 152.2 152.9 152.3 150.8 149.1 147.2 146.0 143.8 142.4 141.3 140.3 139.6 139.0

56.4 69.9 78.7 90.1 103.0 114.4 123.3 128.6 131.6 133.0 129.2 126.9 125.7 125.5 125.6 125.8 126.1 126.4 126.6 126.7 126.6 126.3 126.1 125.9

50.5 63.4 73.0 85.0 97.7 104.6 108.7 111.6 114.5 115.2 115.7 116.0 116.0 115.9 115.7 115.6 115.5 115.3 115.2 115.0 114.9 114.7 114.7 114.6

−410 −137 −9 165 424 699 946 1166 1370 1551 1717 1868 2000 2127 2236 2324 2402 2461 2541 2573 2558 2509 2451 2388

−543 −37 216 553 995 1360 1643 1872 2074 2247 2395 2515 2613 2692 2760 2818 2862 2891 2911 2895 2856 2807 2754 2697

−331 32 303 636 1136 1529 1836 2091 2281 2428 2521 2561 2556 2507 2429 2344 2255 2184 2056 1953 1855 1765 1684 1604

−95 275 527 862 1243 1577 1829 1969 2037 2050 1902 1798 1732 1696 1669 1646 1628 1606 1557 1500 1438 1373 1306 1242

−282 68 344 696 1070 1259 1360 1425 1485 1478 1466 1447 1416 1383 1347 1315 1281 1247 1183 1119 1054 992 930 870

benefit of AUD 5.2 ha–1; from 80% to 90%, AUD 6.5 ha–1; and from 90% to 100%, AUD 7.6 ha–1. Note that this forecast benefit was achieved with a mix of both correct and incorrect (imperfect) forecasts and average economic values for the three moderate forecast categories. With a perfect forecast system of moderate precipitation conditions, we calculate that canegrowers can potentially gain up to an average of AUD 27 ha–1per annum. For moderate wet and dry condition forecasts, the economic gains can be up to AUD 40 and 43 ha–1 per annum, respectively, while much higher economic gains can be achieved with forecasts of extreme wet or dry conditions—AUD 260 and 150 ha–1 per annum, respectively (Fig. 6).

5.2. Seasonal climate forecast value The developed bio-economic framework, by employing the parameterised forecast structure, can quantify economic values across a full and continuous range of forecast skill levels. This not only allows assessment of the economic impacts of current seasonal climate systems but also of improved forecast skill. Forecast value was obtained for a full range of forecast quality from no skill to perfect skill. For moderate precipitation forecasting, it can be seen that the forecast system average value continuously increases with improved forecast quality (Fig. 5). Interestingly, for sugarcane, the forecast value differs among the plant crop and ratoons. Using the expected profit approach (Section 3.3), the difference in optimal gross margins in the three climatic conditions drives the values of forecasts, resulting in highest average forecast values for Ratoon 3 (up to AUD 27 ha–1 per annum for a perfect forecast), followed by Ratoon 2 (up to AUD 13 ha–1per annum), Ratoon 1 (up to AUD 12 ha–1per annum), Ratoon 4 (up to AUD 10 ha–1per annum) and the plant crop (up to AUD 3 ha–1per annum). Forecasting for moderate wet or dry conditions results in higher economic values than that of forecasting for moderate neutral condition, which are up to AUD 40 and 43 ha–1 per annum, respectively (Fig. 6). Importantly, at the current SCF skill of around 60% for the Burdekin region (derived by correlating the ENSO signal and historical climate data), an ENSO phases forecast of moderate rainfall conditions can deliver an average annual gain of up to AUD 4.5 ha–1 (Fig. 5). Annual gains with forecasts of moderate wet or dry conditions can be higher at AUD 6.2 and 7.1 ha−1 per annum, respectively, for a Ratoon 3 crop (Fig. 6). At a similar forecast skill level of 60%, forecasting of extreme wet (85th precipitation percentile) or extreme dry (15th precipitation percentile) conditions can achieve much higher values of AUD 92 and 43 ha−1 per annum, respectively (Fig. 6). Improvements in forecast quality would result in more economic value gained by farmers; for example, in the moderate precipitation forecasting system, a 10% improvement in forecast skill would result in, on average, up to an additional AUD 7.6 ha–1 per annum (Table 4). For Ratoon 3, by 10% increments, ongoing improvement in forecast accuracy from 70% to 80% might achieve an additional average annual

5.3. Implication for improved irrigation water management decisions The present integrated model of forecast use allows cane growers to make decisions about seasonal irrigation water amounts when they receive a forecast. A cane grower’s decisions are dynamic, depending not only on the received forecast but also the cane grower’s perception of forecast quality levels and the stage of the crop cycle (Fig. 7). Generally, the model suggests highest optimal irrigation water amounts for the plant crop, followed by Ratoon 1, Ratoon 2, Ratoon 3, then Ratoon 4. Optimal irrigation water amounts were more sensitive to forecast quality for the La Niña and El Niño forecasts (Figs. 7(a) & (c)) than Neutral forecasts (Fig. 7(b)). Interestingly, while optimal irrigation water amounts reduced with improved quality of the La Niña forecasts, better El Niño forecasts would require higher optimal irrigation amounts. This is reasonable considering that, if farmers are more certain about a wet seasonal climatic condition, they would likely reduce irrigation water to avoid problems associated with overwatering, such as waterlogging which reduces yield and economic returns. On the other hand, if farmers are more certain about dry climatic conditions, they would likely increase irrigation water to optimise yield and economic returns. It can be seen that by improving forecast quality and cane growers’ perceptions, irrigation water resources can be more effectively used. This supports the usefulness of seasonal climate forecasts, even in fully irrigated enterprises such those in the Burdekin district, which do not depend on uncertain seasonal precipitation. 43

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Fig. 3. Simulated average cane yields in the Burdekin district, Australia, employing historical climate data form the Ayr DPI research station from 1889 to 2015, as a function of irrigation water amounts. Average cane yields were plotted for the plant crop and four ratoons in dry, normal, and wet climate conditions. Irrigation water amounts range from 5 mm to 80 mm (readily available water capacity of the silty clay loam soil).

6. Discussion

significant economic values associated with forecasts of wet and dry conditions, especially where extreme wet and/or dry conditions were predicted. This implies that growers can also potentially gain additional profitability by using SCFs in other sugarcane irrigation environments, for instances supplementary irrigation environments. In instances other than for improved irrigation management, the potential impacts of seasonal climate on sugarcane production have been investigated in various countries including but not limited to India (e.g. Subbaramayya and Kumar, 1980), United States (e.g. Hansen et al., 1998; Greenland, 2005), South Africa (e.g. Singels and Bezuidenhout, 1999; Bezuidenhout and Schulze, 2006; Jones and Singels, 2015), Nigeria (e.g. Binbol et al., 2006), Trinidad and Tobago (e.g. Pulwarty and Eischeid, 2001), Brazil (e.g. Pagani et al., 2017), and Australia (e.g. Everingham et al., 2002, 2003, 2008, 2009, [Everingham et al., 2012] 2012, 2016; Thorburn et al., 2017). These works represent a range of SCF applications which might be solely yield/production variability analysis (Subbaramayya and Kumar, 1980; Hansen et al., 1998; Singels and Bezuidenhout, 1999; Pulwarty and Eischeid, 2001; Greenland, 2005; Binbol et al., 2006; Jones and Singels, 2015), yield forecasting (Everingham et al., 2003, 2009, 2016; Bezuidenhout and Schulze, 2006; Pagani et al., 2017), water allocation forecasting

6.1. Seasonal climate and sugarcane industry Sugarcane production systems worldwide are exposed to uncertainty associated with weather and seasonal climate variation. This variability often impacts negatively on crop production, leading to conservative farming strategies that sacrifice productivity in order to reduce the risk of losses in poor years (Jones et al., 2000). The situation is especially severe in Australia where sugarcane production is subject to an extremely variable climate (Muchow et al., 1997). Inter-annual climate variability in Australia is about 15–18% higher than any other major agricultural nation (Cleugh et al., 2011; Walker and Mason, 2015). In such an extremely variable environment, SCFs can play an important role in supporting agricultural risk management, and their use in decision making may potentially add significant profitability. The present study is the first attempt to demonstrate the usefulness of SCFs in sugarcane seasonal irrigation decision making by an integrated and consistent simulation-optimisation framework. While the developed framework was applied to a full irrigation case study exhibiting marginal dependence on seasonal precipitation, it still resulted in 44

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Fig. 4. Estimated average gross margins in the Burdekin district, Australia, using simulated average cane yields (Fig. 3), as a function of irrigation water amounts. Average gross margins were plotted for the plant crop and four ratoons in dry, normal, and wet climate conditions. Irrigation water amounts range from 5 mm to 80 mm (readily available water capacity of the silty clay loam soil).

(Everingham et al., 2008) or nitrogen management (Thorburn et al., 2017). More systematic approaches have also been developed to improve risk management and decision-making capability across the whole industry value chain for decisions relating to yield forecasting (Everingham et al., 2002) and harvest management (Everingham et al., 2002, 2012). These works all suggest the potentially useful application of SCFs. Their actual adoption is, however, limited.

to understand. Unless uncertainty is explicitly and realistically incorporated into models of forecast use, the gap between expectations of forecast use and actual adoption by farmers will continue (Kusunose and Mahmood, 2016). The present study has demonstrated quantifiable and consistent economic value of SCFs in irrigation management decision-making. There is no direct comparison for the value of seasonal forecasts derived from our study, given the lack of similar valuation studies. However, Osborne et al. (2011) estimated a similarly modest economic value (ranging from AUD 0–61 ha–1 per annum) of SCFs for decisions on the scheduling of the annual sugar cane harvest start date. Likewise, Higgins and Muchow (2003) calculated potential gains of up to AUD 157 ha–1 due to the use of SCFs in alternative supply decision making. Here, we have only estimated the economic value of irrigation water management decisions. However, the framework we have developed is generic and can readily be applied to other key decisions within sugar cane production systems (for example, planting, harvest and fertiliser management) and/or other crop production systems which, we believe, will likely identify greater economic value. As demonstrated in this work, the economic value of seasonal

6.2. Adoption problem It is noteworthy that SCFs are largely underutilised by farmers in making key farming management decisions, not only in sugarcane farming systems, even though the literature (e.g. McIntosh et al., 2007; Meza et al., 2008; Klemm and McPherson, 2017) implies that, in many cases, they could significantly benefit from forecasts. This is attributed to farmers’ perception that SCFs are far from certain, despite significant advances in their ability over recent decades (e.g. Kirtman and Pirani, 2009; Doblas-Reyes et al., 2013; Bramley and Ouzman, 2018). Forecast uncertainty is hindering the effective communication of seasonal climate forecast to users who also typically find probability forecasts hard 45

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Table 4 Average economic benefits of improving forecast quality in 10% quality increment steps for the moderate precipitation forecasting system. Forecast quality (q )

0.4 0.5 0.6 0.7 0.8 0.9

– – – – – –

0.5 0.6 0.7 0.8 0.9 1.0

Economic benefit (AUD ha−1) Plant crop

Ratoon 1

Ratoon 2

Ratoon 3

Ratoon 4

0.13 0.23 0.34 0.63 0.68 0.95

0.69 1.19 1.61 2.30 2.66 3.44

0.79 1.26 1.85 2.63 3.03 3.98

1.49 2.77 3.91 5.20 6.47 7.58

0.48 1.06 1.36 1.92 2.29 2.70

climate forecasts will depend on the impact of seasonal climate variability on farm profitability and the decision analysed. The usefulness of our developed framework does not lie in the derived economic values but in its potential as a model for forecast use to more effectively communicate the value of seasonal climate forecasts to users and reduce the gap between expected use of skilful forecasts and actual adoption. The way this model of forecast use might be implemented and funded is still unclear but we foresee at least two alternative options. The first is providing SCF-based simulation services through public institutions (government, regional authorities, forecast providers), establishing an open interactive decision support system for all interested users with a community of users for learning and significant feedback. The other option involves integration with existing agricultural decision support platforms (Yield Prophet or ARM online as examples in Australia) and a subscription service where users contribute financially to the provided service and its further development and maintenance. Such a conventional business and marketing strategy can be a suitable option here because the economic values of the service are readily revealed.

Fig. 5. The expected value of average forecasts (moderate precipitation conditions) as a function of forecast quality for the plant crop and four ratoons in the Burdekin district, Australia.

Fig. 6. Forecast value as a function of forecast quality for Ratoon 3 with forecasting of dry (a) and wet (b) conditions. We show here forecast value ranges (shaded areas) covered by moderate conditions (continuous lines) and extreme conditions (at 15th and 85th precipitation percentiles, dashed lines). 46

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terms of their productivity and profitability while also reducing their exposure to financial and broader environmental risk under variable climatic conditions. Acknowledgments This research is funded through the Queensland Government’s Drought and Climate Adaptation Program (DCAP) and University of Southern Queensland’s strategic research program. We also gratefully acknowledge the contribution of the Burdekin sugarcane growers who participated in the case study interviews and generously provided information about their production systems and cropping and irrigation decision- making. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.eja.2019.01.005. References An-Vo, D.-A., Mushtaq, S., Nguyen- Ky, T., Bundschuh, J., Tran-Cong, T., Maraseni, T., Reardon-Smith, K., 2015a. Nonlinear optimisation using production functions to estimate economic benefit of conjunctive water use for multicrop production. Water Resour. Manag. 29 (7), 2153–2170. An-Vo, D.-A., Mushtaq, S., Reardon- Smith, K., 2015b. Estimating the value of conjunctive water use at a system-level using nonlinear programing model. J. Econ. Soc. Policy 17 (2), 9. An-Vo, D.-A., Mushtaq, S., Reardon- Smith, K., 2017. Modelling irrigated sugarcane crop under seasonal climate variability: a case study in Burdekin district. Paper Presented at the Proceedings of the 22nd International Congress on Modelling and Simulation. (MODSIM 2017). Bell, M.J., Moody, P., Salter, B., Connellan, J., Garside, A.L., 2014. Agronomy and physiology of nitrogen use in Australian sugarcane crops. In: Bell, M.J. (Ed.), A Review of Nitrogen use Efficiency in Sugarcane. Sugar Research Australia (SRA) Research Report, Indooroopilly Queensland, Australia, pp. pp 89–124. Available at. https:// sugarresearch.com.au/wp-content/uploads/2017/05/A-review-of-nitrogen-useefficiency-in-sugarcane.pdf (Accessed October 2018). Bezuidenhout, C., Schulze, R., 2006. Application of seasonal climate outlooks to forecast sugarcane production in South Africa. Clim. Res. 30 (3), 239–246. Binbol, N., Adebayo, A., Kwon-Ndung, E., 2006. Influence of climatic factors on the growth and yield of sugar cane at Numan, Nigeria. Clim. Res. 32 (3), 247–252. Bramley, R.G.V., Ouzman, J., 2018. Farmer attitudes to the use of sensors and automation in fertilizer decision-making: nitrogen fertilization in the Australian grains sector. Precis. Agric. 1–19. https://doi.org/10.1007/s11119-018-9589-y. Brown, P.D., Cochrane, T.A., Krom, T.D., 2010. Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing. Agric. Water Manag. 97 (6), 892–900. Cai, X., Hejazi, M.I., Wang, D., 2010. Value of probabilistic weather forecasts: assessment by real-time optimization of irrigation scheduling. J. Water Resour. Plan. Manag. 137 (5), 391–403. Chavas, J.-P., Pope, R.D., 1984. Information: its measurement and valuation. Am. J. Agric. Econ. 66 (5), 705–710. Cleugh, H., Cleugh, H., Smith, M.S., Battaglia, M., Graham, P., 2011. Climate Change: Science and Solutions for. CSIRO, Australia. Connolly, R.D., Kennedy, I.R., Silburn, D.M., Simpson, B.W., Freebairn, D.M., 2001. Simulating endosulfan transport in runoff from cotton fields in Australia with the GLEAMS model. J. Environ. Qual. 30, 702–713. Dalgliesh, N.P., Cocks, B., Horan, H., 2012. APSoil-providing soils information to consultants, farmers and researchers. Yunusa, I. (Ed.), 16th Australian Agronomy Conference 2012. Doblas‐Reyes, F.J., García‐Serrano, J., Lienert, F., Biescas, A.P., Rodrigues, L.R., 2013. Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip. Rev. Clim. Change 4 (4), 245–268. Everingham, Y., Muchow, R., Stone, R., Inman- Bamber, N., Singels, A., Bezuidenhout, C., 2002. Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts. Agric. Syst. 74 (3), 459–477. Everingham, Y., Muchow, R., Stone, R., Coomans, D., 2003. Using southern oscillation index phases to forecast sugarcane yields: a case study for northeastern Australia. Int. J. Climatol. 23 (10), 1211–1218. Everingham, Y., Baillie, C., Inman-Bamber, G., Baillie, J., 2008. Forecasting water allocations for Bundaberg sugarcane farmers. Clim. Res. 36 (3), 231–239. Everingham, Y., Smyth, C., Inman-Bamber, N., 2009. Ensemble data mining approaches to forecast regional sugarcane crop production. Agric. For. Meteorol. 149 (3-4), 689–696. Everingham, Y.L., Stoeckl, N.E., Cusack, J., Osborne, J.A., 2012. Quantifying the benefits of a long‐lead ENSO prediction model to enhance harvest management—a case study for the Herbert sugarcane growing region, Australia. Int. J. Climatol. 32 (7),

Fig. 7. Optimal irrigation amount as a function of forecast quality for the simulated sugar cane plant crop and four ratoons in the Burdekin district, Australia. The optimal irrigation amount also depends on a specific forecast received by canegrowers which can be (a) a La Niña forecast, (b) a Neutral forecast, or (c) an El Niño forecast. Note that in (b), results of Ratoon 3 were overlapped by those of Ratoon 4.

7. Synopsis Desired levels of forecast reliability, use and understanding of seasonal terminologies, short lead times, and perceptions regarding seasonal climate information have previously been identified as factors limiting the uptake and use of seasonal climate forecasts. Moreover, insufficient evidence about the value of climate forecasts has also long been considered to be a major factor limiting adoption in key decision making in agricultural industries. Although many farmers and advisers continue to point to the need to improve the accuracy of seasonal climate forecasts, it is apparent from this study that there is substantial unrealised potential in existing SCF information that offers considerable economic value. Our research has shown that, properly interpreted, existing climate forecasts, even with current less than perfect levels of forecasting skill, offer considerable potential to increase long-term average yield and decreased production risks, with investment losses minimised in poor years and opportunities maximised in good years. We conclude, based on the results of this analysis, that improved seasonal forecasts, together with better communication, understanding and capacity to use these in decision-making, will benefit farmers in 47

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research. J. Appl. Meteorol. Climatol. 47 (5), 1269–1286. Mishra, A., Siderius, C., Aberson, K., Van der Ploeg, M., Froebrich, J., 2013. Short-term rainfall forecasts as a soft adaptation to climate change in irrigation management in North-East India. Agric. Water Manag. 127, 97–106. Mjelde, J.W., 1986. Dynamic Programming Model of the Corn Production Decision Process with Stochastic Climate Forecasts. Retrieved from. . Muchow, R., Robertson, M., Keating, B., 1997. Limits to the Australian Sugar Industry: Climatic and Biological Factors. Murphy, A.H., Thompson, J.C., 1977. On the nature of the nonexistence of ordinal relationships between measures of the accuracy and value of probability forecasts: an example. J. Appl. Meteorol. 16 (10), 1015–1021. Mushtaq, S., Chen, C., Hafeez, M., Maroulis, J., Gabriel, H., 2012. The economics value of improved agrometeorological information to irrigators amid climate variability. Int. J. Climatol. 32 (4), 567–581. Olivier, F.C., Singels, A., 2015. Increasing water use efficiency of irrigated sugarcane production in South Africa through better agronomic practices. Field Crops Res. 176, 87–98. Osborne, J., 2011. Seasonal Climate Forecasting for Sugar Cane: an Economic Investigation of Harvest Management. James Cook University. Pagani, V., Stella, T., Guarneri, T., Finotto, G., van den Berg, M., Marin, F.R., et al., 2017. Forecasting sugarcane yields using agro-climatic indicators and Canegro model: a case study in the main production region in Brazil. Agric. Syst. 154, 45–52. Perera, K.C., Western, A.W., Nawarathna, B., George, B., 2014. Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs. Agric. For. Meteorol. 194, 50–63. Power, B., Cacho, O.J., 2014. Identifying risk-efficient strategies using stochastic frontier analysis and simulation: an application to irrigated cropping in Australia. Agric. Syst. 125, 23–32. Pulwarty, R., Eischeid, J., 2001. The impact of El nino-Southern oscillation events on rainfall and sugar production in Trinidad. Paper Presented at the Proceedings of the West Indies Sugar Technologists Conference. Sexton, J., Basnayake, J., Everingham, Y., Inman- Bamber, G., Lakshmanan, P., Jackson, P., 2014. Detailed trait characterisation Is needed for simulation of cultivar responses to Water stress. Proceedings of the 36th Conference of the Australian Society of Sugar Cane Technologists, Gold Coast, 29 April-1 May 2014. pp. pp 82–92. Singels, A., Bezuidenhout, C., 1999. The relationship between ENSO and rainfall and yield in the South African sugar industry. South Afr. J. Plant Soil 16 (2), 96–101. Stewart, L.K., Charlesworth, P.B., Bristow, K.L., Thorburn, P.J., 2006. Estimating deep drainage and nitrate leaching from the root zone under sugarcane using APSIMSWIM. Agric. Water Manag. 81, 315–334. Stone, R., Auliciems, A., 1992. SOI phase relationships with rainfall in eastern Australia. Int. J. Climatol. 12 (6), 625–636. Stone, R.C., Hammer, G.L., Marcussen, T., 1996. Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384 (6606), 252–255. Subbaramayya, I., Kumar, K.R., 1980. Crop—weather relationships of sugarcane and yield prediction in Northeast Andhra Pradesh, India. Agric. Meteorol. 21 (4), 265–279. Sugar Research Australia, 2017. Variety Guides 2016-17. http://www.sugarresearch. com.au/page/Growing_cane/Varieties/Publications/. Sunantara, B.J.D., Ramírez, J.A., 1997. Optimal stochastic multicrop seasonal and intraseasonal irrigation control. J. Water Resour. Plan. Manag. 123 (1), 39–48. Thorburn, P.J., Biggs, J.S., Weier, K.L., Keating, B.A., 2003. Nitrate in groundwaters of intensive agricultural areas in coastal Northeastern Australia. Agric. Ecosyst. Environ. 94, 49–58. https://doi.org/10.1016/S0167-8809(02)00018-X. Thorburn, P.J., Biggs, J.S., Webster, A.J., Biggs, I.M., 2011a. An improved way to determine nitrogen fertiliser requirements of sugarcane crops to meet global environmental challenges. Plant Soil 339, 51–67. Thorburn, P.J., Biggs, J.S., Attard, S.J., Kemei, J., 2011b. Environmental impacts of irrigated sugarcane production: nitrogen lost through runoff and leaching. Agric. Ecosyst. Environ. 144, 1–12. Thorburn, P.J., Biggs, J.S., Palmer, J., Meier, E.A., Verburg, K., Skocaj, D.M., 2017. Prioritizing crop management to increase nitrogen use efficiency in Australian sugarcane crops. Front. Plant Sci. 8, 1504. Valle, H., Martin, P., 2015. Australian sugarcane farm businesses: financial performance, 2013-14. Research report by ABARES. Walker, R., Mason, W., 2015. Climate Change Adaptation for Health and Social Services. CSIRO PUBLISHING. Wang, D., Cai, X., 2009. Irrigation scheduling—role of weather forecasting and farmers’ behavior. J. Water Resour. Plan. Manag. 135 (5), 364–372. White, T.A., Snow, V.O., 2012. A modelling analysis to identify plant traits for enhanced water-use efficiency of pasture. Crop Pasture Sci. 63, 63–76. Wilks, D., Wolfe, D., 1998. Optimal use and economic value of weather forecasts for lettuce irrigation in a humid climate. Agric. For. Meteorol. 89 (2), 115–129. Yang, Y., Cui, Y., Luo, Y., Lyu, X., Traore, S., Khan, S., Wang, W., 2016. Short-term forecasting of daily reference evapotranspiration using the Penman-Monteith model and public weather forecasts. Agric. Water Manag. 177, 329–339. Zhang, Q., Cui, N., Feng, Y., Gong, D., Hu, X., 2018. Improvement of Makkink model for reference evapotranspiration estimation using temperature data in Northwest China. Journal of Hydrology 566, 264–273.

1069–1076. Everingham, Y., Sexton, J., Skocaj, D., Inman-Bamber, G., 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36 (2), 1–9. Greenland, D., 2005. Climate variability and sugarcane yield in Louisiana. J. Appl. Meteorol. 44 (11), 1655–1666. Hansen, J.W., Irmak, A., Jones, J.W., 1998. El Niño-southern oscillation influences on Florida crop yields. Paper Presented at the Proceedings-Soil and Crop Science Society of Florida. Hejazi, M.I., Cai, X., Yuan, X., Liang, X.-Z., Kumar, P., 2013. Incorporating reanalysisbased short-term forecasts from a regional climate model in an irrigation scheduling optimization problem. J. Water Resour. Plan. Manag. 140 (5), 699–713. Higgins, J.A., Muchow, C.R., 2003. Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry. Agric. Syst. 76 (2), 623–638. Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., et al., 2014. APSIM – evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350. Inman-Bamber, N.G., Singels, A., Muchow, R.C., 1998. A systems approach to benchmarking for sugarcane production in Australia and South Africa. Proceedings of the South African Sugar Technologists’ Association 72, 3–9. Inman-Bamber, N., Robertson, M., Muchow, R., Wood, A., Pace, R., Spillman, A., 1999a. Boosting yields with limited irrigation water. Paper Presented at the ProceedingsAustralian Society of Sugar Cane Technologists. Inman-Bamber, N., Schuurs, M., Muchow, R., 1999b. Advances in the science and economics of supplementary irrigation of sugarcane. Inman-Bamber, N., Baillie, C., Willcox, J., 2002. In: Tools for improving efficiency of limited water use in sugarcane. Paper Presented at the Proc. Irrigation Assoc. Austr., National Conference. Inman-Bamber, N., Attard, S., Baillie, C., Lawson, D., Simpson, L., 2005. A web-based system for planning use of limited irrigation water in sugarcane. Paper Presented at the Proceedings of the Australian Society of Sugar Cane Technologists. Inman-Bamber, N., Attard, S., Verrall, S., Webb, W., Baillie, C., 2007. A web-based system for scheduling irrigation in sugarcane. Paper Presented at the Proceedings of the International Society of Sugar Cane Technologists. Inman-Bamber, N., Lakshmanan, P., Park, S., 2012. Sugarcane for water-limited environments: Theoretical assessment of suitable traits. Field Crops Res. 134, 95–104. Inman-Bamber, N., Jackson, P.A., Stokes, C.J., Verrall, S., Lakshmanan, P., Basnayake, J., 2016. Sugarcane for water-limited environments: Enhanced capacity of the APSIM sugarcane model for assessing traits for transpiration efficiency and root water supply. Field Crops Res. 196, 112–123. Jamal, A., 2016. Utilizing Probabilistic Weather Forecasting for Optimal Irrigation Scheduling. Master dissertation. Technion–Israel Institute of Technology. Jamal, A., Linker, R., Housh, M., 2018. Comparison of various stochastic approaches for irrigation scheduling using seasonal climate forecasts. J. Water Resour. Plan. Manag. 144 (7) 04018028. Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16 (4), 309–330. https://doi.org/10.1016/S1364-8152(01)00008-1. Jones, M., Singels, A., 2015. Analysing yield trends in the South African sugar industry. Agric. Syst. 141, 24–35. Jones, J.W., Hansen, J.W., Royce, F.S., Messina, C.D., 2000. Potential benefits of climate forecasting to agriculture. Agric. Ecosyst. Environ. 82 (1), 169–184. Katz, R.W., Brown, B.G., Murphy, A.H., 1987. Decision‐analytic assessment of the economic value of weather forecasts: the fallowing/planting problem. J. Forecast. 6 (2), 77–89. Keating, B.A., Robertson, M.J., Muchow, R.C., Huth, N.I., 1999. Modelling sugarcane production systems I. Development and performance of the sugarcane module. Field Crops Res. 61 (3), 253–271. https://doi.org/10.1016/S0378-4290(98)00167-1. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., et al., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18 (3-4), 267–288. https://doi.org/10.1016/s11610301(02)00108-9. Kirtman, B., Pirani, A., 2009. The state of the art of seasonal prediction: outcomes and recommendations from the first world climate research program workshop on seasonal prediction. Bull. Am. Meteorol. Soc. 90 (4), 455–458. Klemm, T., McPherson, R.A., 2017. The development of seasonal climate forecasting for agricultural producers. Agric. For. Meteorol. 232, 384–399. Kusunose, Y., Mahmood, R., 2016. Imperfect forecasts and decision making in agriculture. Agric. Syst. 146, 103–110. Li, D., Chen, J., Luo, Y., Liu, F., Luo, H., Xie, H., Cui, Y., 2018. Short- term daily forecasting of crop evapotranspiration of rice using public weather forecasts. Paddy Water Environ. 16 (3), 397–410. Liu, M., Luo, Y., Wang, W., He, J., Cui, Y., 2017. Comparison of three reference crop evapotranspiration forecasting methods based on short-term weather forecast in Zhanghe irrigation district. Trans. Chin. Soc. Agric. Eng. 33 (19), 156–162. McIntosh, P.C., Pook, M.J., Risbey, J.S., Lisson, S.N., Rebbeck, M., 2007. Seasonal climate forecasts for agriculture: towards better understanding and value. Field Crops Res. 104 (1), 130–138. Meza, F.J., Hansen, J.W., Osgood, D., 2008. Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future

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