https://orcid.org/0000-0001-7528-7139
Figures
Abstract
Improving water use efficiency (WUE) in agriculture is crucial, given the escalating scarcity of water resources. This study aims to maximise the economic value of irrigation water at the farm level – farming gross margin over the total irrigation water including costs, a concept focusing on economic efficiency rather than the conventional biophysical WUE. We propose an integrated simulation-optimisation analysis that links crop growth responses from simulation by a calibrated APSIM-Sugar model with a novel bio-economic model for optimal irrigation scheduling under variable climatic conditions, maximising cane yield, farming profitability and the economic value of irrigation water. A working example is conducted in the sugarcane industry of the Burdekin district, Queensland, the largest irrigated cane-growing region in Australia. Our findings show that a more frequent irrigation schedule (Schedule 1) improved model-simulated potential cane yield and profitability by reducing water stress, without considering the extra cost of higher irrigation frequency. A consistent water supply enhances nutrient uptake and growth, resulting in higher potential yields (30 t ha−1 per season (14%)) and profitability (up to AUD 500 ha−1 per season) than those of a less frequent schedule (Schedule 2). Maximum economic water value (EWV) of AUD 258 ML−1 is achieved in a wet season due to enhanced moisture availability/air humidity and reduced irrigation demand, while better EWVs are achieved by ratoons in all seasonal climate conditions. An optimal irrigation schedule adapting to each seasonal climate condition is recommended for improved irrigation management. Overall, we recommend a 4-steps practical irrigation decision framework for each cropping season: (1) classify the wet/normal/dry upcoming season, (2) select specific optimal water amount (irrigation depth) and interval based on the present proposed method, (3) estimate the total seasonal irrigation water needed in comparison with water allocation/availability, and (4) compare the EWV with water market price to decide buying/selling.
Citation: Ababneh S, An-Vo D-A, Gillies M, Kouadio L, Mushtaq S, Scobie M (2026) Maximising the economic value of water through adaptive, climate-informed irrigation scheduling. PLOS Clim 5(7): e0000696. https://doi.org/10.1371/journal.pclm.0000696
Editor: Zhipin Ai, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, CHINA
Received: July 21, 2025; Accepted: June 1, 2026; Published: July 9, 2026
Copyright: © 2026 Ababneh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data and codes will be made available in GitHub at https://github.com/AgReFed/crop-modelling-notebooks/tree/main/Irrigation.
Funding: This work was supported by the University of Southern Queensland (UniSQ) International Fee Research Scholarship (to Suhaib Ababneh) and Queensland Drought and Climate Adaptation Program (Grant Number: USQ15 to Shahbaz Mushtaq). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
Water availability is crucial for socio-economic development, sustainability, and food production given the global population is steadily growing with no expectation for new water resources. Water allocation for food production, however, often has a low priority compared with other sectors, such as domestic consumption or environmental services, because water policies typically prioritise human consumption and ecosystem sustainability (e.g., [1]). This global situation is creating an urgency for crop production optimisation to enhance food supply despite the constraints posed by limited natural resources and climate change [2, 3]. Particularly, the resource use efficiency needs to be maximised by reducing water use to produce crops over the existing land and available water resources. The need to improve irrigation practices is essential to enhance water use efficiency for food security, as the global food demand is expected to increase 50–60% by 2050 [4], given the climate change with projected increased climate variability and extremes [5]. Solutions that help achieve the best farming productivity, profitability, and especially economic outcome per unit of water use in agriculture are thus desirable.
Water is a key requirement for plant growth; in many instances it is the most limiting factor for agricultural production. Irrigated agriculture is complicated by the extent of water availability (rainfall or other water sources, including reservoirs) and the plant’s water requirement [6] including evapotranspiration and soil moisture. The plant’s water usage should be determined beforehand, based on crop growth and evapotranspiration data; however, the actual water use often varies with soil moisture dynamics, and rainfall variability. Irrigation interventions such as modifying the timing and volume of water applications should therefore be established to replace the amount of soil water that the plant utilises. The plant water shortage happens when the seasonal climate cannot guarantee optimal availability of water from rainfall, directly impacting crop yield [7]. An-Vo et al. [8] assert that seasonal water planning based on irrigation scheduling optimisation is inevitable to ensure water use is minimised and the yield maximised, driving better farming gross margin (profitability). Irrigation scheduling optimisation entails multiple practices, including optimal timing of irrigation practices and the extent of irrigation water that should be adopted to attain maximum yield.
The transient amount of water a farmer applies to crops depends on water availability, rainfall amount, and crop water demand over time. According to Turral et al. [9], in the short term, climate variability can cause extreme events such as heavy rainfall and flooding that cause water logging, or high temperatures that increase soil evaporation. These events have a direct impact on crops including irrigation demands, soil moisture levels, and evapotranspiration rates. In the long term, small changes in temperature and rainfall patterns can increase the frequency and intensity of extreme events, affecting the overall irrigation requirements. Modelling consistently shows double-digit percentage increases in irrigation requirements per degree of warming [10]. Water use thus needs to be optimised to achieve the best production and/or economic outcomes with increasing scarcity of irrigation water, given various short-term and long-term factors that have a direct impact on irrigation scheduling and the amount of water that is needed for irrigation.
Simulation-optimisation approaches are useful in determining optimal irrigation schedules that should be adopted at any given time [11]. The optimisation process, however, faces computational challenges since the optimisation problem must be solved repeatedly [8], using crop models with the associated limitations: (i) the need for site-specific calibration, (ii) uncertainty in model parameters, and (iii) the computational cost for simulations. Nevertheless, there has been a progressive development of the advanced models used in irrigation scheduling optimisation [8], which employ comprehensive crop simulation models such as Agricultural Production Systems Simulator (APSIM) and Soil Water Atmosphere Plant (SWAP) in simulation-optimisation frameworks to support real-time irrigation decisions [12]. APSIM-Sugar is one of the most widely used mechanistic models in global sugarcane research [13, 14, 15], while SWAP is often paired with crop models to simulate detailed soil water and solute transport in sugarcane landscapes including applications in climate-smart irrigation strategies (e.g., [16, 17]). In this study, we selected APSIM-Sugar due to its specific design for sugarcane and comprehensive validation under Australian climatic, soil, and management conditions, enabling reliable simulation of cane growth, water use, and yield response to irrigation. Linking biophysical processes by APSIM-Sugar with agricultural economic outcome – a bio-economic approach, aligning with integrated simulation-optimisation methods that aim to enhance water value and profitability [18] is essential for efficient water allocation and irrigation management. We aimed to investigate water use efficiency (WUE) in terms of economic water value (EWV) – gross margin/profit per unit of farmland divided by total irrigation water used per that unit of land, a concept that focuses on the economic efficiency of irrigation water rather than the conventional biophysical WUE (biophysical yield divided by evapotranspiration).
The sugarcane industry plays an essential role in Australia’s agriculture sector with 3,200 farms, 150,000 harvested hectares and 24 mills, crushing 35 million tons (t) of cane, and producing 4.7 million t of sugar with 3.9 million t (82%) exported, earning a total of AUD 2.2 billion [19]. However, the economic performance of sugarcane is highly dependent on irrigation water management. There is pressure to increase water use efficiency in the Australian sugar industry while enhancing the sugarcane yield and quality at a lower cost, given the limited water resources. The availability and demand for water directly impact sugarcane yield [20]. For example, under optimal temperature and sunlight conditions, sugarcane growth depends on the quantity of water available, including soil water supplied by irrigation and rainfall. It has been demonstrated that for each 100 mm of soil water applied to the crop, an additional yield of 10 t ha−1 of cane is achieved. However, highly effective irrigating practices could result in up to an additional 20 t ha−1, employing the same volume of water [21]. Irrigators, thus, should always use the best practices to minimise the cost of production and maximise the production outcomes and/or profit.
Sugarcane farmers rarely rely on weather and climate factors to make irrigation decisions, leading to suboptimal practices that negatively affect yield and income. Instead, decisions are commonly based on short-term weather observations, recent rainfall history and visual assessment of crop water stress. Knowledge of the coming seasonal climate conditions, however, is essential for decision-making, as they enhance the optimal use of irrigation water and improve water management in crop production [22–24]. Additionally, seasonal climate conditions can be employed for the optimal adjustment of irrigation rules through simulation approaches [25]. Typically, decision-making on irrigation requires scientific approaches such as simulation or simulation-optimisation, where future weather conditions are predicted [26]. To enhance irrigated agricultural production, there is a need to adopt irrigation scheduling optimisation approaches across different weather patterns and seasonal climatic conditions. These concepts are important for all types of irrigated farming, but they are particularly critical for the sugarcane crop, which is highly water-demanding and strongly affected by seasonal climate variability.
This study was conducted in the Burdekin irrigation area of Queensland with the aim to optimise irrigation scheduling practices, particularly seasonal water planning for sugarcane. At the beginning of any irrigation season, cane growers typically need to plan for the required seasonal water supply to the crops (plant crop and ratoons), given the available water allocation, despite their uncertain knowledge of the coming seasonal climate. Here, we propose a simulation-optimisation framework and bio-economic analytic, integrating adaptive sugarcane production and profit functions to seasonal climate conditions to support this seasonal planning. The proposed approach explicitly connects climate-driven biophysical crop responses with seasonal economic optimisation of irrigation decisions, unlike the existing frameworks that depend on fixed or average crop-yield and profit relationships. This allows for the direct estimation of the economic value of irrigation water across various climate scenarios, rather than solely focusing on maximising yield or gross margin. We aim to achieve the best EWV of seasonal water supply, i.e., maximising expected returns per megaliter (ML) of irrigation water on a seasonal aggregation period, especially maximising the economic value per unit of irrigation water according to the possible seasonal climate conditions. This is essential for stakeholders’ decision-making in improving agricultural water use efficiency in Australia and global sugarcane growing areas. Growers can decide early in a season which irrigation schedule applies to plant crops or ratoons for the best whole-of-farm profitability or for the best value per unit of irrigation water, including the possibility of trading in or out of the water market.
2 The study area
This study was conducted in the Burdekin sugarcane growing region, Queensland, Australia (approximately ~18–20° S, 146–148° E) (Fig 1), which is located in the area immediately surrounding the Ayr climate station of the Queensland government’s Department of Primary Industry (DPI) and is characterised by tropical savanna climate under the Köppen classification [27]. The Burdekin experiences a strongly seasonal tropical climate of a wet summer from November to April and a dry winter from May to October. The average temperature in winter is between 25–30 °C, and in summer 30–35 °C [28]. The Burdekin sugar growing region has a low level of average long-term annual rainfall of 954 mm [28], which is lower than wetter tropical sugarcane regions of Queensland, such as Innisfail and Tully, with a mean annual rainfall of 3548 mm and 4109 mm, respectively [28]. Furthermore, the rainfall in the Burdekin often occurs in short intense events, so much of it is lost to runoff and drainage. Burdekin sugarcane growing region is thus highly dependent on irrigation. The Burdekin Falls Dam, with a storage capacity of 1860 GL [8], is an extremely reliable water source with irrigators within the Burdekin River Irrigation Area (BRIA) typically receiving 100% allocation in most years. The Burdekin sugarcane growing region contains approximately 80,000 hectares of irrigated sugarcane (Department of the Environment, Tourism, Science and Innovation Queensland [29]. A participatory approach was used to ensure the engagement of growers in the validation and adoption of the proposed irrigation scheduling strategies and their adaptation to on-farm variability of the production environment.
https://data.gov.au/data/dataset/geoscape-administrative-boundaries is a direct link to the base layer of the map which is openly available under a CC-BY 4.0 license (please refer to the same link to the license information of the base layer shapefile). The sugarcane cropping area is based on a modified Australian Land Use Management Classification (ALUMC) Schema, Version 8 (October 2016). Datasets are available from the Queensland Government Open Data Portal (https://www.data.qld.gov.au).
We collected case study data obtained through direct questionnaire interviews conducted with a specific group of sugarcane farmers and industry experts in the Burdekin region in 2017. The 4 sugarcane farms and farm managers (growers) participating in this study generally schedule irrigation based primarily on their historical experience including the logistics of their water delivery systems, in combination with water deficit indices by soil moisture probes such as GDots. 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 [8]. In the present study, the rationale for using irrigation schedules being adaptive to seasonal climate conditions is the potential of such practices to achieve optimal seasonal water supply by maximising the expected profits of irrigation water on a seasonal aggregation period, increasing economic value per unit of irrigation water and thus irrigation water use efficiency by growers.
3 Methodology
3.1 Ethics statement
A written application for human research ethics was approved by the University of Southern Queensland (USQ) Human Research Ethics Committee for the period from 17/01/2017–17/01/2020 (ethic approval number H17REA009). Participants were recruited in the interview with the recruitment period from 17/01/2017–31/03/2017. A written consent form was signed by each participant prior to undertaking the interview who understood that the collected data may be included in future research.
3.2 Overview
Irrigation scheduling involves determining the timing and quantity of water application [30]. In this study, sugarcane growth was simulated using the Agricultural Production Systems Simulator (APSIM)-Sugar model being calibrated to the sugarcane production areas in the Burdekin district [8]. We employed specific irrigation scheduling strategies in APSIM-Sugar that involved modifying the applied water volume from 5 mm to 100 mm per irrigation event. To examine the effect of irrigation timing, fixed intervals of 5-day (Schedule 1) and 10-day (Schedule 2) between irrigation applications were implemented. These fixed intervals were chosen based on the employed intervals by growers recruited in the interview. For simplicity, APSIM was configured to irrigate with the fixed irrigation amount and at the time interval according to each schedule regardless of the current soil water balance. This will result in runoff and/or drainage losses in times of low crop demand or heavy rainfall, but APSIM can model these effects. Long-term historical daily climate data (1889 – 2022) was used to simulate crop performance in different seasonal climatic conditions. We then developed water-yield production and profit functions of plant crops and ratoons in wet, dry, and normal seasons. Seasonal climatic conditions were classified using terciles of total seasonal rainfall from the 1889–2022 historical data. Dry, normal, and wet seasons correspond to the lower (≤ 33rd), middle (33rd–66th), and upper (≥ 66th) rainfall terciles, respectively (Table 1).
3.3 APSIM-sugar model
The APSIM-Sugar model (version 7.10 r4158) was used to simulate sugarcane growth and yield. APSIM is a point-scale, modular modeling framework that 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 [31,32]. Full documentation on APSIM-Sugar can be found at APSIM Documentation [33].
In our study, long-term sugarcane yield simulations were performed using daily climate data for the period of 1889–2022. Climate data including rainfall, solar radiation, maximum and minimum temperatures, evaporation, and evapo-transpiration (ET) were retrieved from the SILO database (http://www.longpaddock.qld.gov.au/silo) for the Ayr DPI research station (Fig 1). No further infilling or homogenization was used in this study; instead, missing observations were internally filled in using spatial interpolation. In APSIM-Sugar, evapotranspiration values are used in the calculation of plant transpiration. Transpiration demand is modelled as a function of the current day’s crop growth rate, divided by the transpiration-use efficiency. The latter is defined as the ratio of biomass gained to total water used by the plant (evapotranspiration) [34, 35] and is inversely proportional to vapour pressure deficit.
Model calibration and configurations were done following An-Vo et al. [8], ensuring simulated ranges of cane yield are aligned with observed farm practices (Fig A in S1 Appendix). The silty clay loam soil was used in our simulations, given its predominance in cane growing areas near the Ayr DPI climate station [8]. Standard values of soil parameters for soil profile No 682 in APSoil with a plant available water capacity (PAWC) of 162 mm (https://www.apsim.info/Products/APSoil.aspx; [36]) for sugarcane were used in our simulations (Table A in S1 Appendix).
For the simulations in APSIM-Sugar, sugarcane was planted with a stalk density of 10 plants/m2. This planting practice was initiated on 30 April at the beginning of each 6-year crop cycle. The crop cycle consisted of one plant crop for 15 months, followed by four ratoon crops, each for 13 months. A quantity of 100 kg N ha−1 as urea was administered at planting and 50 kg N ha−1 on a fixed date of 1st September each cropping year. This application rate was set to be appropriate in preventing any negative effects on crop yield from nutrient reduction [37]. At the start of each crop cycle, the soil water and nutrient levels were reset.
In our simulation, a ‘modified’ cultivar, i.e., q117n [8], was used because the default cultivars in the standard release of APSIM-Sugar are no longer widely used on farms (QCANESelect™; Sugar Research Australia, 2017 [38]). Due to the lack of data for the detailed characterisation necessary to simulate the physiology of currently cultivated sugarcane varieties, a sensitivity analysis was carried out to calibrate q117n. Parameter values of the default cultivar q117 were varied within plausible ranges [32, 39, 40]. Modelled cane yield and commercial cane sugar content were benchmarked against regional mill data spanning 1942–2014, and the modified cultivar showing the best statistical performance, as indicated by the mean absolute error (MAE) and the root mean square error (RMSE), was selected. Following the sensitivity analysis, four crop parameters were adjusted: green leaf number (green_leaf_no), fraction of accumulated biomass partitioned to cane (cane_fraction), fraction of accumulated biomass partitioned to sucrose (sucrose_fraction_stalk), and minimum stem biomass before partitioning to sucrose commences (min_sstem_sucrose) (Table B in S1 Appendix). The agronomic traits of q117n are similar to those of the default cultivar q117, a drought-tolerant cultivar, with the adjusted parameters values for q117n mainly related to biomass partitioning. More details of the sensitivity analysis and calibration of q117n can be found in An-Vo et al. [8]. A comparison of simulation errors of cane yield and commercial cane sugar content for q117n and default cultivars q117, q124, q138 and nco376 is presented in Table C in S1 Appendix. MAE and RMSE values were 36 t ha–1 and 52 t ha–1, respectively, for q117n cane yield. Corresponding values for commercial cane sugar content were 1.2 and 2.3 percentage points (Table C in S1 Appendix). One of the limitations of APSIM-Sugar is that it does not simulate the impacts of pests, diseases and weeds on cane yield; it is assumed that the grower would take all reasonable steps to keep their farm pest and disease-free. The irrigation module in APSIM classic applies irrigation water straight to the soil surface. 100% field application efficiency is implicitly assumed because there is no irrigation efficiency parameter. The soil water balance module uses antecedent moisture and soil properties to dynamically simulate runoff and deep drainage.
3.4 Bio-economic analysis
The bio-economic approach refers to an integrated modelling framework that combines biophysical crop simulation with economic analysis. APSIM-Sugar simulations were generated by systematically varying irrigation levels to establish yield-water use relationships (production functions) and profit functions. The profit function accounts for the total revenue minus the cost of inputs, including irrigation water and others (Table D in S1 Appendix) with respect to different climatic conditions. The profit function is outlined below.
Where P(W) is the profit function – a function of irrigation amount per event W; Y(W) is sugarcane water- yield production function under the three seasonal climate conditions, i.e., dry, neutral and wet; ph the harvesting cost (AUD 10.81 t−1 given by the Department of Agriculture and Fisheries [41]); pw water price (AUD 53.34 ML−1 given by the Lower Burdekin Water [42]); C indicates other input costs including the fertiliser cost and planting cost (plant crop only); and p is the cane price (AUD 44 t−1).
Here, we developed sugarcane water production and profit functions that adapt to different seasonal climatic conditions. Yearly profit for each crop stage was estimated using Eq (1) to form a large sample of simulated yearly profits {Pi(W)}i∈{1889,1890,…,2022}. Each year of the simulated period was classified into 3 sets js corresponding to 3 seasonal climate conditions based on the total rainfall of the cropping season j∈{wet, normal, dry}. The water profit functions for each crop stage in the 3 seasonal climate conditions were given by,
where Tj is the number of historical climate years belong to a set j of a seasonal climate condition.
In this study, we analysed water use efficiency based on the irrigation economic water value (EWV); it was calculated for each crop stage and each seasonal climate condition by dividing the corresponding water profit function (AUD ha−1) by the total irrigation water (ML ha−1) of the cropping season. This approach helped identify the most efficient schedule tailored to the seasonal climate condition that maximises economic returns of the irrigation water.
4 Results
Discrepancies were observed in the ideal amounts of irrigation water needed for each cycle of crop growth, from planting cane to the four subsequent ratoons, to achieve the highest possible yield (Table 2), maximum economic benefit (Table 3), or maximum EWV (Fig 2 and Table 5). These observations were also made in relation to the three distinct climate conditions (Figs 3, 4, 5, and 6; and Table 4). This underscores the significance of customising irrigation schedules based on prevailing climate conditions.
The water value is maximised in the wet season. Ratoon crops exhibit higher water values than those of the plant crop.
4.1 Tailored irrigation water management to respective goals based on cane yield and profitability
Biophysical and economic responses under an average climate condition at the full range of irrigation amounts differed among the plant crop and four ratoons are presented in Tables 2 and 3. In terms of cane yields, there were levels of irrigation amounts (20 mm in Schedule 1 and 35 mm in Schedule 2) above which potential cane yields reduced from plant crop to ratoon 1, ratoon 2, ratoon 3, and ratoon 4, as expected. Moreover, cane yields for ratoon 2, ratoon 3, and ratoon 4 did not increase much in our simulation above these threshold levels, indicating small or no yield benefits of additional irrigation. These thresholds were marked by yield response rates of ratoon 2, 3 and 4 converging to zeros in both the schedules (Fig 2). Below these threshold levels, potential cane yield of certain ratoon crops was similar or greater than those of the plant crop, indicating by better yield response rates to irrigation water (Fig 2).
In both the schedules, while the optimal irrigation amounts required to achieve maximum yields for the plant crops were almost at the soil water holding capacity, the optimal amounts were lower in ratoon crops indicating lower transpiration capacities (Table 2). Compared to a short schedule (Schedule 1), a longer schedule (Schedule 2) required greater irrigation amounts to achieve optimal cane yields, e.g., the optimal irrigation amount for ratoon 1 increased from 35 mm under Schedule 1 to 72.5 mm under Schedule 2 (Table 2). The shorter schedule (Schedule 1) also shows a higher potential cane yield than those of the longer schedule (schedule 2) across all crop stages (Table 2).
In economic terms, a short schedule (Schedule 1) typically achieves greater potential (optimal) gross margins (AUD 3040 ha−1) than those of a longer schedule (Schedule 2) (AUD 2830 ha−1), particularly for the plant crop (Table 3). Among the various crop stages, ratoon 1 could achieve similar profitability to that of the plant crop (Schedule 1, Table 3) or the highest (Schedule 2, Table 3). Though the plant crop had the highest potential yield, it incurred the cost of planting operations and thus resulted in similar or lower gross margins than those of ratoon 1. These costs included the planting operation, land preparation, and seed cane, which are not incurred in ratoon crops.
At low irrigation amounts below the threshold levels, similar to the yield responses, ratoon 2, 3, and 4 achieved similar or greater potential gross margins than those of the plant crop or ratoon 1 (Table 3). Comparing optimal irrigation amounts for maximum yields, the optimal irrigation amounts for maximum gross margin declined with the age of crop, i.e., 35 mm (60 mm), 30 mm (55 mm), 17.5 mm (35 mm), 12.5 mm (27.5 mm), and 12.5 mm (25 mm) in Schedule 1 (Schedule 2), respectively, for plant crop, ratoon 1, ratoon 2, ratoon 3, and ratoon 4 (Table 3). Negative gross margins occurred at the small irrigation amounts (i.e., < 12.5 mm) in both the irrigation schedules (Table 3), indicating the essential role of irrigation for viable sugarcane production in the studied region.
The biophysical and economic 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). The maximum yield and profit gaps between a wet season and a dry season reached up to 50 t ha−1 (Fig 3(b)) and AUD 1500 ha−1 (Fig 4(b)), respectively. At high irrigation amounts, the influence of climate conditions on yield and economic returns was marginal in most of the crop stages. The achievable profits tend to be higher in a short schedule (Schedule 1) than those of a longer schedule (Schedule 2), especially in the plant crop, ratoon 1, and ratoon 2 (Fig 4(a), 4(b) and 4(c)).
The simulation showed clear yield responses with increasing irrigation amount per event, but this levels out to a flat response when the irrigation depth exceeds threshold levels. The optimal irrigation amounts in each seasonal condition also varied among the crop stages, being smaller in a short schedule (Schedule 1) and in terms of profitability (Table 4). Particularly in terms of gross margin, optimal irrigation amounts consistently increased from wet condition to normal condition, and to dry condition (Table 4), validating the fact that more irrigation water is needed in the dry seasons.
4.2 Water use efficiency
In this paper water use efficiency is expressed as the economic water value (EWV). Our results indicate that potential water use efficiency was higher in the short schedule (Schedule 1–5-day) owing to more frequent irrigations, avoiding crop water stress. In contrast, a longer schedule (Schedule 2–10-day) showed lower water use efficiency due to the longer delay between irrigation events, leading to increased water stress and, hence, reduced the potential yield (please see the shifts in peaks of potential yield, profit and EWV in Figs 3, 4 and 5, respectively). In both irrigation schedules, the effect of seasonal precipitation was more significant with smaller irrigation amounts, though less regular irrigation by Schedule 2 leads to more sensitive yield and economic responses. At an irrigation amount of 5 mm, cane yield (economic) difference between a wet and a dry season in the Schedule 1 can be up to 40 t ha−1 (AUD 1200 ha−1) in the plant crop (Fig 3(a) (Fig 4(a))) and up to 50 t ha−1 (AUD 1500 ha−1) in ratoon 1 (Fig 3(b) (Fig 4(b))), while in the Schedule 2, the highest difference was noted in ratoon 1, being approximately 50 t ha−1 (AUD 1500 ha−1) (Fig 3(b) (Fig 4(b))). For an irrigation amount being greater than 40 mm, the yield (economic) difference was less than 10 t ha−1 (AUD 300 ha−1) in both the schedules (Figs 3 and 4). These highlight the significant differences in water use efficiency depending on seasonal climate conditions and crop stages for deficit irrigation management (with limited irrigated water amount and less regular irrigation) in the sugarcane industry. There were distinct results in terms of EWV where the greatest water use efficiencies were achieved in ratoon 2 (AUD 203 ML−1 with Schedule 1 and AUD 200 ML−1 with Schedule 2) and ratoon 3 (AUD 187 ML−1 with Schedule 1 and AUD 201 ML−1 with Schedule 2) instead of plant crop or ratoon 1 (Table 5). A short or long schedule had limited effects on achieving potential EWVs in all crop stages. All ratoons, however, showed significantly higher potential EWVs than those of the plant crop, due partly to the shorter duration (13 months) of ratoons compared to that of the plant crop (15 months), leading to smaller total irrigation volumes per cropping season. The better yield responsiveness to irrigation of certain ratoon crops below threshold irrigation levels identified above (Fig 2), also contributed to improved EWVs. Interestingly, there were also optimal irrigation amounts to achieve the best EWVs, which were smaller than those to achieve the best cane yields and gross margins in both the schedules (Tables 4 and 5). In this work, we showed in sugarcane production that irrigation schedules with even more deficit irrigation amounts than those achieving the best profits to achieve the best economic efficiency of irrigation water.
The quantified EWVs in various sugarcane crop stages and seasonal climate conditions provided a more nuanced understanding of water use efficiency (Figs 5 and 6). In both the schedules, EWVs tended to be high across all crop stages in a wet season. Notably, the value of irrigation water was boosted in wetter climates to more than AUD 250 ML−1 (ratoon 3 in Schedule 2, Fig 6(b) at 17.5 mm per event (Table 4)). This showed a complementarity between rainfall and small irrigation water amounts (irrigation depths less than 20 mm for Schedule 1 and less than 40 mm for Schedule 2) that higher soil moisture/air humidity results in lower evaporative loss and thus stronger yield responses, gaining higher economic outcomes. In wet seasons, rain maintains the soil moisture/air humidity, so small amounts of irrigation can effectively relieve short-term water stress during sensitive growth stages. This leads to higher EWVs than those in dry conditions. With the limited irrigation amounts, a longer schedule (Schedule 2) showed greater impacts of seasonal climate on water use efficiency. Particularly, Schedule 2 at 5 mm in a wet season improved EWVs by up to AUD 600 ML−1 relative to those achieved in a dry season (ratoon 1, Fig 5(b)) while the improvement in potential economic value of water was smaller, being up to AUD 116 ML−1 (ratoon 3, Fig 6(b)). In all crop stages and both the schedules, optimal irrigation amounts achieving the maximum EWVs (water use efficiency) increased from a wet season to a normal season, and to a dry season (Table 4) but vice versa for the maximum water values themselves (Fig 6). The simulated patterns of water use efficiency and irrigation response align with established field studies in the Australian sugarcane sector (e.g., [8, 37, 43]), thereby validating the reliability of the model outputs. The negative water returns in Table 5 and Fig 5 represent those water amounts (application depths) per event which result in a negative financial return per unit of irrigation water. These irrigation schedules should be avoided in favour of those with positive outcomes. We acknowledge that these results are still subject to modelling uncertainty, as APSIM does not explicitly account for all sources of variability, including pests, diseases, irrigation system losses, or management heterogeneity among farms. The results are also specific to the soil, cultivar, prices and climate selected for this study.
5 Discussion
We investigated the optimisation of irrigation scheduling, adapting to seasonal climate conditions and enhancing the value of irrigation water for sugarcane crops in the Burdekin region. We analysed irrigation water use efficiency of sugarcane in terms of irrigation scheduling and in a wider sense, including yield, profitability and water value (EWV). The results revealed enormous opportunities for enhancing the cane yield, profitability and water use efficiency. Particularly, the optimal irrigation amount varied depending on the optimisation objective. The highest sugarcane yields generally occurred at a higher irrigation amount, the highest profitability at a slightly lower irrigation amount while the highest EWV was achieved at a lower irrigation amount than the other two optimal values (optimal EWV < optimal profitability < optimal cane yield). This expected trend reflected the law of diminishing marginal returns with increased water applied and the increase in variable costs proportional to the amount of water used. Our findings enhance existing knowledge by illustrating the systematic variation of optimal irrigation amounts across yield, profitability, and water-value objectives, as well as the seasonal shifts in these optima. This study provides a more integrated and climate-responsive framework compared to prior research, which generally focuses on optimising a singular objective (yield or profit) without clearly measuring EWV throughout different crop stages and seasons. This resulted in diminishing economic returns (gross margins) as the amount of water used increased, along with the associated costs (Fig 4). In the other hand, EWV focuses on the economic return per water unit used, emphasising efficiency. Irrigation water productivity and profitability thus do not mean the best economic values of irrigation water.
These findings are consistent with existing literature showing that optimal water use efficiency is usually achieved at lower irrigation amounts compared to those needed to maximise yield or profitability. Simulation-optimisation frameworks [12, 30] demonstrated that irrigation schedules optimised for water use efficiency require less water than those aimed to maximise yield or profit. This highlights the importance of goal-specific irrigation scheduling in sugarcane cropping systems, especially under climate variability, by determining the required irrigation amount for optimal water use efficiency, profitability, and yield.
Our interpretations were based on the simulation in which we assumed no constraints such as soil, fertiliser, water quality, and diseases to achieve the potential cane yield. The potential cane yield thus depended on irrigation water availability and decreased from the plant crop to the four ratoons, reflecting the reduced responsiveness of older ratoons to irrigation. Under the average climate condition at the studied region, we also found distinct responses to irrigation water between the plant crop and ratoon crops. While the transpiration capacity of the plant crop was greater leading to greater cane yields, ratoon crops showed small or no yield benefits of irrigation with regular volumes above the threshold levels. Ratoon crops, however, show better water use efficiency with irrigation amounts below the threshold levels. In both schedules and crop stages, irrigations above the threshold levels produce diminishing yield and gross margin response. These thresholds, quantified in the results section, underscore the importance of avoiding irrigation beyond the crop marginal benefits. Our results indicate that potential cane yields and gross margins were higher in the short schedule (Schedule 1 – 5-day) owing to more frequent irrigations, avoiding crop water stress. However, these benefits of more regular irrigation also require more labor, infrastructural pressure, and the risk of increased drainage (modelled drainage was not reported).
Sugarcane yields in response to irrigation water varies according to the sugarcane cultivar, phenological development cycles, and environmental conditions (soil properties, weather). Indeed, greater yield responses to irrigation water can be achieved if irrigation is applied to match the soil water deficit under the plant transpiration capacity and when soil water evaporation is low, when stalk elongation has commenced and when relative humidity is high [43, 44]. Our results highlight the importance of determining optimal irrigation amounts according to crop stages and seasonal climate conditions. Indeed, for all crop stages, optimal irrigation amounts varied according to the seasonal climate conditions in terms of both best cane yield and profitability, highlighting the need for adaptive, climate-informed irrigation scheduling practices (Figs 3 and 4). This demonstrates the vital role of climate-informed irrigation in maximising the economic value of irrigation scheduling decisions. An-Vo et al. [8] found that seasonal forecasting is important for sugarcane irrigation planning and suggested that making well-informed decisions based on climatic prediction can lead to higher yields and less water use. This aligns with research by Brown et al. [26], who found that data from seasonal climate forecasts and ideal irrigation schedules can greatly increase the economic benefit.
Strong influences of climatic conditions on yield and economic returns were observed when irrigation amounts were limited. Due to enormous climate variability in Queensland and eastern Australia, a tailored irrigation scheduling system is important, optimising water application in terms of timing and quantity. Our analysis highlights the importance of irrigation water management according to seasonal climate conditions and crop stages for maximum economic returns or maximum water use efficiency. The latter can be achieved with more deficit irrigation schedules than those of the former. The emphasis is to irrigate at the optimal schedule in each seasonal climate condition. In the wet season, higher rainfall and water availability allow for reduce irrigation frequency and irrigation amount, avoiding over-watering, while in a normal or a dry season, it is important to irrigate based on the optimal amount. These results highlight the significant differences in water use efficiency depending on seasonal climate conditions and crop stages for deficit irrigation management (with limited irrigated amount and less regular irrigation) in the sugarcane industry. Particularly in the dry season, the strategy should emphasise the use of increased irrigation volume and frequency if there is enough water availability to substitute for reduced rainfall, with a focus on crucial periods of crop phenology of sugarcane requiring more water. It is thus important to monitor climate conditions for the dynamic adaptation of irrigation strategies.
Irrigation scheduling is also dependent on the growers’ experience, as their insights involving the timing and amount of irrigation based on their observations and past experiences assist in refining the decision-making in water management. During drought periods, the strategy of prioritisation becomes necessary to maximise EWV. In the Burdekin sugarcane growing region, growers generally schedule irrigation on a regular 6–8-day interval at the peak demand periods with the water volume based on monitored soil water deficit indices regardless of the type of season it can be. Short-term weather forecast is used and there may be very little irrigation if forecasted rainfall in the coming days. If dry weather is forecasted, 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. 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, growers may decide to abandon older less productive ratoons in favour of keeping younger more productive crop cycles alive and growing.
Our results, however, provide more nuanced insights indicating that adapting irrigation schedules to seasonal climate conditions and crop stages can achieve better water use efficiency beyond the current practices. In a wet season, rather than focusing on avoiding over-watering by limiting or no irrigation, our emphasis is to irrigate at the optimal schedule of each plant crop or ratoons to enjoy the high economic value of water. Particularly, owing to good water availability and low water price on the water market (e.g., [45, 46, 47]), growers can trade in more water to expand their sugarcane growing areas given the known benefit owing to the gap between the EWV of sugarcane production and water prices of the market. In a prolonged dry period with limited water availability, rather than abandoning older ratoons in favour of plant crops higher EWVs can be achieved in ratoons at optimal irrigation schedules. These schedules should be a smart adjustment of not only longer intervals between irrigation events and reduced water volume but also prioritizing water to more sensitive phenology periods such as tillering and stalk elongation. In cases where there is not enough water to irrigate at the optimal amount, trading water at a high price in the water market can be done if the water price is still lower than maximum EWVs potentially achieved among the crop stages. The results in this paper are dependent on the assumptions on crop management and costs and thus any changes in these values will alter the numerical outcome. However, the trends are likely to persist, and the methodologies will be equally applicable if any of these assumptions change.
6 Conclusion and recommendation
A novel integrated bio-economic analytic was developed, integrating the calibrated APSIM model with production and profit functions that are adaptable to seasonal climate conditions. Results showed that a well-planned irrigation strategy, being tailored to seasonal climate conditions, can significantly provide better water use efficiency, crop yields and potential economic benefits. The present study also underlines the importance of maximising the economic value of irrigation water in Australian sugarcane farms. A practical irrigation decision framework for each cropping season can be recommended including: (1) classify the wet/normal/dry upcoming season based on experiences, historical climate data or seasonal climate outlook, (2) select stage-specific optimal water amount (irrigation depth) and interval, (3) estimate the total seasonal irrigation water needed in comparison with water allocation/availability, and (4) compare the economic water value (EWV) with water market price to decide buying/selling.
Understanding the optimal irrigation schedule which depends on optimisation objectives and the maximum value of water in various seasonal climate conditions has been shown to improve irrigation water management beyond current practices. This is especially important during wet or dry seasons, particularly in extreme conditions like prolonged drought periods. We demonstrated two important trends of optimal irrigation amounts (depths) influenced by seasonal climate variability. While those achieving highest water values are smaller than those achieving highest farming profitability which are smaller than those achieving highest cane yield; they also increase from a wet to a dry seasonal climate in all the three goals.
We investigated the irrigation performance in seasonal climate conditions across the sugarcane crop cycles, where the plant crop with higher transpiration capacity responded with a higher yield to larger irrigation amounts up to the soil water holding capacity. Ratoons with lower transpiration capacities showed limited or no benefit of irrigation beyond a certain water volume threshold level. Potential water use efficiency, however, was better with ratoons, represented by greater water values with deficit irrigation schedules in all seasonal conditions. We also demonstrated that a wetter season could amplify irrigation water use efficiency. The effect of irrigation timing was also studied in two irrigation schedules of 5 and 10-day intervals, showing that more frequent irrigation enhanced sugarcane potential yields and economic benefits owing to less water stress.
As previously mentioned, this paper considered fixed irrigation amounts at two fixed irrigation schedules. Future work should focus on applying this modelling framework to other irrigation scheduling approaches for example: (1) a fixed irrigation amount on a flexible schedule linked to the water balance, and (2) a fixed irrigation amount on a flexible schedule informed by the water balance and short-term weather forecasts.
Supporting information
S1 Appendix. Table A in S1 Appendix.
Soil parameters used for sugarcane growth simulations at Burdekin (APSoil No 682). Table B in S1 Appendix. Plausible ranges of selected variety parameters as reported in the literature, and modifications performed after sensitivity analysis. The modifications applied both to the plant crop and ratoons. q117n is the cultivar with modified parameters values obtained after the sensitivity analysis. Table C in S1 Appendix. Comparisons between observed and predicted cane yield and commercial cane sugar for default cultivars q117, q124, q138 and nco376 and the ‘modified’ cultivar q117n. MAE and RMSE refer to mean absolute error and root mean square error, respectively. q117n is the cultivar with modified parameters values obtained after the sensitivity analysis. Table D in S1 Appendix. Variable costs (AUD ha−1) for plant and ratoon crops in the Burdekin region, Australia are based on 2021–22 estimates provided by ABARES [48]. Note that the costs here do not include irrigation, fertiliser, and harvesting costs. Irrigation and fertiliser costs were estimated by the total irrigation water volume and total urea amount, respectively, used in simulations for each plant or ratoon crop. Water charge is AUD 53.34 per megalitre (Lower Burdekin Water, 2022). Urea price is AUD 750 t−1 harvesting cost were estimated with a rate of AUD 10.81 t−1 (Department of Agriculture and Fisheries, 2024 [41]). Fig A in S1 Appendix. 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 the interview conducted in the Burdekin region; regional yield datasets were supplied by the Canegrowers, Australia (http://www.canegrowers.com.au).
https://doi.org/10.1371/journal.pclm.0000696.s001
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