Evaluating the potentials of deficit irrigation as an adaptive response to climate change and environmental demand

environmental science & policy 14 (2011) 1139–1150

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Evaluating the potentials of deficit irrigation as an adaptive response to climate change and environmental demand Shahbaz Mushtaq a,*, Mahnoosh Moghaddasi a,b a b

Australian Centre for Sustainable Catchments, University of Southern Queensland, Toowoomba, Qld 4350, Australia College of Agriculture, Arak university, Arak, Iran

article info Published on line 19 August 2011 Keywords: Deficit irrigation Non-linear programming Climate change adaptation Production function Profit function Environmental water demand

abstract Water is increasingly becoming scarce due to competing demands from agriculture, industry, recreation and the environment. With increased concerns regarding climate change and environmental water demand, system managers and irrigators are being forced to consider deficit-irrigation options. This study illustrates the potentials of deficit irrigation as an effective adaptive response to climate change and environmental water demand in achieving efficiency gains, water saving and maximizing benefits that could be achieved at system level. We compared three scenarios: optimization with full irrigation, optimization with deficit irrigation and deficit irrigation without optimization. A non-linear optimization model, which uses crop production function and profit functions endogenously, was used to evaluate the potential of deficit irrigation. The results show that optimization with deficit irrigation could result in both environmental flows and maximizing net returns objectives, increase overall water use efficiency, and therefore offer an effective adaptive response against climate change. We envisage deficit irrigation could be used as a cost-effective adaptive response for meeting climate and environmental objectives. Water saved through deficit irrigation could be used to restore environmental balance through augmenting environmental flows. # 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Deficit irrigation, a practice of deliberate under-irrigation of a crop or deliberate stressing of crops to influence yield and profit (Robinson, 2004; English et al., 1990, 2002; English and Raja, 1996; Gorantiwar and Smout, 2003; Panda et al., 2003; Zhang and Yang, 2004) or save water (Khan et al., 2008a,b; Ouyahia et al., 2005). A system that has been attracting increasing interest globally, because it appears to provide several useful objectives: using less water to achieve the same yield result and, hence achieving environmental objectives by providing additional water for environmental purposes, therefore providing robust tool to manage climate change impacts; using the

same amount of water to achieve a better result (in terms of profit); or actually improving the quality of the final product. Deficit stands in contrast to the current irrigation practice on many farms of irrigating crops to meet crop water requirements (full irrigation) and consequently strive for maximum yield. Deficit irrigation is therefore an irrigation management strategy that fulfils an economic as well as biological objective (Robinson, 2004), and hence provide an effective adaptive response in meeting climate change challenges. The production economics demonstrates that with decreasing water supply, increasing water costs and diminishing marginal returns of crop production to water use, the economic optimum level of irrigation will be less than would be required to achieve maximum yield (Robinson, 2004). In addition, reduced yield under deficit

* Corresponding author. Tel.: +61 7 463 2019; fax: +61 7 4631 5581. E-mail address: [email protected] (S. Mushtaq). 1462-9011/$ – see front matter # 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsci.2011.07.007

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environmental science & policy 14 (2011) 1139–1150

irrigation could be compensated by increased production from the additional irrigated area with the water saved by deficit irrigation (Ali et al., 2007; Vazifedoust et al., 2008) or additional benefits by selling water to environmental managers through market mechanisms to improve environmental outcomes, therefore, maintaining a healthy river environment in terms of rate, temporal variability and volume over season—restoring the balance through improving seasonality of flow (Khan et al., 2008a,b; Cosier et al., 2010). Best available science suggests that for the Murray–Darling Basin (MDB) to be healthy, the river consistently needs around two-thirds of their natural flows, which means a need for an increase of around 4400 gigalitre (GL) for the environment and a reduction of around 40% in existing water diversions (Cosier et al., 2010). Deficit irrigation could be used as a cost-effective market based adaptive response for meeting environmental objectives under climate change (Khan et al., 2008a,b). Water saved through deficit irrigation could be used improve seasonality of flows. Additionally, environmental managers can buy water for environment requirements at market price and provide it back to the rivers on a seasonal flow improvement basis. Targeted buying of water will not only result in greater production from the same (or less) amount of water, but will also accrue greater environmental benefits where water is traded from degraded areas and/or low value low efficiency production (Ouyahia et al., 2005). Climatic changes, drought, ongoing reductions in seasonal water allocation, and demand for environmental water are becoming the key concern for irrigators in Australia. Climate change is causing an increase in temperatures and a decrease in rainfall making water increasingly scarce (Cortignani and Severini, 2009; Hafi et al., 2009). The predicted future climate, especially in the MDB, is expected to be one of lower average rainfall and higher average temperatures (Alcamo et al., 2005). The CSIRO assessment of water availability in the MDB confirms that view (CSIRO, 2008). A reduction in rainfall will directly affect Basin agricultural production in two ways. Firstly, reduced rainfall will reduce crop yields, and secondly, the quantity of water available for irrigation will fall. Demand for water will also be affected by government policy related to increasing flows in environmentally stressed water systems (Hafi et al., 2009; Ouyahia et al., 2005; Blanco et al., 2004). Furthermore, Smith et al. (2010) find that impact of climate change on crop growing season, with respect to temperature and rainfall, will have a market influence on irrigation water requirements. Rainfall is the dominant climate factor that can substantially increase or decrease water requirement of crops. To be efficient, irrigators need to manage their exposure to the risks associated with decreasing seasonal allocation. Consequently, the irrigation industry is continually adjusting to become more efficient in the use of available water supply to maintain farm profitability and, on the other hand, can be efficient to restore environmental flows with water saving. This situation is forcing farmers to consider options associated with deficit-irrigation or growing alternative crops such as ‘winter wheat’ that require less irrigation water, but maybe generally less profitable (Payero et al., 2006). Under conditions of scarce water supply and drought, deficit irrigation can lead to greater economic gains than maximizing yields per unit of water for a given crop; farmers are more inclined to use water

more efficiently, and more water efficient cash crop selection helps optimize returns (FAO, 2002). The main aim of the paper is to demonstrate the potential of deficit irrigation as cost effective adaptive response against climate change by achieving efficiency gains, maximizing returns and water saving, and environmental flows at a system-level. The basic information needed to adopt this technique is the response of water deficit of various stages of the crop. To understand this relation, we employ crop production functions, developed by Khan et al. (2008a,b), relating yield response to irrigation applications using the SWAGMAN Destiny crop growth model. Production functions were then used to derive profit functions. The optimal level of irrigation for each crop was determined using profit functions. A non-linear optimization was carried out to analyse the performance of deficit irrigation. We compared three scenarios: optimization with full irrigation, optimization with deficit irrigation and deficit irrigation without optimization. The results show that optimization with deficit irrigation could achieve both environmental flows and maximizing net returns objectives, and therefore increase overall water use efficiency.

2.

Deficit irrigation and its application

Techniques for applying water for deficit irrigation involve limiting application depths, such that a portion of the field is under-irrigated and controlling irrigation timing and frequency to maximize water use efficiency (English et al., 1990). However, making the right decision remains important more than traditional know-how. There is also considerable skill required in identifying the best time to apply water deficits which has the minimum impact on crop yield and quality. Consequently, pressurised irrigation systems are more likely to be suitable for the application of deficit irrigation compared to flood systems (Robinson, 2004). In the first instance, farmers need to be able to put a realistic value on the water which will be used, so as to balance that against the real market value of the product (Playa´n and Mateos, 2005). Deficit irrigation is being practiced in many world regions to increase agricultural production from available water supplies. The practice of deficit irrigation is widespread in the Great Plains and the Columbia Basin of the United States of America, the Indian subcontinent, parts of Africa and other water-short regions of the world. For example, in India where there is over one million square kilometres of regularly drought affected land, irrigation projects are designed for extensive irrigation where available water is spread over a large area and is primarily used to protect crops from complete failure rather that to meet full crop water requirements (FAO, 2002; Robinson, 2004). Water use policy and irrigation management in parts of Pakistan are similar to India where it has been estimated that overall water use is 35% below full crop water requirements. In Texas, United States, where water supply is limited, it is common practice to irrigate roughly double the nominal area that would occur under full irrigation. Deficit irrigation of wheat and corn is common practice using centre pivot systems in the Columbia Basin of the United States (Henggeler et al., 2002; English et al., 1990). A number of the empirical studies by Kirda et al. (1999) have showed that considerable water savings can be achieved with

environmental science & policy 14 (2011) 1139–1150

deficit irrigation with minimal impact on yield. For example a study in Turkey found that a reduction in irrigation water application of around 30–40% in cotton and approximately 20% in maize and sugar beet could be achieved without significant decreases in yield. Likewise, irrigation application could be halved for wheat in Morocco and maize in Brazil without significantly decreasing yield (Kirda et al., 1999). Similarly, analytical studies of Gorantiwar and Smout (2003) and Kumar et al. (1998) found that deficit irrigation allowed total crop production and the irrigated area to increase by 45% and 50%, respectively, for an irrigated area in India, with an increase of net returns of approximately 20%. Several mathematical models, optimization and simulation, have been used for optimal management of water resources (Shangguan et al., 2002; Reca et al., 2001; Kumar et al., 2006; Moghaddasi et al., 2010). Shangguan et al. (2002) developed a recurrence control model for regional optimal allocation of irrigation water resources, aiming to overall maximum efficiency, with decomposing-harmonization principal of large system. Their model consists of three layers that optimize crop irrigation scheduling, allocate optimally water among the various crops and irrigation subsystems. Kumar et al. (2006) compared linear programming (LP) and genetic algorithm (GA) for optimum water allocation of a single purpose reservoir. The objective function was to maximize the relative crops yield. They introduced river inflow, effective rainfalls, and seasonal water competition of the crops, soil available moisture content in the different fields, soils heterogeneity and the crops sensitivity to water deficit as inputs to their model. The results showed that performances of the LP and GA models are not significantly different. Moghaddasi et al. (2010) compared two approaches, optimization on equitable water reduction, to manage agricultural water demand during drought to minimize impact. To evaluate these methodologies, the 1999 drought in the Zayandeh Rud irrigation system of Iran was selected and the required models developed. In the optimization method, crop growth stages and their sensitivity to water stress at different stages are embedded in the calculations. The results show that the optimization method resulted in 42% more income for the agricultural sector using the same amount of water allocated in the 1999 drought. This brief review leads us to conclude that deficit irrigation has been widely used in the water sector but its application as cost effective adaptive response in meeting climate change and environmental challenges is very limited in Australia. Crop production functions have been rarely applied in nonlinear models to demonstrate the benefits of deficit irrigation practices. Armed with such non-linear optimization models that endogenously use crop production functions, we aim to contribute to literature of optimal water resource operation and management.

3.

Methodology

3.1.

Theoretical model

The maximum yield of a crop (Ym) is achieved when water is adequately supplied during the growing season and no other agronomic constraints limit production. Applying water to

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obtain maximum production is usually the optimal irrigation strategy when water supplies are plentiful. However, when water supplies are limited, the optimal strategy is for some level of deficit irrigation to maximize yield per unit of water (Robinson, 2004). The evapotranspiration required to achieve maximum yield is called maximum evapotranspiration (ETm). Crop stress due to water deficit has an effect on the crop’s evapotranspiration and yield. When the soil moisture is depleted beyond a critical level, the actual evapotranspiration (ETa) is less than the maximum amount. This reduces crop development, photosynthesis and ultimately the quantity and quality of production (Kodal et al., 1997). The actual yield of a crop (Ya) will depend on the timing and intensity of the water stress during the crop’s growing period (Robinson, 2004; FAO, 2002). The relationship between crop yield and evapotranspiration deficit can be determined when the crop’s water requirement for maximum yield and actual crop yield in response to water deficits can be quantified. It is often expressed as a linear relationship where the relative yield decline (1  Ya/Ym) and the relative evapotranspiration deficit (1  ETa/ETm) are correlated by the empirically derived yield response factor (ky) (Robinson, 2004; Doorenbos and Kassam, 1986). If the main objective of farming is to maximize profits, the problem is to determine the optimum level of inputs use that will achieve maximize profits. The optimum amount of a variable input is the amount that maximizes short-run profits from the production process. Once the optimum amount of variable input is being used in the short run, the only possible way to increase profits is to change technologies or quantities of fixed inputs (Robinson, 2004; Doll and Orazem, 1984). When irrigation is constrained by water availability or limited irrigation system capacity and irrigable land is not limiting, the water saved with deficit irrigation might be used to irrigate additional land.1 Maximum farm net returns will be achieved by maximizing the crop’s net returns to the most limiting resource (English et al., 2002). Therefore the optimal level of irrigation to maximize profits when water is the most limiting resource is the level that maximizes the crop’s Gross Margin (GM) per unit of water applied. For irrigation systems having number of different crops with limited water supply and other cropping constraints such as land, labour, capital, markets, cropping rotations, the optimal irrigation volume for each crop becomes complex. When water supply is limited there is an opportunity cost for water and the decision maker needs to consider all crops and alternative uses of water (e.g. sell water to environmental manager through water market for environmental purposes) simultaneously and allocate water to its most profitable use. Since this is a complex problem, therefore, mathematical programming techniques such as linear, non-linear or dynamic programming are often used to optimize net returns within the system level water and other constraints (Shangguan et al., 2002; Reca et al., 2001; Kumar et al., 2006; Moghaddasi et al., 2010). 1 We did not pursue the case of maximizing gross returns under limited land as Australia has abundant land while water is a limiting factor.

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3.2.

Optimization model

An optimal farming plan was developed aiming to maximize the aggregate gross margin (total revenue less variable costs) from crop production and water trading subject to several land, water, technical and administrative constraints. The model was then applied for a regional irrigation area, Coleambally Irrigation Area (CIA) of New South Wales (NSW), Australia. The following model was used to obtain optimal cropping plan under different deficit irrigation scenario: Max

Y

¼

where GWsy is sustainable yield of the groundwater. The groundwater sustainable yield was based on groundwater sharing plans. The groundwater use 206,000 ML during 2000–2001 was lower than the announced allocation (471,200 ML) for the same year (Kumar, 2002).

3.3.3.

Land constraints

Land allocated to various crops under different technologies must not exceed total available cultivable area during the summer and winter seasons.

I X R I X R I X X X Pi  ðY ir Þ  Xir  Cir  Xir  ½g  Ps

I X R X Xir  TA

i¼1 r¼1

i¼1 r¼1

i¼i r¼1

 Wi þ ð1  gÞ  Pg  Wi   Xir

i¼1

(1)

where i are the numbers of crop grown in the whole CIA, r represent irrigation technology, Pi is output prices ($1 t) of crop i, Yir is crop yield (t/ha1) grown with technology r, Xir area devoted to different crops (ha) with technology r, Cir is costs of variables inputs other than water costs ($1 ha) with technology r, Ps is surface water price ($ML1), Pg is groundwater pumping cost ($ML1); Wi is crop water use (ML ha1) of i crop with r technology, and g is percentage of water from surface water, the value of g = 1 implies all surface water was used. The yields of main crops in response to water use, crop production functions, were obtained from Khan et al. (2008a,b), who estimated crop yields for the various crops using Griffith, NSW, rainfall data for the years 1962–2001 and applying irrigation of specified amounts at set dates during the growing period. Total water inputs, i.e. irrigation plus rainfall—yield crop production functions were derived for various crops using the SWAGMAN-Destiny model. The production functions were derived by fitting the following non-linear curve using Ordinary Least Squares (OLS) regression analysis. Yir ðWir Þ ¼ b0 þ b1 Wir þ b2 Wir 2 b3 Wir 3 þ ei

(5)

(2)

where Yir is yield of crop i with technology r, Wir is total water used by crop i with technology r, and bi are coefficients of total water use, b0 is constant, and error term ei.

where TA is total cultivated area available, which is about 79,000 ha.

3.3.4.

Allowable area constraint

Management considerations, market conditions, machinery capacity of the farm, and climatic conditions, restrict the minimum or maximum land acreages under certain crops to meet the local food production in the command area.

(a) Lower bound Xir  mmin ir TA

(6)

(b) Upper bond TA Xir  mmax ir

(7)

where mmin and mmax is minimum and maximum fractions of cultivated area allowed under crop i and technology r.

3.3.5.

Non-negativity constraint

The non-negativity constraints are given in Eq. (8). Xir  0

(8)

3.3.

Model constraints

This constraint ensures that the solution remains physically possible. The analysis was performed under the assumption that crop inputs will remain the same with full and deficit irrigation practices. The only input that varied was water use.

3.3.1.

Surface water constraint

3.4.

The total surface water use must not exceed the corresponding announced water allocation for the year. I X R X

NCWRir Xir  SWv

(3)

i¼1 r¼1

where NCWR is net crop water requirement (ML ha1) of crop i with technology r, SW is the total surface water entitlements for CIA, v is the general security water announced allocation for a for 2000/01, which is fraction of total surface water entitlement.

3.3.2.

Groundwater constraint

The groundwater licenses/withdrawal of should not increase above minimum sustainable yield. I X R X NCWRir Xir  GWsy i¼1 r¼1

(4)

Deficit irrigation scenario and their rationale

To demonstrate the effectiveness of deficit irrigation in managing negative impact of climate change and achieving environmental flow objects with maximum returns, minimizing losses, we compared three different scenarios under different levels of water allocations.

3.4.1.

Scenario 1: Optimization with full irrigation practices

Under this scenario, we run the NLP model under 100% water allocations level to calculate the optimal crop areas and maximum gross margins. Then, we progressively reduced water allocation, aiming to divert additionally saved water for environmental purposes, to evaluate the impact of optimization on crop areas and maximum returns. Later, we used temporary water trading prices, proxy for opportunity cost of water, to value additional saved water to demonstrate the overall benefits of optimization with full irrigation practices.

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environmental science & policy 14 (2011) 1139–1150

Fig. 1 – Coleambally Irrigation Area (CIA) in New South Wales, southeast Australia (see inset) relative to the Murrumbidgee Irrigation Area and the Murray Irrigation Area near the border between NSW and Victoria.

3.4.2.

Scenario 2: Optimization and deficit irrigation practices

In this scenario, we employed the deficit irrigation concept where irrigators use deficit irrigation practices as an effective tactical response to maximize the return or minimize the economic losses at lower levels of water allocations. The crop production functions used in the NLP model endogenously determined the optimal water use for each crops. Similar to scenario 1, we value additional water saving using temporary water trading prices show overall benefits of deficit irrigation.

3.4.3. Scenario 3: Deficit irrigation practices without optimization Under this scenario, we run NLP model under 100% water allocations level to calculate the optimal crop areas and maximum gross margins. Then, we apply deficit irrigation by equally reducing water use for each crop to calculate the impact of gross margins on total gross margins. Later, the overall impact of deficit irrigation was estimated by valuing water saving at temporary water trading prices.

has a bulk license of 629 GL of water. Over the years the water deliveries have been decreased, 389 GL water was supplied during 2005/2006 (Fig. 2). Available water supplies are allocated on a priority basis, first to the high security and then to the general security water. The high security licenses are fewer; the town water supplies have the highest security of all consumptive water licenses followed by permanent crops. Irrigators with high security water usually receive close to full entitlement, 95% during 2005/2006. Recently, general security allocations have been as low as 20%. Overtime, contraction in water supply/allocations has meant higher uncertainty for irrigators. Licence holders are allowed to participate in spot water markets (Bjornlund and McKay, 2002; Bjornlund and Rossini, 2005; Bjornlund, 2006) and permanent trade in water entitlements (Bjornlund and Rossini, 2007) subject to certain regulations. In the early years of water trading, CIA was a ‘‘net importer’’ of temporary water, however in the last 5 years CIA had been a ‘‘net exporter’’ of

700000

4.

Study area

600000

(ML)

500000 400000 300000 200000 100000

Annual Diversion

2005/06

2004/05

2003/04

2002/03

2001/02

2000/01

1999/00

1998/99

1997/98

1996/97

0 1995/96

The Coleambally Irrigation Area (CIA) is located in the south eastern part of the Murray Darling Basin in New South Wales, Australia (Fig. 1). The CIA is one of the largest irrigated land settlement schemes attempted in Australia. The irrigation area was constructed to make use of water diverted westward as a result of the Snowy Mountains Hydro-Electric Scheme. Water is supplied from the Murrumbidgee River from the Gogeldrie Weir pool through the 41 km main canal and 477 km of supply channels. The Irrigation Area covers some 79,000 ha of intensive irrigation, 42,000 ha irrigation/dry farms and 297,000 ha Outfall District stations delivering water supply to 473 farms owned by 364 business units. The average rainfall in the CIA lies between 400–450 mm/year. Coleambally Irrigation

Licenced Entitlement

Fig. 2 – Annual diversion and licensed entitlements in the CIA between 1995/1996 and 2005/2006. Source: Coleambally Irrigation Cooperative Limited (2006).

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environmental science & policy 14 (2011) 1139–1150

50000 40000 30000 20000 10000 2006/07

2005/06

2004/05

2003/04

2002/03

2001/02

2000/01

1999/00

1998/99

-20000

1997/98

0 -10000

1996/97

Volume (ML)

temporary water (Fig. 3). This can probably be attributed to reduced allocations leading to insufficient water for a reasonable summer cropping program and a significant increase in the price of temporary transfer water. Land use patterns show the diverse nature of agriculture in the region (Table 1). Crops, including cereal and oilseeds, rice, and annual pastures for grazing and dairy stock are the major outputs. There is gradual decrease in rice growing area. Also yield, crop water use and gross margin are shown in Table 2. Crop production functions (irrigation plus rainfall) for CIA were obtained from Khan et al. (2008a,b), who estimated yields for the various crops and using Griffith rainfall data for the years 1962–2001 and applying irrigation of specified amounts at set dates during the growing period. The production functions of all major crops are shown in Table 3.

-30000 -40000 -50000 Volume traded in (ML)

Volume traded out (ML)

Net transfer (ML)

Fig. 3 – Volume of water transferred in and out of the CIA and net transfers. Source: Coleambally Irrigation Cooperative Limited (2007).

Table 1 – Land use in the Coleambally Irrigation Area.a Land use Rice Pasture Wheat Barley Corn/maize Oats Soybeans Canola Triticale Sorghum Faba beans Lucerne Sunflower Summer pasture Winter pasture Miscellaneous Total

2000/2001

2001/2002

2002/2003

2003/2004

2004/2005

2005/2006

38 15 19 5 5 1 6 3 1 1 0 1 0 0 0 5 100

32 16 25 6 4 2 4 3 2 1 0 1 0 0 0 4 100

16 17 31 7 8 4 3 3 4 2 2 1 0 1 1 6 100

17 17 29 7 6 5 3 2 5 2 2 1 0 1 0 3 100

12 19 30 9 5 5 2 4 3 1 0 1 1 2 1 5 100

26 22 21 10 5 3 3 2 2 1 1 1 1 0 1 2 100

Source: Coleambally Irrigation Cooperative Limited (2007). a

All numbers are presented as a percentage of the total.

Table 2 – Yield, crop water use and gross margin per hectare, Coleambally Irrigation Area. Crop Rice Wheat Oats Barley Maize Canola Soybean Summer pasture Winter pasture Lucerne (uncut) Vines Citrus Winter vegetable Summer vegetable

Yield (t/ha)

Gross margin ($/ha)

Water use (ML/ha)

9.1 5 3 4.5 10 2.6 3 – – 8 15 35 30 29

1550 326 525 262 1103 204 446 431 700 1795 6750 2000 1999 2507

12 2.9 2.6 2.8 6.4 1.7 6.4 4.1 2.1 11 5.7 7.9 0.6 8

Source: Khan et al. (2008a,b) and New South Wales Department of Primary Industry (2006, datasets). ‘‘–’’ means ‘‘not applicable’’.

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0 0 0 7.4 18.3 10.8 58 0 0 0 4.1 11 12 8 0 0 0 19,250 7700 4384 7700 39,034 0 0 0 545 1537 1202 4246

Scenario 1: Managing climate change impact and achieving environmental flow objectives through optimization with full irrigation practices. a

58.1

0 0 0 7.4 18.3 10.8 58 0 0 0 4.1 11 12 8

0 0 0 19,250 7700 8160 7700 42,810 62.6 250 0 0 545 1555 1202 4246 2.8 0 0 7.4 18.3 10.8 58 2.9 0 0 4.1 11 12 8 1234 0 0 19,250 7700 11,550 7700 47,434 67.1 250 0 0 545 1555 1202 4246 2.8 0 0 7.4 18.3 10.8 58 2.9 0 0 4.1 11 12 8 16,860 0 0 19,250 7700 115,50 7700 63,060 71.0 250 0 0 545 1586 1202 4246 2.8 0 0 7.4 18.3 10.8 58 2.9 0 0 4.1 11 12 8 30,800 0 0 19,250 7700 11,550 7700 77,000 76.9

Water (ML/ha) Water (ML/ha) Yield (t/ha) Area (ha)

100%

Profit ($/ha)

Area (ha)

90%

Yield (t/ha)

Profit ($/ha)

Area (ha)

Water (ML/ha)

Yield (t/ha)

Profit ($/ha)

Area (ha)

70% 80%

Water allocation Crop

Table 4 – Optimal area and water, yield and profit for all of crops in the Coleambally Irrigation Area for scenario 1.a

This scenario demonstrates optimal benefits under full irrigation practices in achieving environmental flow and maximum total gross margins. The NLP modeling results are shown in Table 4. The results indicate that maize and soybean are not economically efficient crops even at 100% allocation level. The total economic benefit at 100% water allocation is $76.9 million while the total water use and total area is 453,145 ML and 77,000 ha, respectively. Total water use is calculated by multiplying optimized crop area (ha) with crop water use (ML ha1) obtained through optimization. However, when we reduce water allocation levels to 90%, aiming to save water for environmental purposes, total economic benefits decreased by 7.7% ($5.95 million). Similarly, when we reduce water to 60% allocation level, the total economic benefit reduced by 24.5% ($18.9 millions) while total planted area reduced by 49% (37,966 ha). Among different crops, we observed that maximum changes occur with wheat area (100%) and rice (62%). The decrease in wheat area is mainly due to lower yield levels, consequently lower gross margins compared with other crops. Similarly, significant decrease in rice area occurs as well and this is due to higher water use. Important to note here is that no change in tomato area observed at any allocation level. This is because of higher yield followed by high gross margins among all crops grown in CIA. The modeling results revealed tradeoffs between achieving environmental objectives by reducing water allocation and optimized gross margin. However, if we value the saved water at current market prices then benefits are expected to be higher than the costs involved. Assuming a smoothly functioning water market, temporary water trading prices reflect the opportunity cost of water (Khan et al., 2008a,b). The temporary water trading prices averaged during 2007–2008 was $445 ML1 (CICL, 2007). Fig. 4 shows changes in total gross margins, with and without water trading price, and water saving under different water allocation levels. We observed that selling water saving through water markets can result in increased total gross margins. For example, selling water saving achieved through water allocation reduction by 10% (45,046 ML) would result additional income of $20 millions.

Yield (t/ha)

5. Modeling outcome of various deficit irrigation scenario at system-level

Wheat Maize Soybean Pasture Lucerne Rice Tomato Total Total benefit (M$)

Yield (t/ha) Area (ha) Profit ($/ha)

Source: Khan et al. (2008a,b).

5.1. Managing climate change impact and achieving environmental flow objectives through optimization with full irrigation practices

Water (ML/ha)

0.005W3 + 0.008W2 + 0.974W 0.011W3 + 0.128W2 + 0.724W 0.005W3 + 0.079W2 + 0.029W 0.041W3 + 0.387W2 + 0.94W 0.0202W3 + 0.315W2 + 0.64W 0.0065W3 + 0.12W2 + 0.395W 0.0032W3 + 0.7895W2 + 1.145W

Water (ML/ha)

Wheat Maize Soybean Winter pasture Lucerne Rice Tomato

Production function 60%

Crop

Profit ($/ha)

Table 3 – Estimated water yield production functions derived for the major crops grown in the Coleambally Irrigation Area.

0 0 0 545 1535 1022 4246

environmental science & policy 14 (2011) 1139–1150

1146 $200

200

$160

160

$120

120

$80

80

$40

40

$0 50%

60%

70%

80%

90%

0 100%

Volume of water savings (GL)

Total gross margin ($m)

environmental science & policy 14 (2011) 1139–1150

Water allocation Without valuing saved water ($m)

With valuing saved water ($m)

Volume of water saving (GL)

Fig. 4 – Volume of water saving and the total gross margin for scenario 1 (managing climate change impact and achieving environmental flow objectives through optimization with full irrigation practices).

Total gross margin ($m)

This scenario demonstrates the usefulness of deficit irrigation to achieve water saving for environmental purposes while maximizing returns or minimizing the economic losses. Deficit irrigation analysis was performed under the assumption that crop inputs will remain the same, with full and deficit irrigation practices, other than water inputs. The results of this scenario are shown in Table 5. The results at 100% water allocations are same as shown in scenario 1. When we decrease water allocation levels to 90%, aiming to save water for environmental purposes, total economic benefits decreased by 6.8% ($5.3 million). However, when we reduce water to 60% allocation level, total economic benefit reduced by 26.6% ($20.5 millions) while total planted area reduced by 46% (35,708 ha). The decrease in economic benefits could be much lower if we reduce the input uses matched with crop water uses. At the crop level, the model efficiently utilized deficit irrigation practices and reduced the water to insensitive crops such as wheat, which also shows lower gross margins. The model did not reduce water use for tomato because of its sensitivity to water and yield, and higher gross returns compared with other crops. The decrease of crop water use has resulted 57% decrease in yield of pasture and 39% decrease

in yield of wheat, which indicates pasture is more sensitive to water than wheat. Similar to scenario 1, the model revealed tradeoffs between achieving environmental objectives and increasing total gross margin. However, valuing water saving by selling water to temporary water markets shows higher total gross margin (Fig. 5).

5.3. Scenario 3: Managing climate change impact and achieving environmental flow objectives through deficit irrigation practices without optimization The aim of this scenario is to demonstrate the usefulness of deficit irrigation practices to achieve environmental objectives after applying deficit irrigation equitably at a crop level. The results are shown in Table 6. As noted, these results indicate that maize and soybean are not economically efficient crops even at 100% allocation levels. The results at 100% water allocation are the same as shown in scenarios 1 and 2. When we decrease water allocation levels to 90%, and equally reduce crop water use from all crops to save water for environmental purposes, total economic benefits decreased by 21.8% ($16.8 million). Similarly, when we reduce water to 60% allocation level, the total economic benefit reduced by 87.3% ($67.3 million). This significant decrease is because of reducing crop water uses at very low levels to which crop returns were negative.

$200

200

$160

160

$120

120

$80

80

$40

40

$0 50%

60%

70%

80%

90%

0 100%

Volume of water savings (GL)

5.2. Managing climate change impact and achieving environmental flow through optimization with deficit irrigation practices

Water allocation Without valuing saved water ($m)

With valuing saved water ($m)

Volume of water saving (GL)

Fig. 5 – Volume of water saving and the total gross margin in for scenario 2 (managing climate change impact and achieving environmental flow through optimization with deficit irrigation practices).

Table 5 – Optimal and water, yield and profit for all of crops in the Coleambally Irrigation Area for scenario 2.a Water allocation Crop

a

90%

80%

70%

60%

Area

Water

Yield

Profit

Area

Water

Yield

Profit

Area

Water

Yield

Profit

Area

Water

Yield

Profit

Area

Water

Yield

Profit

(ha)

(ML/ha)

(t/ha)

($/ha)

(ha)

(ML/ha)

(t/ha)

($/ha)

(ha)

(ML/ha)

(t/ha)

($/ha)

(ha)

(ML/ha)

(t/ha)

($/ha)

(ha)

(ML/ha)

(t/ha)

($/ha)

30,800 0 0 19,250 7700 11,550 7700 77,000 76.9

2.9 0 0 4.1 11 12 8

2.8 0 0 7.4 18.3 10.8 58

250 0 0 545 1,586 1202 4246

26,094 0 0 19,250 7700 11,550 7700 72,294 67.3

2.7 0 0 4.1 9.7 10.6 8

2.6 0 0 7.4 17.4 9.9 58

209 0 0 545 1474 1001 4246

11,003 0 0 19,250 7700 11,550 7700 57,253

2.1 0 0 4 9.7 10.8 8

2 0 0 7.21 17.4 10.8 58

84 0 0 519 1474 1036 4246

3609 0 0 17,580 7700 11,145 7700 47,734 61.2

2.1 0 0 3.6 9.2 10.2 8

2 0 0 6.4 16.8 9.6 58

84 0 0 412 1388 926 4246

2582 0 0 15,250 7210 8550 7700 41,292

2.1 0 0 3.8 9.2 9.4 8

2 0 0 6.8 16.8 8.9 58

84 0 0 466 1388 756 4246

72.6

56.5

Scenario 2: Managing climate change impact and achieving environmental flow through optimization with deficit irrigation practices.

Table 6 – Allocated area and water, yield and profit for all of crops in the Coleambally Irrigation Area for scenario 3.a Crop

Area (ha)

Water allocation 100%

Wheat Maize Soybean Pasture Lucerne Rice Tomato Total Total benefit (M$) a

30,800 0 0 19,250 7700 11,550 7700 77,000

90%

80%

70%

Water (ML/ha)

Yield (t/ha)

Profit ($/ha)

Water (ML/ha)

Yield (t/ha)

Profit ($/ha)

Water (Ml/ha)

Yield (t/ha)

Profit ($/ha)

Water (ML/ha)

Yield (t/ha)

2.9 0 0 4.1 11 12 8

2.8 0 0 7.4 18.3 10.8 58

250 0 0 545 1586 1202 4246

2.6 0 0 3.7 9.9 10.8 7.2

2.5 0 0 6.6 17.6 10.1 48

189 0 0 439 1502 1036 2904

2.3 0 0 3.3 8.8 9.6 6.4

2.2 0 0 5.7 16.3 9.1 38.8

127 0 0 330 1304 800 1687

2 0 0 2.9 7.7 8.4 5.6

1.9 0 0 4.9 14.4 7.9 30.6

76.9

60.2

42.5

24.5

60% Profit ($/ha) 63 0 0 220 1019 512 596

Water (ML/ha)

Yield (t/ha)

Profit ($/ha)

1.7 0 0 2.5 6.6 7.2 4.8

1.7 0 0 4 12 6.6 23.3

6.9 0 0 110.6 673.8 189.9 0

environmental science & policy 14 (2011) 1139–1150

Wheat Maize Soybean Pasture Lucerne Rice Tomato Total Total benefit (M$)

100%

9.72

Scenario 3: Managing climate change impact and achieving environmental flow objectives through deficit irrigation practices without optimization.

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1148 $200

200

$160

160

$120

120

$80

80

$40

40

$0 50%

60%

70%

80%

0 100%

90%

Volume of water savings (GL)

Total gross margin ($m)

environmental science & policy 14 (2011) 1139–1150

Water allocation Without valuing saved water ($m)

With valuing saved water ($m)

Volume of water saving (GL)

Fig. 6 – Volume of water saving and the total gross margin in for scenario 3 (managing climate change impact and achieving environmental flow objectives through deficit irrigation practices without optimization).

With equally reducing water at crop level, we observed reduction of lucerne and wheat (35%) and the maximum decrease in tomato yield (60%). These results show that tomato is more sensitive to water stress. Similarly, we observed minimum decrease in wheat profit and maximum decrease in tomato profit. Similar to scenarios 1 and 2, valuing water saving by selling water to temporary water markets shows higher total gross margin (Fig. 6).

Optimization with full irrigation was found to be better than reducing crop water use equally from all crops. This is because some crops were more sensitive to water stress and showed negative returns when crop water use was reduced to 60% such as tomato.

5.4.

Irrigators are constantly under pressure to increase water use efficiency due to lower water supplies as a result of climate change and increased water prices. Water saving technology has the potential to decrease water use but may not be economically efficient due to additional capital and operating costs outweighing any tangible benefits. Water use efficiency can possibly be increased without the need to change irrigation systems by changing from the current irrigation management practice of full irrigation to the practice of deficit irrigation. With climatic change, drought, potential ongoing reductions in seasonal water allocation, and demand for environmental water, the economic optimum level of irrigation will be less than would be required for maximum yield. State and Federal governments are increasingly reliant on the

Comparison of three scenarios

A comparison of three scenarios has been shown in Fig. 7. Among the three scenarios, deficit irrigation with optimization demonstrated better results despite the fact that input level were maintained the same for full and deficit irrigations. To maximize total revenue and increase water saving under climate change, there is an optimum trade-off between the amount of irrigation water each crop receives and the total area of each crop to be irrigated. When irrigation is constrained by water availability, total production or total returns can be increased as the water savings from deficit irrigation can be used to irrigate additional land or can be sold in water markets.

6. Scope for deficit irrigation in meeting climate and environmental challenge

Total grass margin ($m)

$140 $120 $100 $80 $60 $40 $20 $0 50%

60%

70%

80%

90%

Water allocation S1: without valuing saved water ($m) S2: without valuing saved water ($m) S3: without valuing saved water ($m)

Fig. 7 – Comparison of total gross margin of the 3 scenarios.

S1: with valuing saved water ($m) S2: with valuing saved water ($m) S3: with valuing saved water ($m)

100%

environmental science & policy 14 (2011) 1139–1150

new innovative tools for restoring environmental balance in the Murray–Darling Basin. The changing water and environmental policy requires modified conceptualisations of market instruments as a vehicle delivering water back to the environment. We demonstrate the potential of deficit irrigation practices an effective strategy for reducing climate change impacts and achieving environmental objectives. To demonstrate this we compare three scenarios: optimization with full irrigation, optimization with deficit irrigation and deficit irrigation without optimization. A non-linear optimization model, which uses crop production function and profit functions, was used to evaluate the performance of deficit irrigation. The modeling results revealed tradeoffs between achieving environmental objectives by reducing water allocation and optimized gross margin. However, if we value the saved water at current market prices then benefits are expected to be higher than the costs involved. Among the three scenarios, deficit irrigation with optimization demonstrated better results even though same input levels were used for full and deficit irrigations. Reducing water equally may not be the best option as some crops show more sensitivity to crop water stress. Optimization with full irrigation was found to be better than reducing crop water use equally from all crops. A deficit irrigation management strategy to water use efficiency appears to have been overlooked, especially for broadacre crops, even though it has the potential for increased water use efficiency thus potential for water saving under increasingly lower water availability due to climate change and possibly without the need for any significant capital investment to implement. Deficit irrigation can even become more economically attractive when the value of alternative water use increases or cost of water increases. There is a growing policy interest in reducing water use in agriculture when this generates sufficiently large environmental benefits and increases the well-being of other water users. To achieve a more sustainable balance of water in the Murray Darling Basin, the Federal Australian Government Water for Future Policy, under which billions of dollars has been committed to buy back existing water entitlements from willing irrigators. Like most market-oriented policies designed to purchase water for the environment, the buyback of water depends on the voluntary participation of irrigators throughout the MDB. Wider adoption of deficit irrigation systems and participation in buy back policy would result better environmental outcome. This study illustrates the potential for significant efficiency gains that could be achieved with deficit irrigation. However, it is important to note that this study may over-estimate the potential gains of deficit irrigation as the analysis did not incorporate any negative quality impacts on crop production.

Acknowledgements The authors greatly thank Professor Roger Stone and Dr. Jerry Maroulis of the University of Southern Queensland, Australia, for technical and financial assistance in preparation of this study.

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