When is the price right? 1

Transcription

1 Austraian Agricutura and Resource Economics Society (AARES) 51 st Annua Conference February 2007 Rydges Lakeand Resort, Queenstown, New Zeaand When is the price right? 1 Mary-Ann Franco-Dixon 2, Coin G. Brown 3 and Pau C. Riethmuer 4 1 The views expressed in this paper are those of the primary author and do not necessariy refect the views of the organisations she represent. 2 Schoo of Natura and Rura Systems Management, University of Queensand, St Lucia, Brisbane Qd 4072 Austraia and the Department of Primary Industries, 80 Ann Street, Brisbane Qd 4000; Te No ; Fax No ; Emai Address: Mary-Ann.Franco-Dixon@uq.edu.au 3 Schoo of Natura and Rura Systems Management, University of Queensand, St Lucia, Brisbane Qd 4072 Austraia, Emai Address: Coin.Brown@uq.edu.au 4 Schoo of Economics, University of Queensand, St Lucia, Brisbane Qd 4072 Austraia, Emai Address: P.Riethmuer@uq.edu.au

2 1. Introduction One key component of water suppy reguation and aocation and one of the compex underying issues in many water poicy decisions is the setting of water prices for a its uses. In setting the price of water, the frequenty asked question is whether the set price is right. But when is the price of water exacty right? As has been shown in various iterature the price of water is said to be right if it has achieved what it was initiay set for. Water pricing coud be set to meet three main purposes such as: financia - to cover capita investment and operation and maintenance (O&M) costs of water services; efficiency - to emphasise among users the intrinsic vaue of resources and deivery systems and to discourage water wastage, strengthen institutiona capacities and improve quaity of services; and equity - to reduce gaps in income distribution and thereby achieve socia justice. In the iterature, two different opposing schoos of thought emerge with respect to water pricing. According to one schoo of thought, as represented in the findings of the Industry Commission (1992), the provision of irrigation water is heaviy subsidised because prices for irrigation water fa short of covering the costs to governments of buiding, managing and maintaining dams and distribution systems which suppy water. This view is endorsed by authors such as Aexandra and Fisher (1995). Watson (1995), however, argued that the roe for the price mechanism in rationing water shoud be based on the scarcity of water and not cost recovery. Athough the pricing poicy in Queensand was estabished primariy to recover costs of water service or deivery, in future cacuation and setting of water prices, the issue of the water user s capacity to pay as was raised by irrigators during consutations is acknowedged. This paper evauates the irrigator s capacity to pay by ooking at different water price eves and how four representative farms with their different and sizes and water aocations adjust to these water price eves. Some of the many factors that can infuence the irrigators capacity to pay such as variabiity of weather, water avaiabiity; product prices and debt eves are not incuded in this paper. 51 st AARES Annua Conference Queenstown New Zeaand, February

3 2. Integrated modeing approach The main objective of this paper is to provide an indication of the on-farm financia impacts of aternative water price eves and thus the irrigator s capacity to pay. It is recognised that any anaysis needs to refect differences in the physica and financia characteristics of farms in the Emerad Irrigation Area. The magnitude of adjustments to some poicy changes is such that it might easiy threaten farm financia viabiity. Viabiity effects can be best assessed in a whoe farm budgeting framework or inear programming modes. A inear programming mode was deveoped using the integrated economicbiophysica-hydroogic framework (Figure 1). This farm eve integrated biophysicaeconomic-hydroogic mode was used to quantify the direct farm-eve economic impact of water pricing scenarios. This mode aows for the estimate and anaysis of water demand under aternative poicy scenarios of different water aocation eves and water pricing regimes. This mode is a short-run mode that incudes different crop production techniques, different irrigation techniques and aow for the incusion of variabes that refect different eves of management. Mathematica programming is a robust methodoogica approach that can determine the economic impacts of water poicy changes in agricuture by determining optima activity and optima resource input eves. Linear programming has been the method of choice in numerous researches on water in Austraia and overseas because of the fexibiity in accommodating research probems with huge size and high dimensions. 2.1 Bio-physica component As shown in Figure 1 weather factors such as dayength, temperature, faow and incrop rainfa and evaporation were the primary inputs to both OZCOT and APSIM. Using Rainman, weather data was generated for the years 1900 to 1995 using the Emerad Post Office data. Agronomic factors such as panting dates and soi types were aso incorporated to demonstrate differences in yied response under different crop water use. The mode outputs of a combination of crop yied and crop water use provides the crop water functions of the different activities which is the different 51 st AARES Annua Conference Queenstown New Zeaand, February

4 eves of crop yied under various irrigation water eves. These crop water functions are then incuded in the inear programming Economic Mode. 2.2 Hydroogic component The stochastic water suppy was captured through the hydroogica simuation mode. As shown in Figure 1, inputs such as upstream and catchment infow, tota rainfa, evaporation, diversion, seepage and osses were incorporated in the Integrated Quaity and Quantity Management (IQQM) mode to generate the monthy streamfow data for 96 years (1900 to 1995). This streamfow data then inputs into the inear programming mode. 2.3 Economic component The inear programming economic mode brings together the output data from OZCOT, APSIM and IQQM mode incorporated with institutiona, agronomic, physica and economic factors to achieve optimisation of farm net revenue. The outputs generated by the inear programming mode as shown in Figure 1 are optima net revenue; optima water used, optima area used, optima crop mix, optima abour used and optima tractor hours 51 st AARES Annua Conference Queenstown New Zeaand, February

5 Figure 1. Integrated modeing 51 st AARES Annua Conference Queenstown New Zeaand, February

6 3. Farm-eve inear programming mode structure The mode deveoped in this study is an optimising farm mode with cotton, sorghum, wheat and chickpeas. The inear programming approach was adopted to deveop the farm eve mode using Genera Agebraic Modeing Systems (GAMS). It uses data on avaiabe and, water requirements per unit and area for different crops and net revenue per unit of and area, generated by the growing of those crops. This net revenue is cacuated by deducting variabe costs and payments for water (or water costs) from gross revenue. The mode takes the exogenous variabes of water price for each of the farm types and generates endogenousy the cropping pattern and choice which maximises net farm revenue. The water prices are then changed and the GAMS mode re-soved severa times to construct a demand function for each Farm Type and for the tota water avaiabe. The water price was parameterised from the current charge to increasing tota charges by increments of $10 which is proportionay added to the Part A 5 and Part B 6 of the water price. For exampe, the water price of river suppemented water as shown in Tabe 1, has risen by an increment of $10 in price scenario 1 but this $10 was proportionay distributed to the Part A - $6.20 (which is 62 per cent of water price) and Part B -$3.80 (which is 38 per cent of water price). In addition to assessing effects of different water price eves, the mode was aso used to examine the effects of changes in water quantity aocations on optima crop combinations. Tabe 1. Water price for river suppemented water in Emerad Irrigation Area Water charges PSB PS1 - $10 PS2 - $20 PS3 - $30 PS4 - $40 Tota price(increment) Part A 6.16 (62%) (6.20) (12.40) (18.60) (24.80) Part B 3.75 (38%) 7.55 (3.80) ( 7.60) (11.40) (15.20) Note: Part A water charge or access charge or fixed charge Part B water charge or voumetric charge or variabe charge PSB is price scenario base case PS1 is price scenario 1, PS2 is price scenario 2, PS3 is Price Scenario 3, PS4 is Price Scenario 4 5 Part A water charge is referred to as access charge or fixed water charge or entitement charge. Part A charge is payabe for each megaitre (ML) of water entitement or aocation. 6 Part B water charges is referred to as voumetric charge or variabe water charge or usage charge Part B charge is payabe for each megaitre (ML) of water used under the water entitement. 51 st AARES Annua Conference Queenstown New Zeaand, February

7 The water charges that are used in the study reate to the current water poicy which does not price water at its margina or resource cost. Current water charges refect costs of storage, repair and maintenance, water deivery and drainage. 3.1 Objective function The objective function of the farm inear programming modes is to maximise net revenue or profit at the farm eve in the Emerad Irrigation Area by seecting the optima mix of water-consuming crop production activities such as cotton, sorghum, wheat and chickpeas. It is assumed in the anaysis that irrigators are risk neutra and mainy profit maximisers. Maximising profits is the objective of the inear programming mode and this requires some parameters such as genera costs (such as panting costs, harvestings costs, herbicide costs, insecticide costs and others), depreciation and other costs that are specific to each decision variabe. Cacuating these parameters coud be very subjective and is difficut because of the mass of data needed. It is therefore assumed in this study that gross margin is a good estimator of profit or revenue (Berbe and Gomez-Limon 2000 and Gomez-Limon et a. 1996)) and that the maximisation of profit is equivaent to the maximisation of gross margins (revenue ess variabe costs). The genera representation of tota gross margins is: minus - equas = Gross Income Variabe Costs Gross Margin Thus, the GAMS mode cacuated the gross margin or net revenue. Tota costs, tota yied, area panted, tota variabe costs and tota water reated costs were aso cacuated by the mode. The net revenue was cacuated by deducting tota crop costs (cotton, sorghum, wheat or chickpeas production costs) from tota revenue. The objective function equation used in the inear programming mode is shown in Equation 1. This is equivaent to the maximisation of tota net private (farmer) economic benefits such as the net revenue. 51 st AARES Annua Conference Queenstown New Zeaand, February

8 maxπ where: = 4 c= 1 w= 1 s= 1 e= 1 t= c= 1 w= 1 s= 1 e= 1 t= Χ Χ [( Y ( VC P ) 1+ ( Y c + WC ) P ) 2.2] cs Equation 1 c w s e π is crop type (irrigated or raingrown soid, singeskip or doubeskip) is irrigation water source (river or channe suppemented or unsuppemented) is the soi type (based on the soi water hoding capacity) is irrigation efficiency as indicator for irrigation technoogy (89 per cent, 95 per cent, 99 per cent) is the different eves of irrigation water appication (0 to 10 ML per hectare) is profit from the management scenario Χ csti are hectares of crop c, soi s, water eve, panting time t and irrigation type i under management scenario m Y are yieds for irrigation eve associated with c, w, s, e and t P c activities is the price of cotton int P cs is the price of cotton seed WC is irrigation reated costs for irrigation eve associated with c, w, s, e and t activities VC are variabe costs associated with c, w, s, e and t activities The variabe costs and irrigation water reated costs vary from one decision variabe to another. The seven parts of the irrigation reated costs are cacuated based on Equation 2. WC = IOC + IRSWC + IRSEC + ICSWC + ICSEC cswet + IUSWC + IUSEC Equation 2 51 st AARES Annua Conference Queenstown New Zeaand, February

9 where: IOC irrigation operating costs IRSEC eectricity cost for pumping river suppemented water ICSEC cswet eectricity cost for pumping channe suppemented water IUSEC eectricity cost for pumping unsuppemented water IRSWC river suppemented water cost ICSWC channe suppemented water cost IUSWC unsuppemented water cost where: IRSEC ICSEC IUSEC = PIRSWU = PICSWU cswet = PIUSWU ECIRS ECICS ECIUS 3.2 Constraints Water aong with and is one of the usua constraints incuded in a inear programming mode. Other farm constraints such as abour, fertiiser, equipment and others were hed constant. This is because the focus of this study is to determine the farm eve effects of changing water prices and quantity of aocation. In order to ensure that no other constraint is infuencing the optimisation resuts, ony water and and were incuded. The first set of constraints buit in this mode is the avaiabe irrigation water with three water types based on the water suppy sources of river suppemented suppy, channe suppemented suppy and unsuppemented suppy. Water constraints are generay written in the form shown in Equation 3. W ij X j W i Equation 3 where: Wi is the tota avaiabe amount of water type i. 51 st AARES Annua Conference Queenstown New Zeaand, February

10 In this mode, Equation 4 was used as the water constraint equation c= 1 w= 1 s= 1 e= 1 t= ( PIRSW + PICSW + PIUSW ) W Equation 4 where: PIRSW River suppemented water use PICSW Channe suppemented water use PIUSW Unsuppemented water use W Tota avaiabe water or tota water aocation at irrigation eve The second constraint buit into the mode is avaiabe irrigabe and of four different soi types such as auvia, downs, scrub and dupex which have equivaent water hoding capacity of 200 SWHC 7, 300 SWHC, 250 SWHC and 150 SWHC respectivey. The genera form of the and constraint is show in Equation 5. X jk A k Equation 5 where: k is the soi type isted previousy X jk is the area of activity j in soi type k A k is the tota area avaiabe for soi type k. The constraints ensure that the sum of the areas of the crops under each category k wi not exceed the area avaiabe for that category. 7 SWHC Soi avaiabe water hoding capacity. Each soi type is characterised by its water hoding capacity. This is the maximum voume of water that a specific type of soi can hod and woud be the amount of water avaiabe for the crop to use. 51 st AARES Annua Conference Queenstown New Zeaand, February

11 The and constraint is expressed in Equation 6 as: Χ c= 1 w= 1 s= 1 e= 1 t= A Equation 6 where: A is tota area avaiabe at irrigation eve 4. The data for the mode Data was sourced from a combination of data resuts gathered through a survey of cotton growers in the Emerad Irrigation Area in September 2004, the use of statistica data from the Austraian Bureau of Statistics (ABS) and the Austraian Bureau of Agricutura and Resource Economics (ABARE), information gathered by various government departments and organisations such as SunWater; farmers; and farmer organisations. Data were vaidated through informa consutation with oca farmers and representatives of organisations invoved in agricuture and water management in Emerad region. 4.1 Current nomina and announced aocation The irrigation water that farmers have avaiabe to them is dependent on both the nomina water aocation of each irrigator as we as the announced aocation. In Queensand, in-stream water aocation is predominanty singe-voumetric. Under a singe-voumetric water aocation, the authorities specify a nomina amount of water to each icensee. However, the actua quantities of water aocated each water year are imited by the water suppy. Thus, at the commencement of each water year (which is 1 st Juy), the water authority, having assessed the suppy of water and after considering the suppy to high reiabiity demand from industry, manufacturing and urban and town suppies, announces the amount of water that the authority coud actuay suppy for irrigation as a percentage of each farm's nomina aocation. The farm inear programming mode used the tota of suppemented and unsuppemented water as the threshod of water avaiabe (Tabe 2). The current suppemented water aocation for each farm type was then varied according to the water aocation scenarios. 51 st AARES Annua Conference Queenstown New Zeaand, February

12 Tabe 2. Tota water avaiabe in a Farm Types by water source Water suppy source Farm Type A Farm Type B Farm Type C Farm Type D ML ML M ML River suppemented water Channe suppemented water Unsuppemented water Overand fow Tota water Source: September 2004 survey of cotton farmers Tabe 3 shows the water aocation used in the farm inear programming mode assuming that the water aocation of suppemented water is 100 per cent. Unsuppemented water was eft constant when avaiabe in the farm type because the voume of water from this source is not affected by the avaiabiity of water from suppemented source coming from the dams, weir and channes. Tabe 3. Suppemented water aocation by scenarios Scenarios Water aocation Farm Type A Farm Type B Farm Type C % ML ML ML Base Case Note: Farm Type D is not incuded in the tabe because tota water suppy in this Farm Type is from overand fow The anaysis incudes the water use for the various crops based on the crop-yied reationship for each of the crops. The GAMS mode cacuated how much tota water was required by the crops in each farm based on the optima use of water. 4.2 Water price The water prices for the different sources of water suppy, shown in Tabe 4, were used in the mode as the base case. These prices were set by the Department of Natura Resources, Mines and Water 8 for the Emerad Irrigation Area under the Rura Water Pricing Direction Notice (No. 01) In October 2000, the Queensand government set five to seven-year price paths to ensure the majority of the irrigation schemes reached at east minimum financia viabiity by The price schedues were used because the production, prices and costs data gathered from the September 2004 survey were based on financia year The water charges set for the Emerad Reguated Section and the Emerad Channe 8 The current Department of Natura Resources Mines and Water, (NRMW) was aso known as Department of Natura Resources and Mines (NR&M) and Department of Natura Resources (DNR) 51 st AARES Annua Conference Queenstown New Zeaand, February

13 under the Rura Water Pricing Direction Notice 2000 were up to and have been the same water charge from the 2 nd year of the price path in The Department of Natura Resources, Mines and Water together with SunWater are currenty ooking at revising this price path. Tabe price schedue for the Emerad Irrigation Area Water suppy source Part A Part B Tota $/ML $/ML $/ML River Suppemented (Emerad Reguated Section ) Channe Suppemented Water (Emerad Channe) Unsuppemented Water Source: Department of Natura Resources and Mines Crop and yied The crops anaysed are cotton, sorghum, wheat and chickpeas. Crop yieds per hectare for cotton, sorghum, wheat and chickpeas were obtained from the resuts generated by the crop production modes OZCOT and APSIM. These estimated yieds refect the different possibe water avaiabiity eves based on various eves of water aocations; different panting dates; soi types; irrigation technoogies; and water eve appication. This information was inked to the inear programming mode to determine the irrigation eves for various soi types which maximise farm profitabiity or net revenue. The anaysis aso assumes present and recent historica data refect current and future irrigation in the region and therefore cropping patterns. In addition, where the area of and for irrigation is reduced due to the authorities aocating a ess than nomina aocation of water to farmers, it is assumed that the farmers use the and for raingrown or dryand agricutura production. Where farmers increase the area of and under irrigation due to an expected greater than nomina aocation, the mode assumes the and had been previousy used for dryand agricutura production. 4.4 Crop price and costs data Tabe A.1 in Appendix A shows the crop prices used in the inear programming mode. The information on cotton int and seed price is based on the September 2004 survey of cotton farmers whie the crop prices for sorghum, wheat 51 st AARES Annua Conference Queenstown New Zeaand, February

14 and chickpeas were from data provided by the Department of Primary Industries and Fisheries (2005). Prices quoted for farm costs are aso based on growers survey as we as industry benchmark figures (Tabe A.2). Other costs shown in Tabe A.3 and Tabe A.4 incude the costs for usua practices for herbicide, insect, pest and disease contro requirements for we-managed crop in an average season. We-managed crops are crops grown using necessary herbicides, insecticides and pesticides; irrigated with the minimum water requirement and panted in suitabe soi type. Average season is a season with adequate rainfa minus the extremes of drought, food or hai. A costs are based on the prices of the inputs paid by irrigators in Emerad Irrigation Area. The owner s abour costs are excuded. 5. Farm eve production mode resuts for Emerad Irrigation Area The farm eve mode was deveoped to simuate mixed cropping using parameters refecting current practices in the Emerad Irrigation Area. In the farm eve mode, it was assumed that the irrigation system used is surface irrigation, specificay furrow irrigation. This is the case for most of the Emerad Irrigation Area farms. It is assumed in this farm eve mode that farmers are profit maximisers. To maximise their net revenue, the optima eves of irrigation are determined for each soi type, water source and irrigation eve. Based on the estimates done by Key and Anderson (2004), the proportion of soi types in Emerad Irrigation Area used for the farm eve mode are Auvia 65 per cent, Downs 13 per cent, Scrub 12 per cent and Dupex 10 per cent. Net revenue was then cacuated by deducting tota costs from tota revenue. Four mixed crop farm eve inear programming modes were generated depending on the farm types anaysed. This was based on a more reaistic assumption that a mixture of crops is grown simiar to some combinations of crops grown in the Emerad Irrigation Area. The cropping activities presented in the farm eve mode are cotton, sorghum, wheat and chickpeas production. Price data for cotton saes and variabe costs data for cotton were sourced from the September 2004 survey of cotton growers in the Emerad Irrigation Area. Cotton yied data were sourced from the crop production simuation 51 st AARES Annua Conference Queenstown New Zeaand, February

15 using OZCOT whie sorghum, wheat and chickpeas yied data were sourced from the crop production simuation using APSIM. As shown in Tabe B.1 in Appendix B, the highest mean cotton yied from the OZCOT simuation for the 95 year simuation occurred for panting dates between 1 November and 1 December. This is considered ate panting in Emerad because it is riskier in terms of insects and pests probems. These yieds were higher than those crops panted on the 1 October which was supposed to be in the window of conventiona panting of ast week of September to 1 st week of October. For this reason, in buiding the farm eve mode, the yied from the 1 November panting date simuation was used in the inear programming mode. This discrepancy in the more idea panting date coud be attributed to OZCOT not caibrated for osses due to insects and pests. But in order to be consistent in choosing the panting dates for a the crops, the highest mean yied panting date for the 95 year simuation was chosen. The highest mean sorghum yied from APSIM simuation for the 95 year simuation was highest for panting dates 1 November and 1 December (Tabe B.2). This simuation resut for sorghum is consistent with works done by the Department of Primary Industries and Hammer et a. (2002). The yied from the 1 December was used in the inear programming farm eve mode. Tabe B.3 shows that wheat yied is highest when panted during the months of Apri and May. These yied resuts from the APSIM simuation is consistent with resuts obtained by Hammer et a. (2002) and with what is found to be true in the fied based on the Department of Primary Industries and Fisheries resuts. Chickpeas yied was highest during the months of Apri and May as shown in Tabe B.4. This is exacty the same as the resuts for wheat. Wheat and chickpeas are both winter crops and the optimum resuts occur when the temperature are ow. Error! Reference source not found. shows the gross margins of a the crops as an indicator of farm profit based on 2003 and 2004 price data. 51 st AARES Annua Conference Queenstown New Zeaand, February

16 Tabe 5. Gross margins of the crops grown Crops Gross Margins $/ha Irrigated cotton Raingrown cotton soid 833 Raingrown cotton singe skip 763 Raingrown cotton doube skip 692 Irrigated sorghum 945 Raingrown sorghum 381 Irrigated wheat 996 Raingrown wheat 522 Irrigated chickpeas 772 Raingrown chickpeas 427 Source: September 2004 survey of cotton growers, Department of Primary Industries, persona communication with growers. Tabe 6 shows that for a farm types, the optima soution from the mode resuts in a farm growing monocuture cotton in summer with minimum chickpeas grown in winter. Sorghum and wheat did not come into the optima soution because of the imited avaiabe abour in winter with the cotton and preparation competing with wheat panting. Because of the higher cotton gross margin, the mode seects to grow cotton over wheat and winter abour is used for cotton and preparation instead of panting wheat. Panting chickpeas becomes an option in the modes since in trying to optimise the net revenue, the mode uses whatever amount of water is eft avaiabe for other crops after panting and irrigating the optima and area to cotton. This conforms with current practices observed since irrigated sorghum, wheat and chickpeas occur as opportunistic production in Emerad. This refects the situation in the Emerad Irrigation Area where cotton farmers grow cotton as the primary summer crop and do not faow as a norma management practice. Chickpeas, if irrigated with ony 1 ML of water per hectare, yied around 2 tonnes per hectare. Assuming a crop price of $490 per tonne, the tota revenue for chickpeas per hectare is $980. To produce the same tota revenue, cotton yied has to be around 1.8 baes per hectare assuming a particuar cotton price and at east have a minimum irrigation of 1 ML of water per hectare. Assuming that the abour constraint is constant, cotton is expected to be chosen over chickpeas. However, the tota costs to produce cotton are higher than to produce chickpeas. Thus, the optima soution uses the ast voume of water for chickpeas. Another intuitive resut is for the farmer to keep the 10 per cent of the and it pants to chickpeas in faow. Key and Anderson (2004) attribute the ow faowing rate in the 51 st AARES Annua Conference Queenstown New Zeaand, February

17 Emerad Irrigation Area to the andocked situation in the area. Given a certain amount of water aocation, farmers woud not be abe to easiy expand and panted to cotton or any other crop because of the fixed or finite and avaiabe to them. Thus, they keep panting the maximum irrigated and avaiabe year after year and woud ony invountariy faow if water is not avaiabe to pant cotton or any winter crop or when it becomes uneconomica to grow cotton under ow water avaiabiity scenarios. Due to the high water cost and the higher returns from cotton production, it is expected that cotton is the crop of choice if adequate water is avaiabe to irrigate. Tabe 6. Base case crop production Farm type/crops Tota revenue Farm costs Water costs Part A Part B* Net Revenue ($) ($) ($) ($) ($) Farm type A (222 ha) Farm type B (960 ha) Farm type C (1100 ha) Farm type D (580 Ha)** Note: *Part B water costs and irrigation operation costs are incuded in the cacuation of farm costs. **Water suppy from overand fow is assumed to have zero water cost and pumping cost. Tabe 7 shows the modeing resuts for the mixed crop mode in terms of the tota area, optima and area, and the per cent of optima and area panted to cotton or other crops. The mode was constructed as a singe season mode but with abour constraint divided into summer and winter abour. The seasona abour requirements of cotton, sorghum, wheat and chickpeas were set in the mode and is the variabe driving the seasonaity of growing these crops. As mentioned earier, Emerad does not have a distinct summer and winter cropping for irrigated crops. A of the inear programming modes for a the farm types grow irrigated cotton for summer cropping with irrigated chickpeas as the winter crop grown. Winter cropping in the Emerad Irrigation Area occurs when there is not enough water to pant and irrigate the tota and area to cotton. In this situation whatever water is not used in growing cotton is then saved and used for winter cropping. The mode cacuates this optima combination of summer and winter cropping. The percentage of irrigated cotton grown over irrigated chickpeas ranged from 77 per cent to 90 per cent depending on the farm type. The smaer farm type A had 51 hectares (23 per cent) of its and panted to chickpeas for winter, farm type B had 96 hectares, farm type C had 132 hectares and farm type D had 104 hectares. Thus, chickpeas production was minima with the area of and panted to chickpeas ony about 50 to 132 hectares with an 51 st AARES Annua Conference Queenstown New Zeaand, February

18 average of 96 hectares. Thus the mode optimises by panting sma areas to chickpeas based on the extra water from the production of cotton. This coud easiy occur especiay in farm types B and C where water aocation was high but with a imited and to pant more cotton. Tabe 7. Optima farm use for mixed-crop production each farm type Farm type Tota Area Optima and use Irrigated Irrigated cotton chickpeas Farm revenue ha ha % ha ($) Farm type A Farm type B Farm type C Farm type D Source: Linear programming mode resuts Another distinct characteristic of Emerad cotton growing is that raingrown cotton is not a common aternative to and use if there is not enough avaiabe water. Cotton farmers interviewed during the consutation a said that raingrown cotton growing is not profitabe for the Emerad area. This is mainy because in extreme cimate conditions, it is expected that rain woud not come when needed. In contrast, Daring Downs cotton farmers do grow raingrown cotton. When the announced water aocation is ow at the beginning of the water year, some Daring Downs cotton farmers take the risk and sti pant the same cotton area and hope that rain wi come to ease the water stressed pants. Tabe 8 shows that the base case mode runs for a farm types - which a have a predominanty cotton production - have the same pattern of water aocation usage. In a of the farm types, 100 per cent of the water aocation was used for crop production. Ninety-six per cent of the channe water aocation was used for cotton production in farm type A and 96 per cent of the overand fow in farm type D was aso used for cotton production. When both river suppemented water and unsuppemented water are avaiabe as in the case of farm type B, a of the river suppemented water was used for cotton production and 96 per cent of the unsuppemented water was used for the water requirement of the cotton and the rest for chickpeas. 51 st AARES Annua Conference Queenstown New Zeaand, February

19 Tabe 8. Tota water used as percentage of tota aocation IRS ICS IUS Overand Fow Tota Water Tota Water Tota Water Water Tota aocation Used aocation Used aocation Used Used ML % ML % ML % ML % Farm type A Cotton (Summer) Chickpeas (Winter) 51 4 Farm type B Cotton (Summer) Chickpeas (Winter) Farm type C Cotton (Summer) Chickpeas (Winter) Farm type D Cotton (Summer) Chickpeas (Winter) Source: Linear programming mode resuts The usage pattern for cotton resuting from the inear programming mode is consistent with farm practices in Emerad where farmers with unsuppemented water aocation use this water source first before ordering water through the suppemented system. Unsuppemented aocation is based on the river fow conditions and when unsuppemented water becomes avaiabe as the in-stream water reaches some threshod eve, water must be harvested or it wi be an opportunity ost. When there is some water stored in on-farm dams from unsuppemented water harvesting, it is a common practice to use this first or osses from evaporation and seepage wi occur. The avaiabiity of three water sources in farm type C shows that 100 per cent river suppemented and channe suppemented and 94 per cent unsuppemented water were a optima sources of water for cotton. 6. Farm adjustment responses to changing water prices One of the determinants of the impact of water charges on the profitabiity of irrigation farms reates to the types of adjustment responses that irrigators woud adapt to water price increase. The adoption of an adjustment strategy is cosey reated to the concept of easticity of demand. The price easticity of demand 9 for water is defined as the percentage change in quantity of water demanded that occurs in 9 Demand is said to be eastic when the easticity is greater than one (quantity changes proportionay more than price) and ineastic when the easticity is ess than one (quantity changes proportionay ess than price) (Jayasuriya et a. 2001). 51 st AARES Annua Conference Queenstown New Zeaand, February

20 response to a percentage change in price. The demand for water is a derived demand based on the vaue of water as an input into agricutura production and as such the vaue of water is dependent on the profitabiity of the crops to which it is appied (Jayasuriya et a. 2001). 6.1 Demand curves for irrigation water using two-part pricing Two-part pricing is one of the voumetric approaches in water pricing. It invoves setting a water access charge as part A which farmers pay as a fixed charge regardess of the voume of water used. The part B charge is the variabe charge that depends on the actua voume consumed by the farmer. The mixed crop mode was run for the four farm types to evauate the response of agricutura production to increase in water prices ranging from $10 to $600 per ML using the two-part pricing approach. The resuts generated for the four farm types by the inear programming mode are shown in Tabe C.1 to Tabe C.4 in Appendix C. The proportion of crop in farm type A is the same for a water price eves. A the water aocation was consumed for 100 per cent of the irrigated and. Farm type A is a smaer cotton property with 222 hectares of and panted to cotton. For a farm such as this the optima use of the water was to use a of its water to irrigate a of its and in order to obtain the same revenue. At the highest water price eve of $600 per ML, farmers continued panting the same area of and for cotton and irrigating with the same amount of water. The interpretation of this resut is that inear programming wi sove for a $0 optima soution where the most profitabe aternative for the farmer is to produce nothing. The negative resuts in this mode are based on the way this mode was set where part A charge is deducted after inear programming finds the optima soution for the particuar scenario. This approach was taken since part A charge is a fixed cost incurred by the whoe farm. The mixture of crops panted in farm type A for the base case is shown in Tabe 9. In this farm type, irrigated cotton was the ony crop panted except for 51 hectares of chickpeas. Irrigated cotton was panted in three soi types with soi type 1 yieding a gross profit of $ (81 per cent of tota gross profit). Looking at the pant avaiabe water capacity among the soi types, soi type 2 is expected to have higher 51 st AARES Annua Conference Queenstown New Zeaand, February

21 cotton yieds but as was shown in the OZCOT resuts in bio-physica simuation chapter, this was not necessariy true since yied is not ony a function of the pant avaiabe water capacity but aso of a range of agronomic parameters such as starting water, panting date as we as pant variety among others. In this mode soi type 2 was used to pant chickpeas. Tabe 9. Crops in farm type A PSB Has Water Yied Tota Revenue Tota Cost Gross Profit IC.ICS.IE1.T4.SWHC1.R IC.ICS.IE1.T4.SWHC1.R IC.ICS.IE1.T4.SWHC3.R ICh.ICS.IE1.T3.SWHC2.R ICh.ICS.IE1.T3.SWHC4.R Tota Source: Linear programming mode resuts Note: IC- irrigated cotton, ICh irrigated chickpeas, ICS irrigated channe suppemented, IE1 food irrigation, T3 and T4 conventiona panting, SWHC1 auvia, SWHC2 Downs, SWHC3 Scrub, R7 water eve of 7 ML per hectare The demand curve for farm type A shown in Figure 2 is perfecty ineastic. This means that the quantity of water demanded remains the same even if the price of water progressivey increases to $600 per ML of water. The size of the farm in this particuar farm type is a significant factor in this management decision. Because the farm is sma with ony 222 ha, the farmer continued to pant the same and area to get simiar yieds to cover the increasing production costs. Water price per ($/ML) Water demand (ML) Figure 2. Irrigation demand curve for farm type A 51 st AARES Annua Conference Queenstown New Zeaand, February

22 In farm type B, the tota optima and area remained as 960 hectares of fuy irrigated and and the optima use of water was ML for water price eves of $10 to $300 as shown in Tabe C.2 in Appendix C. From a price eve of $310, the mode resuts show various ways in which the farmer coud adjust. In many of the water price anaysed, there is change in tota farm water use and tota and irrigated for cotton. However, crop mix and irrigation eves change in the determining the optimum soution for the modes. At a price eve of $310, the water used decreased by 13 per cent to ML whie tota irrigated and remained at 960 hectares as shown in Tabe C.2 in Appendix C. With a water price of $310 per ML, the mode adjusted to this water price increase by keeping the same area of and panted to crops but decreasing water consumption. There was a decrease of the voume of water consumed because of the change in the proportion of crop mix. The and panted to chickpeas increased from 96 hectares in the base case to 221 hectares. This is at the expense of panting cotton which decreased in area panted to 739 hectares from 864 hectares in the base case which have a very ow eve of optimum water requirement. This means that farmer is sti abe to maximise his revenue by using 87 per cent of the tota water aocation of ML for the $320 per ML increment when the tota water used started decreasing to ML per hectare but using the same tota and area of 960 hectares. At the water price eve increment of $360 to $380 per ML, the and panted to cotton remained at 960 hectares but water consumption decreased to ML per hectare. This decrease in water consumption was due to farmer appying 7 ML of water to 624 hectares of cotton panted with the rest (115 hectares) sti irrigated with 8 ML per hectare. Up unti price eve of $420 per ML, area and irrigated remained at 960 hectares but water consumption decreased to ML. From price increment of $430 per ML to $490 per ML, the tota and area finay decreased of 864 hectares and water utiised was ML per hectare. This decrease in and is now refecting the decreasing profitabiity of continuing to irrigate chickpeas. Thus the decrease in and irrigated is actuay due to 96 hectares of chickpeas not panted and irrigated in this scenario. This mode resut refects the opportunistic growing of chickpeas in Emerad as mentioned earier where their growing is dependent on the avaiabiity and cost of extra water. At water price eves of $500 to $590 per ML, water consumption 51 st AARES Annua Conference Queenstown New Zeaand, February

23 dropped significanty to 864 ML per hectare. At this price eve the optima soution was to pant pure chickpeas in 864 hectares with a water irrigation eve of 1 ML per hectare. This scenario has not occurred in Emerad as yet but may not be out of the question in the future if the current severe drought continues. At the highest price eve anaysed of $600 per ML, chickpeas production decreased by 72 per cent to 240 hectares. This is not a surprising resut given that the most profitabe crop in this area is cotton. Athough chickpeas are aso quite profitabe they woud not cover for the significant water cost at $600 per ML of water. Figure 3 shows the decreasing area of irrigated cotton and in farm type B as the price of water increases. At water price eve of $500 per ML of water, the mode stopped growing cotton atogether and instead shifted to chickpeas production $0 $310 $320 $360 $390 $430 $500 $600 Water price eves Cotton area Chickpeas area Figure 3. Cotton and chickpeas area based on water price eves in farm type B The water price and the tota water demanded for crop production (farm type B) in coumns 1 and 3 in Tabe C.2 are presented in Figure 4 as the short-term demand curve for tota water used. The inear curve was then appied to ascertain the best fitting curve. There is a very strong correation between price and demand in farm type B as shown by the high correation coefficient farm type B are sope = ; intercept = and 2 R. The estimated coefficients for 2 R = st AARES Annua Conference Queenstown New Zeaand, February

24 y = x R 2 = Water demanded (ML) Figure 4. Irrigation demand curve for farm type B The mixture of crops panted in farm type B for the base case is in Tabe 10. Simiar to farm type A, farm type B has a mixture of ony irrigated cotton and irrigated chickpeas. Irrigated cotton was panted in four soi types as discussed earier but the optima soi type was auvia. One hundred per cent of the unsuppemented water aocation (2 380 ML) was used to irrigate 282 hectares and 100 per cent of the river suppemented water (4 820 ML) was used to irrigate 678 hectares. The optima and irrigated is 100 per cent of the tota and area in farm type B. In a good season and at current water pricing using 100 per cent of their and, farm type B farms generates a net revenue of $ Tabe 10. Crops in farm type B PSB Has Water Yied Tota Revenue Tota Cost Gross Profit IC.IRS.IE1.T4.SWHC1.R IC.IRS.IE1.T4.SWHC3.R IC.IUS.IE1.T4.SWHC1.R IC.IUS.IE1.T4.SWHC2.R IC.IUS.IE1.T4.SWHC3.R ICh.IRS.IE1.T3.SWHC3.R Tota Note: IC- irrigated cotton, ICh irrigated chickpeas, IRS irrigated river suppemented, IE1 food irrigation, T3 and T4 conventiona panting, SWHC1 auvia, SWHC2 Downs, SWHC3 Scrub, R8 water eve of 8 ML per hectare Tabe C.3 in Appendix C shows the water demand responses to changing water prices in farm type C. Simiar to the resuts in farm types A and B, the crop mixture in farm 51 st AARES Annua Conference Queenstown New Zeaand, February

25 type C was aso cotton and chickpeas ony. Sorghum and wheat did not come into the soution as aternative crops even at a high water price scenario. The optima irrigated area in farm type C remained the same at hectares from water price eve increments of $10 to $290 per ML and the optima use of water was 100 per cent of the tota water aocation of 8000 ML up unti the $290 per ML after which the tota water used started to decrease to ML per hectare. At the water price eve increment of $410 per ML, the tota irrigated and panted decreased to 990 hectares with 715 hectares of cotton sti panted but chickpeas area decreased by 29 per cent to 275 hectares. At price increment of $490 per ML, the whoe tota and area of 990 hectares was panted to irrigated chickpeas. This then decreased to just 275 hectares at price eve of $590 per ML. As with farm type B, the soution indicates that in farm type C, mono-cuture irrigated chickpea was the optima crop choice when water becomes so expensive that it is no onger profitabe to grow irrigated cotton. Water cost incuding both part A and part B payments pus irrigation operationa cost at this eve is 27 per cent more than the gross revenue. Figure 5 shows the decreasing area of irrigated cotton and in farm type C as the price of water increases. Simiar to the trend in farm type B, at the water price eve of $490 per ML in farm type C, the farmer stopped growing any cotton and shifted to chickpeas production. There is a simiarity between the two resuts in that arge farms are ineastic at the first 29 price increment eves. The mode keep using the same amount of water of ML. To optimise its revenue when faced with increasing water costs, farm type C in the mode adjusted to the situation and varies the combinations of water sources with the different soi types so optima soution is one that wi resut in profitabe business. This then resuts in a shift in the combination of crops with mono-cuture irrigated chickpeas panted from the water price eve of $490 per hectare. 51 st AARES Annua Conference Queenstown New Zeaand, February

26 $0 $300 $310 $350 $380 $410 $490 $590 Water price eves Cotton area Chickpeas area Figure 5. Cotton and chickpeas area based on water price eves in farm type C The mixture of crops panted in farm type C for the base case appears in Tabe 11. In this farm type, irrigated cotton and chickpeas were the ony crops panted simiar to the resuts obtained in Farm types A and B. Irrigated cotton was panted in four soi types as discussed earier but the optima soi type was auvia. Ninety-four per cent of the unsuppemented water aocation (2 721 ML) was used to irrigate 340 hectares of cotton and the rest (135 ML) was used to irrigate chickpeas. One hundred per cent of the river suppemented was used to irrigate 305 hectares of cotton and 100 per cent of the channe suppemented was used to irrigate 320 hectares of cotton. The optima area of and irrigated was 100 per cent of the tota and area of hectares in Farm Type C. In a good season, at current water prices and using 100 per cent of their and, Farm type C farms generates net revenue of $ st AARES Annua Conference Queenstown New Zeaand, February

27 Tabe 11. Crops in farm type C PSB Has Water Yied Tota Revenue Tota Cost Gross Profit IC.IRS.IE1.T4.SWHC1.R IC.IRS.IE1.T4.SWHC2.R IC.ICS.IE1.T4.SWHC1.R IC.IUS.IE1.T4.SWHC1.R IC.IUS.IE1.T4.SWHC3.R ICh.IUS.IE1.T3.SWHC2.R ICh.IUS.IE1.T3.SWHC1.R Tota Source: Linear programming mode resuts Note: IC- irrigated cotton, ICh irrigated chickpeas, IRS- irrigated river suppemented, ICS irrigated channe suppemented, IUS-Unsuppemented, IE1 food irrigation, T3 and T4 conventiona panting, SWHC1 auvia, SWHC2 Downs, SWHC3 Scrub, R8 water eve of 8 ML per hectare Figure 6 shows the short-term demand curve for cotton in Farm type C. As with farm type B, there is a strong correation between price and demand as shown by the high correation coefficient ; intercept = and 2 R. The estimated coefficients for farm type B are sope = - 2 R = y = x R 2= Water demanded (ML) Figure 6. Irrigation demand curve for farm type C Tabe C.4 in Appendix C shows the water demand responses to changing water prices in farm type D. Simiar to the resuts in farm types A, B and C, farm type D farm mode of cotton, sorghum, wheat and chickpeas resuted in cotton and chickpeas farm combination. The optima irrigated area in farm type D remained constant at 580 hectares from water price eve increments of $10 per ML to $200 per ML. The optima use of water was ML or 100 per cent of the tota water aocation of 51 st AARES Annua Conference Queenstown New Zeaand, February

28 4000 ML up unti the $200 per ML increment when the tota water used started to decrease to ML per hectare but using the same tota and area of 580 hectares. At the water price eve increment of $210 per ML, the and panted to cotton remained at 580 hectares but ony 90 per cent is cotton production. From the price increment of $230 per ML to $260 per ML, the tota and area of 580 hectares was sti utiised but the proportion of cotton area decreased to 77 per cent and raingrown increased to 23 per cent. The irrigated area further decreased to 65 per cent when the water price increment became $270 per ML. Because of the contraction of the irrigated area, there was aso a corresponding decine in water demand. The changes in the combination of irrigated and raingrown cotton is a typica response of farmers in the Emerad region. Given decining water avaiabiity due to the change in prices, farmers tend to adapt to this situation by non-irrigating some of their cotton and opting to irrigate esser area. The mixture of crops panted in farm type D for the base case is in Tabe 12. In this farm type, irrigated cotton and chickpeas were the ony crops panted simiar to the resuts obtained in Farm types A, B and C. Irrigated cotton was panted in four soi types as discussed earier but the optima soi type was soi type 1. One hundred per cent of the overand fow water (4 000 ML) was used to irrigate 580 hectares. The optima and irrigated was 100 per cent of the tota and area of 580 hectares in Farm type D. In a good season, at current water prices and using 100 per cent of their and, Farm type D generated a net revenue of $ Tabe 12. Crops in farm type D PSB Has Water Yied Tota Revenue Tota Cost Gross Profit IC.OW.IE1.T4.SWHC1.R IC. OW.IE1.T4.SWHC2.R IC. OW.IE1.T4.SWHC3.R ICh.OW.IE1.T3.SWHC2.R ICh.OW.IE1.T3.SWHC1.R Tota Source: Linear Programming mode resuts Note: IC - irrigated cotton, ICh irrigated chickpeas, OW overand fow, IE1 food irrigation, T4 conventiona panting, SWHC1 auvia, SWHC2 Downs, SWHC3 Scrub, R7 water eve of 7 ML per hectare Figure 7 shows the short-term demand curve for water in farm type D. Compared to farm types B and C, there is a strong correation between price and demand for Farm 51 st AARES Annua Conference Queenstown New Zeaand, February