Water productivity accounting in Australian agriculture: The need for cost-informed decision-making

Abstract

Primary producers need strategies and tools to assist in monitoring water use with a view to improving physical and financial productivity. The purpose of this research is to integrate farmer financial accounting data with soil moisture and climate data to better account for water use on farm. Farm-accounting systems, if present, lack the sophistication to allow growers to analyze use, loss, and productivity of water. Water-accounting technologies, if present, do not readily link to business systems to provide the optimal real-time financial decision-making data, nor the necessary context for new technologies to support a broader integrated approach to water management. Findings of desk-based technology benchmarking suggest elements required include real-time sensory data integration that allows for strategic allocation to the full suite of direct and indirect water costs. Key actor interview and producer surveys highlight demand for a farm business integrated water productivity tool and findings from field data collected in a potato case study provide demonstration of how irrigation decision-making can be supported by the crucial link between producers’ business systems and sensing technology.

Keywords

Water productivity sensing technology water accounting soil moisture farm accounting potato farming

Introduction

Rising food demand and growing water scarcity are increasing pressure on agriculture, which uses about 70% of global fresh water for irrigation (UNIPCC, 2007). The agricultural sector is under increasing competitive and regulatory pressure to produce more with less and requires techniques to reduce pressure on water resources while increasing productivity and adaptive capacity to the effects of climate change (Altobelli et al., 2019; Hess and Knox, 2013).

The accounting profession has been at the forefront of driving technical business tool adoption in agriculture; however, there is acknowledgment that tools for costing water resource inputs are challenging to develop for the sector (Jack, 2009; Young and McColl, 2009). Primary production comprises activities and processes which not only demand high water volume allocations from multiple sources but also hide water usage and costs at various stages (FAO, 2017; Young and McColl, 2009). While there are currently water accounting tools in development and use around the world, these are often designed for the broader basin/catchment scale (Hoekstra et al., 2011), can be complex (Mulla, 2013, Srinivasan et al., 2007), and do not link to producers’ existing management or accounting systems (Hay and Pearce, 2014; Young and McColl, 2009). The lack of tools embedded in the appropriate financial decision-making context also means water productivity (WP) initiatives can have the perverse outcome of encouraging nonbeneficial water consumption (Grafton et al., 2018; Perry, 2010). Work is needed toward accurate and complete water accounting at the farm level (FAO, 2017; Hoekstra et al., 2011). Hence, investigation into how accounting data and tools can support improved WP for the largest freshwater user, agriculture, is of considerable relevance. Therefore, this article seeks to answer the question: How can an integrated WP accounting tool be developed?

Materials and methods

A mixed-methods data collection strategy was employed to capture a detailed review of water accounting systems, perceptions of key actors and producers, and WP-related data from a case study site (Alcon et al., 2014). Phase 1 comprised a desk-based analysis of currently available water accounting frameworks and technologies to investigate the costs and benefits of implementation of the available programs. These were comparatively analyzed using publicly available data about the elements and their applicability to Australian producers. Data were used to form a matrix of approaches and their positives and negatives for producers which facilitated benchmarking elements of better practice.

Phase 2 included interviews with nine experts in agriculture who had been identified publicly their thought leadership in agricultural water sustainability. Interviewees included senior farmers, academics, industry body, and government representatives. Ethical approval was granted¹ and participants were identified through public sources and contacted via e-mail in the first instance. Semistructured interviews using an interview guide were undertaken face-to-face at the participants’ locations in 2014 and 2015: (i) to determine the necessary practical elements for water use productivity accounting, (ii) to discover meaningful patterns, and (iii) how water and accounting elements (from Phase 1) should best be combined, structured, and delivered to producers. Confidentiality was retained to protect privacy throughout all phases of data collection (transcripts securely protected), data cleaning (identifiers removed and stored separately and use of pseudonyms), and data coding (interview data presented generally to avoid identifiers linked with comments). Coding of main themes was undertaken in NVivo v11 and data were combined with the desk-based tool benchmarking results of phase 1 enabling the development of a conceptual WP accounting tool.

Phase 3 tested demand for the conceptual WP accounting tool model through producer survey. The target population was agricultural business owners or managers in South Australia (SA), Tasmania, New South Wales, and Victoria operating in industries known to require intensive water use (e.g. irrigated agriculture) (Tingey-Holyoak, 2014a, b). Ethical approval was granted² and the survey was conducted via mail with farm owners or managers³ with follow-up phone calls until the sampling frame was exhausted and an adequate response rate achieved. Data collected were made nonidentifiable in the analysis and no information which could lead to identification was released. Basic statistics and nonparametric tests of the anonymized data were conducted in IBM SPSS v21.

Phase 4 was a participatory case study of a potato farm in Walker Flat in the Murraylands of South Australia. The phase was ethically approved⁴ and required formal and informal collaboration through farmer and stakeholder meetings and on-site data collection to extend the findings of the above phases (Chevalier and Buckles, 2019), specifically the collection of sensing and accounting data and its integration to help decision-making. Despite being relatively narrow and field-specific, soil moisture and local climate data were chosen as the key data to represent water given the relative ease of access to data and important area of focus it provides to direct attention to water demands, use, and productivity (Hoekstra et al., 2011; Perry, 2011). An industry partner in direct monitoring was identified and approached and a sample of producers were purposefully chosen from their customer list with demonstrated interest in improving their WP (Welsh and Rivers, 2011). A producer was engaged in a series of formal and informal site visits, meetings, and equipment installation. Discussions revealed the optimal site for monitoring an upcoming crop on a center pivot irrigated field which was then installed with two soil moisture probes to 60 cm and an on-site weather station. Data collected from sensors include soil moisture, drainage, evapotranspiration, rainfall, and irrigation events. Data collected from farm accounting system included costs of water, pumping, fertilizer, storage, maintenance, interest, insurance, depreciation, carting, labour, and licensing. Accounting data were scaled to the field- and season levels for analysis of cost structures, profit, and WP, then further disaggregated to the daily level for integration with direct monitoring data in a Microsoft Excel dataset.

Results

The following section details the findings of the desk-based comparative analysis and key expert interviews which result in the development of a conceptual model. Results of the producer demand survey are then explored, followed by participatory case study.

Current supply of water and technical accounting tools (phase 1)

There are a range of tools for water accounting globally, many of which are still under development and they have different strengths and weaknesses (CDP, 2013). For this project, the relevance of tools to Australian agricultural producers is of specific importance and so included but was not limited to tools developed by Australian research and industry (Climate Kelpie, 2018; Hochman and Carberry, 2011). As in other parts of the world, agricultural production in Australia is often undertaken by many small-scale producers who do not have access to accounting or technically trained support staff and equipment (Young and McColl, 2009). This is compounded by an “implementation problem” and so tools that cited on-the-ground delivery problems were excluded (McCown et al., 2009). Findings from comparative analysis revealed 10 main tools, many of which were broad/catchment scale or externally focused (Table 1).

Table 1.

Review of water accounting tools for agriculture and resulting spectrum of positive and negative elements identified in the literature and from public sources.

	Negatives	→	Positives
Life-cycle thinking tools	Data too hard to access for producers		IDs hotspots in production
Water footprint accounting	Time-consumingExpensive	Can integrate into larger LCA framework
Global environmental management initiative tools	Not user-friendly	Data integrate with large organization external reporting systems
World business council for sustainable development global water tool	Not sensitized to the small primary producer level
Aqueduct	Macrolevel data
Aqua gauge	Qualitative inputs time-consuming	Quick and full versions	Excel-basedBenchmarking function
Veolia’s water impact index	Complex understanding of water systems required	Single output measure	Direct and indirect measures
Water risk filter	Risk assessment only		User-friendly
Water stewardship	Not sensitized to the small primary producer level	Links to other standards
General purpose water accounting	External focused

LCA: life-cycle assessment.

It became apparent that there is a lack of integration with or express recognition of the water information technologies that can assist producers achieve maximum productivity, such as direct monitoring and sensing tools. So technical tools were also explored and an in-depth review found 18 main tools. Many reportedly lack the required user-friendliness and applicability at the farm level. Importantly, none integrates into other business applications (Table 2).

Table 2.

Review of technical water information systems for agriculture and resulting spectrum of positive and negative elements.

	Negatives	→	Positives
Yield Prophet	Grain productionHistorical weather data	Can be sensitized for climate change	Real-time crop data during growing time
IrriStat-SMS	Limited regions	Benchmarking capability	Robust irrigation decision-makingUser-friendly
WaterSense	Sugarcane production	No functionality without Internet
AQUAMAN	Peanut productionHeavy farmer data inputs	No functionality without Internet	Daily e-mail report
Sense-T	Tasmania only	No link to accounting/costing	Low costReal-time spatial and historical dataUser-friendly
IrriMate	Cumbersome	Specifically for water loss
PIRSA water budgeting tool	Needs other tools	Limited integration potential
Consolidated co-operative wineries variety and water priority	Riverland only	Limited integration potential	Simple to use spreadsheet
RESSTAT	Online surveyPoint in time	Integrated socioeconomic factors
SWAGMAN	Complicated	Lacks economic underpinnings for suitable
Aquatech watertrack	Potentially expensivePoint in time	Potential for daily dataStorage analysis as well as use	BenchmarkingBenefit/cost calculation
Sustainability radar	Requires entire supply-chain integration
EnviroVeg platinum	Requires producer/retailer buy in
Irrigation recording and evaluation system	Horticultural production onlySimplistic
Irrigation performance audit and reporting tool	Suitable for consultants, industry bodies, etc. rather than producers		Crop-, field-, and farm-level spatial and temporal analyses
Water manager		Not available for specialty crops	Benchmarking and budgeting based on farm characteristics
CSIRO water use efficiency benchmarking		Limited integration potential	Cost-effective and full versions
More dollars per drop	ExpensivePoint in time		In-depth assessment of technical requirements linked to economic objectives

The elements of the water accounting tools (Table 1) and technical water information systems (Table 2) were benchmarked using publicly available information about their utility (Table 3).

Table 3.

Water accounting and water technical information systems elements benchmarking.

	Minimum-level element → Best practice element
Water accounting tools	Quantitative production metrics	Identifies hotspots in production
	Ease of access to entry data	Direct and indirect water-use data capture
	Quick and full versions	Data integrated across decision-making systems
	Microlevel farm applicable	Sensitized for farm size, especially small
	Internal in focus	Benchmarking
Technical water information systems	Sensitized to different prod types	Product-level sensory capability
	Regional sensitivity	Real-time farm/catchment weather data
	Easy to use	Integrates other platforms including economic
	Cost-effective	Benchmarking
	Output easy to understand	Real-time data output in simple format

Benchmarking revealed that water accounting tools and technical water information systems are usually separate and difficult to link. This in itself presents a further challenge to integrating all elements into a water costing tool that readily links with producers’ business/accounting systems. The following key expert interviews were designed to assess current systems in use, reinforce, and advance benchmarked water accounting and information elements above, and illuminate other factors beneficial for a user-friendly WP accounting tool.

Interviews (phase 2)

Nine face-to-face interviews, each of approximately 1 h in length was undertaken as per Table 4.

Table 4.

Key expert interview participant demographic data.

Key expertise	Type of business	Position	Number of employees
Industry leading producer	Vineyard/winery (TAS)	Viticulture director	15
Industry leading producer	Vegetable grower (VIC)	Director and irrigation manager	5–50
Key industries	Accounting firm (TAS)	Partner	10
Key industries	Agribusiness consultancy (TAS)	Principal	5
Academic	Academic (TAS)	Professor	5000
Government	Government (TAS)	Development officer	1000–3000
Government	Government (VIC)	Policy officer	1000–3000
Peak body	Industry body (VIC)	Program manager	50
NRM	Natural resources board (TAS)	Regional manager	30

TAS: Tasmania; VIC: Victoria; NRM: natural resources management.

Participants were asked what kind of costing systems were commonly in use for primary production. Responses were varied with nearly half of respondents using Mind Your Own Business (MYOB) accounting software (43%) and basic budgeting (43%) (Table 5).

Table 5.

Monetary accounting systems use in primary production.

Accounting system (monetary)^a	%
Phoenix	14
MYOB	43
Excel	14
Employ accountant	14
Basic budgeting	43
Basic planning	14
Tax return only	14
Intrinsic knowledge	14

MYOB: Mind Your Own Business.

^aRespondents could identify more than one system.

Most interviewees indicated that the widespread use of formal accounting systems was limited beyond Excel; however, the accounting interviewee indicated that there were many clients operating MYOB. Most interviewees perceived that it will be difficult to account for water throughout the entire in-field production process; however, both leading producers indicated that they try to and plan to do it: “I differentiate water costs between different lines of products” (Irrigation Manager, Director) and “I am trying to get to gross margin per block for the first time, but it is challenging to plan to capture everything [related to water]” (Viticulture Director). The accounting interviewee perceived that it was possible but “….not currently happening with the systems and tools available” (Accounting Firm Partner). The agricultural consultant perceived that accounting for the cost of water throughout production was a challenge, but definitely possible with the correct integration of tools: “Many [producers] have sophisticated data collection systems on pivots etc. and more could be made of these to link with accounting systems as per your proposed tool idea” (Principal, Agribusiness consultancy).

The interviewees considered elements that would need to be integrated to assist incorporating water technology into accounting systems, which included “…to see costs associated with my options to do different things at any given time given to me in a user-friendly way, rather than having to go deeply into analysing it all myself” (Viticulture Director). The industry consultant provided context for how practically a tool could be of benefit: “…it costs $2000/ML to store water in a dam versus $1100 to buy in, however the farmer thinks he could store water for $700, but they are not including the hidden costs of inspection every 3 years…a water accounting system that takes hidden costs into account that helps profit margin analysis is needed” (Principal, Agribusiness consultancy). The lack of real-time integrated data has halted take-up of tools, supporting findings in phase 1: “Real-time collection of water flow information is fundamental to take up and effectiveness of existing tools…Biggest [potential need for tool] are potatoes.”

While there was some divergence across the different types of interviewees, common themes emerged that included the need for integration with real-time data and moving beyond the technical to better inform practical planning and profit margin analysis, especially in industries like potatoes. As a result of phases 1 and 2, a conceptual model of an integrated WP accounting tool was developed for presentation to phase 3 survey participants (Figure 1).

Figure 1.

Integrated WP accounting conceptual model resulting from phases 1 and 2. WP: water productivity.

The conceptual model developed (Figure 1) demonstrates initial on-farm water-related cost drivers need to be identified and documented. Phase 2 interviews showed these drivers need to be integrated to ensure that hidden/indirect water-related costs can be factored into production decision-making. This then needs to be combined with the increasing sources and amounts of sensing data, much of which can be of limited use without integration with business planning and processes. Interviewees advised that the link must lead to better-informed decision-making required for budgeting, profit margin analysis, and planning.

The following section presents results of surveys of producers presented with the model and their demand and need for such a tool explored.

Conceptual tool demand testing (phase 3)

To understand producers’ receptiveness to WP accounting, a survey was undertaken with 110 producers who were presented with the model (Figure 1). Participants were asked how they were currently costing their produce and over one-third had no formal method (35%) with no statistically significant difference between the states (X ² = 33.58, p = 0.09) (Table 6). If a method was identified, then it was usually undocumented (17%) or a part of planning (15%) (Table 6).

Table 6.

Producers’ common general and water costing methods.

	SA (%)	NSW (%)	Victoria (%)	Tasmania (%)	Total (%)	Chi-Squared
No method	21	70	30	38	35	X ² = 33.58, p = 0.09
Undocumented methods	5	0	18	25	17
Part of budgeting/planning	21	0	18	15	15
Phoenix	26	20	25	8	18
Excel	11	0	3	10	7
Handwritten methods	5	10	3	4	5
MYOB	11	0	3	0	3
Specific water costing	31	0	31	13	27	X ² = 12.84, p = 0.38

SA: South Australia; NSW: New South Wales; MYOB: Mind Your Own Business.

^a Significant at p < 0.05.

^b Significant at p < 0.01.

^c Significant at p < 0.001.

Only around one-quarter of producers surveyed are costing their water into their produce (27%). Only 13% of those surveyed perceived the conceptual model presented would not be useful to their business and over-third of respondents indicated that the tool would improve their profitability (34%) (Table 7). Around a quarter thought their competitiveness would improve (25%) and a third that their environmental impact would be improved.

Table 7.

Producers’ perceived possible business improvements from tool.

Improvement	SA (%)	NSW (%)	Victoria (%)	Tasmania (%)	Total (%)	Chi-Squared
Profitability	37	20	37	32	34	X ² = 6.86, p = 0.87
Competitiveness	52	0	27	25	25	X ² = 10.88, p = 0.54
Environmental impact	32	0	31	34	30	X ² = 13.61, p = 0.33
Customer communication	27	20	37	22	27	X ² = 4.76, p = 0.97
Regulator communication	42	20	46	54	52	X ² = 11.93, p = 0.45
Bank communication	27	0	21	9	10	X ² = 8.95, p = 0.71
Product design and labeling	11	0	14	16	13	X ² = 16.71, p = 0.34
Worker satisfaction	32	0	46	26	25	X ² = 17.78, p = 0.28

SA: South Australia; NSW: New South Wales

^a Significant at p < 0.05.

^b Significant at p < 0.01.

^c Significant at p < 0.001.

The most perceived potential business improvements were found to be in improving regulator communication (52%), which highlights the need for water accounting tools that can discharge accountability to regulators as part of usual business processes without additional data gathering or communications. Most respondents did not think the tool would assist them with communicating their water information with the bank (90%), nor would it be helpful with product design/labeling (87%). The most preferred technology for the tool was an iPhone or iPad application (44%) followed by Excel (24%) (Table 8), suggesting that increasingly producers require technology that they can access in the field across multiple platforms.

Table 8.

Producers’ preferred platform for an integrated WP accounting tool.

Platform	%
iOS App	44
PC	21
Excel	24
Android App	3
Filemaker	3
MYOB integrated	3
Specialized agribusiness software	3
Mac	3
Cloud	3

WP: water productivity; MYOB: Mind Your Own Business.

^a Respondents could choose more than one option.

Findings indicate enough scope for advancing the tool to a demonstrable platform, such as through field development to highlight the specific links to business data and make integration with accounting data more visible.

Participatory case study (phase 4)

In light of the producer survey results and the fact that potatoes are set to remain a dominant irrigation water user into the future (FAO, 2008; Weatherhead et al., 2015), a case study was pursued in potato farming. Potatoes are relatively productive water users, yielding more food per water input unit than any other major crop (FAO, 2008). However, modern varieties are sensitive to soil water deficits and need frequent irrigation. SA is the largest producer of potatoes with 29% of production and also produces 80% of nation’s fresh-washed potatoes worth $206 million in farm gate production (Potatoes SA, 2017). The case study region in the lower Murray harvests nearly all year but suffers from notorious water issues, being low rainfall and predominantly sandy loam soil in which water percolates quickly and requires frequent irrigation to avoid plant stress. Accounting for water using soil moisture information is an important area of focus as helps to know what crop needs, what it currently has, when it will likely go into stress, and where to apply to promote growth, avoid waste, and save costs (Pardossi et al., 2015). Therefore, the first step was to set up the potato site with soil moisture probes and a weather station and monitor a season of potato crop (Figure 2).

Figure 2.

Visual depiction of soil moisture probe and weather station establishment at case study site.

The 28 ha site ran two potato varieties for 97 days from mid-March 2018 to end of June 2018 for which accounting data were also collected from Xero accounting software, suppliers, and farmer records and included those presented in Table 9, collected in line with the original model outputs (see Figure 1).

Table 9.

Water-related accounting data collected from case study site.

	Item	2018 Season data
Budgeting	Electricity costs per hour	Wholesale market −10.041c/hWh average
	Water cost	$340/mL
	Cost of fertilizer	$6465
	Hourly rate irrigation labour	$100 ph
	Carting fees	$0
	Insurance costs	$0
	Cost of storage	$0
	Cost of repairs	$3500
	Cost of maintenance	$0
	Depreciation per year on pivot/pipes	$6000
	Water licensing/allocation fees/charges	$319
Practical planning	Field size	28 ha
Practical planning	Total property size	3800 ha
	Amount of total fertilizer	1200 kg
	Days ML applied irrigation to crop	387 mm
	Hours pumped each day	643 h
Strategic planning	Hours spent on irrigation per day on site setup preseason	28 h
	Hours spent on irrigation labour/day during season	236 h
	Quality data	7.5/10
	Comparative notes	Virgin soil
Profit margin analysis	Yield data	670.63 t

Data revealed fixed costs of interest, depreciation, and licensing were only approximately 6% of total water-related costs but that hidden water-related costs emerged as significant, with owner labour and water quality treatment comprising 15% of total water-related costs, previously completely unaccounted for (Figure 3). In terms of variable costs, power accounted for 12% of total variable costs but was highly variable all season, given the producer pays market rates subject to half-hourly changes. This ranged from $0.64/MWh to $4798.86/MWh during the period. Water costs were still high at 75% of the total variable costs.

Figure 3.

Water-related cost across variable, fixed, and hidden categories at case study site for 2018 season.

Yield data were collected for the season and the relationship to full water-related costs is demonstrated in Figure 4, showing that site-specific water-related costs comprise approximately 75% of total yield revenue for the season, not including other costs related to the site, including the significant costs associated with ground preparation, seeding, harvest, and processing not considered by the research.

Figure 4.

Relationship of water-related costs for the given yield revenue.

From yield data, WP could be calculated using methods available in the literature (Hussain et al., 2007; Molden et al., 2007):

{WP}_{volume} = yield (kg) {/water (m}^{3})

{WP}_{dollars} = yield ($) {/water (m}^{3})

Research indicates that for potatoes with a yield value of approximately $0.1/ kg, the value should be between 3–7 kg/m³ (per equation (1)) and 0.3–0.7 $/m³ (per equation (2)), within which the producer’s productivity results reside for equation (1) but at the upper end (Table 10). For equation (2), the gross value-based WP is higher than the average range which is positive for the producer; however, implementation/testing and further data are required to track changes overtime which is where powerful information resides.

Table 10.

WP at case study site compared to potato indicators from the literature.^a

	1. Product per unit of water applied WP (kg/m³)	2. Gross value of product per unit of water applied WP ($/m³)
Case study 2018 potatoes season	2.79	0.96
Potatoes indicator^b	3–7	0.3–0.7

WP: water productivity.

^a Molden et al. (2007); Renault and Wallender (2000).

^bIndicator range encompasses all possible agricultural types, from extensive systems without additional nutritional inputs to super-intensive systems.

Further discussions with the producer participant revealed the need for the tool to not only to expose the full cost of irrigating to identify areas of possible cost-saving and to provide productivity updates, but also to integrate as much as possible in the way of all available data in one spot, including energy market costs and weather forecasting, in line with results from benchmarking of other tools (phase 1) and key expert interviews (phase 2). To facilitate this, accounting data were scaled to the season and field level, further disaggregated to daily amounts and then entered into Microsoft Excel with the daily sensor and climate data enabling an integrated into a comprehensive time-series database. An additional linked database was established for energy market data integration, based on half-hourly readings of market costs linked to specific irrigating times. Furthermore, findings from phases 1–3, and key issues raised in phase 4, also revealed the need for cost and accounting data to permeate other possible utilities, particularly:

Cost-informed alerts

Sensors can provide data on all key agronomic indicators, including (i) plant stress, (ii) ineffective irrigation, for example, in times of high wind, and (iii) disease conditions, for example, soil temperature.

Costing information produced by this research can advise on alternative action in light of the alert.

Cost-informed forecasting and scenario analysis

This requires development of scenario models that can provide costed irrigation action support. Example mapping revealed increased utility of this function for producers paying wholesale power prices, which create great variability in pumping costs, especially in peak seasons, see, for example, in Figure 5.

Figure 5.

Cost-linked forecasting example scenario mapping. Yield numbers demonstrated in this figure are proxy numbers only. Further seasons of growth data are required to analyze around yield at this stage, which is the scope of future research.

Phase 4 works provide advancement on the original conceptual model (Figure 1) derived out of phases 1 and 2 and demand-tested in phase 3 which is demonstrated in Figure 6, highlighting the need for costs to be scaled seasonally and geographically to the field level and “mapped” against the daily sensing data. The figure shows that by using real data from the 2018 potato growing season and linking full water-related costs, soil moisture, and climate data together at a basic level, it was possible to actualize an Excel prototype comprised of a source worksheet linked to destination worksheets with specific information required from phases 1–4.

Figure 6.

Advancement of conceptual model to practical prototype outcomes for WP decision-making. WP: water productivity.

While the prototype requires further advancement as practical iOS software, as illuminated as a requirement in phases 2 and 3, the following section discusses implications of the development to date, in line with all of the phases of research.

Discussion

This study aimed to investigate the potential, demand, and practical possibilities for a user-friendly water accounting tool and found through a review of standardized tools and methods (phase 1), any tool needs increased crop- and farm-level sensory and real-time data functionality, in addition to integration potential with other decision-making systems, supported by other research as key for acceptance by other stakeholders, such as regulators (Foster and Perry, 2010). Further works through key expert interviews (phase 2) reveal that tools need to be tailorable for different purposes and importantly able to capture both indirect and direct water-related costs. Such a tool would need to be able to fully integrate water technology and financial accounts utilizing timely spatial and temporal data to facilitate practical planning, yet still remain cost-effective. This is supported by global research finding evidence-informed decision-making to be crucial to water accounting success (FAO, 2017), even more so in areas like California where decision-making base on sensed water data is critical for effective response on both water quantity and quality aspects (Shilling et al., 2005).

A subsequent farmer survey (phase 3) indicated that there are many informal systems of accounting and costing being used that would be challenging to link accurately to technical water information and further still, most are not costing in their water at all meaning profitability could be eroded. So many participants could see the potential of the tool for their competitiveness and profitability. Interestingly, many also see potential in using the tool to discharge accountability to regulators, reinforcing that regulatory pressure is one of the greatest pressures on agricultural business water use at the moment and the tool can streamline communication while also making transparent hidden water-related costs, noted as a critical problem globally (FAO, 2017). Furthermore, focused case study work (phase 4) allowed for the establishment of a baseline database for potatoes, the development of the key decision trigger points being drawn from the data, including full cost tracking, cost-informed alerts, and forecasting, combined in a linked database that can be implemented on-site for further testing. While the producers WP was good when compared to common indicators (Molden et al., 2007; Renault and Wallender, 2000), the comparison of costs to revenue shows the impact of including all water-related costs on profit erosion. This supports other studies and the results from phases 1–3 that there is a need for inclusion of all direct and indirect costs to be integrated with sensing data for any water accounting to have a meaningful impact on producer decision-making (FAO, 2017). This study shows this and reveals the often hidden costs of owner labour and water quality treatment, which is necessary as a first step to understanding any type of farm productivity in real terms (Moran, 2009).

Conclusions

Through answering the question: How can an integrated WP accounting tool be developed? it was found enhanced understanding of water costs and drivers can be used for simpler budgeting and planning, gaining competitive advantage, discharging accountability to water and other regulators, and also for staff and other delegates to be better informed of the cost and impact of their irrigation decisions. It should be noted that on-farm WP improvements are not the responsibility of the farmer alone and roles and partnerships overlap between policymakers, water managers, irrigation industries, banks, and financial providers. As such, WP accounting tools could serve to meet the often nonintegrated policy objectives of both sustainable regions and water resources public policies if broad adoption was promoted via initiatives such as colearning and capacity building within broader catchment scales (Srinivasan et al., 2007).

The study examines perceptions and data from key actors, producers, and a season of potatoes at a point in time which requires caution when generalizing broadly to a problem like agricultural WP requiring a longer-term view. However, how farm accounting systems can be linked to the key elements for water accounting identified by this study in a user-friendly tool, and how the tool can easily be applied by producers, is of significance to both the accounting and the primary production sectors. This article forms a foundation for continued research while advancing understanding about how tools for interlinked water accounting and farm management can and should be integrated into farming businesses.

Footnotes

Acknowledgement

The authors would like to thank the 9 key actor interviewees, 110 producers surveyed, and 2 very generous case study participants who graciously shared their time and knowledge.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to thank the Chartered Institute of Management Accountants (CIMA) for funding the preliminary project and also to University of South Australia for Research Themes Investment Funding.

ORCID iD

Joanne L Tingey-Holyoak

Notes

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