Australasian Agribusiness Perspectives 2019, Volume 22 ... · value chain and its competitiveness for firms. Grain production is a major industry in Australia and an important sector

Technology and Data in the Australian Grains Industry Mumford and Griffith

Australasian Agribusiness Perspectives, 2019, Volume 22, Paper 3 Page 31

Australasian Agribusiness Perspectives 2019, Volume 22, Paper 3

ISSN: 2209-6612 --------------------------------------------------------------------------------------------------------------------------

Technology and Data in the Australian Grains Industry: Issues and Opportunities for Improved

Coordination

James Mumford a and Garry Griffith b

a Postgraduate student, Centre for Global Food and Resources, University of Adelaide, Adelaide. b Centre for Global Food and Resources, University of Adelaide, Adelaide; UNE Business School, University of New England, Armidale; and Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville. -------------------------------------------------------------------------------------------------------------------------- Abstract This paper analyses the Australian grains industry value chain from a technology and data perspective. The grains industry is described and opportunities are explored which may provide improved value chain coordination, information sharing, and overall profitability for actors in the value chain, particularly from the viewpoint of crop inputs as a key expense item and driver of grain farm profitability. Some current and emerging technologies are identified which have the potential to facilitate better value chain coordination with the goal for the grower to extract greater returns from the value chain. Key words: grains industry, technologies, data sharing, chain coordination Introduction This paper analyses the Australian grains industry value chain from a technology and data perspective. With grain producers competing in a highly competitive global market, they can do little to influence the price received for their goods. Approximately 65 per cent of Australian grain is exported, increasing to 90 per cent in the states of South Australia and Western Australia where there are limited domestic markets (Gordon, 2016). Often, the export destinations of Australian grain commodities are in direct competition with substitute grains from alternative origins with a lower cost of production or Government subsidies, further highlighting the need to analyse the grain value chain and its competitiveness for firms. Grain production is a major industry in Australia and an important sector of the economy. In the 2015 financial year, the sector contributed $13.7 billion (farm gate value) to the Australian economy (Gordon, 2016). As displayed in Figure 1, export earnings for the sector have increased from approximately $6b in 2005 to $11.4b in 2015. Figure 2 highlights the important contribution of the grains sector to the Australian economy. Profitability for grain growers is a widespread issue. Figure 3 displays the proportion of Australian grain farms with negative returns. In the period 2006-2016, 56 per cent of grain farms recorded a loss (ABARES, 2017), although that figure has recovered to around 40 per cent in 2016-17.



Figure 1. Australia’s 5-year average total grain production and value for 2005, 2010, 20151

Source: Gordon (2016)

Figure 2. Contribution of the grains industry to total Australian agriculture production in 2014-15: total of $13.7b or 26 per cent of total value of Australian agriculture

Source: Gordon (2016)

1 Note that some colours are incorrect in the original document.



Figure 3. Proportion of Australian grain farms with negative annual farm business profit for years 2000-2017

Source: ABARES (2017)

Meanwhile, Hajkowicz and Eady (2015) highlighted the cost-price squeeze faced by Australian farmers whereby input costs increase at a faster rate than product prices. Figure 4 shows how the average size of an Australian grain farm has increased at a rate of 61ha per year on average, from 1,082ha in 1990 to 2,672ha in 2016. This means that grain farmers have been able to benefit from economies of scale and invest in large and efficient machinery to partially offset the cost-price squeeze and lower the marginal cost of production. However, income per hectare has flatlined during this period (0.7 per cent annual growth on average during this period) while key variable cropping expenses, chemicals and fertiliser, have increased 2.4 per cent and 2.1 per cent per annum, respectively, as displayed in Figure 4. The dotted lines plot the actual data while the linear trend line is a continuous line. This is further supported by the stark decline in terms of trade as shown in Figure 5. This paper describes the value chain of the Australian grain industry and explores opportunities for current and emerging technology to provide improved value chain coordination, information sharing, and overall profitability for actors in the value chain, particularly from the viewpoint of crop inputs as a key expense item and driver of grain farm profitability. Value Chain Map The value chain map shown in Figure 6 indicates the key actors for the grains value chain from input suppliers to consumers. It maps the export and domestic supply chain pathways and identifies key profit drivers at each stage.



Source: Data sourced from ABARES (2017)

Figure 5. Terms of trade for Australian agriculture for years 1972-2013

Source: Keating (2015)

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Figure 4. Average farm size, fertiliser and chemical expense trends in years 1990 - 2016 for Australian broadacre cropping farms

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Figure 6. Grains value chain map

Source: adapted from Rural Directions (2017), Johnson (2017) and Economic Development Board SA (2016)



Figure 7 illustrates the estimated percentage of value for each actor in the value chain from farmgate to supermarket for bread and flour products in Australia. Figure 8 is a comparison to the Dutch bread value chain with more detail, including costs and margins for each actor. Figure 9 details the various factors affecting the pricing of grains and flour in Australia.

Source: adapted from Spencer (2016) and Nguyen et al. (2016)

Figure 8. Bread supply chain margins, Netherlands, 2005 to 2008

Source: NMa (2009) and Nguyen et al. (2016)

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Figure 7. Estimate of the distribution of value / revenue along the Australian wheat value chain for bread and flour products



Figure 9: Factors affecting grains and flour pricing

Source: Spencer (2016)



Value Chain Strategies, Drivers and Performance Rural Directions (2017) suggests four main profit drivers in broadacre farming: gross margin optimisation, developing a low-cost business model, people and management, and risk management. In developing a low-cost business model, the report highlights that efficiency in labour and machinery utilisation, along with leasing land at cost-effective rates, are key. However, it does not make mention of an emphasis on procurement strategies for inputs (although this would be broadly considered under the aforementioned low-cost business model). Chopra and Meindl (2013) claim there is a compromise between efficiency and responsiveness in a value chain. These are influenced by logistical value chain drivers: facilities, inventory and transportation as well as cross-functional drivers: information, sourcing and pricing. The emerging technology available in the grains sector has the potential to dramatically impact these cross-functional drivers and overall value chain coordination. It operates in a commodity based, highly efficient structure. The value chain is global – starting from input manufacturing which may take place in another country and imported into Australia. There are many global companies involved with differing objectives resulting in conflict and a general lack of coordination, therefore reducing the overall chain surplus (Chopra and Meindl, 2013). They also highlight that delayed or distorted movement of information between stages of the chain further contributes to a lack of coordination. Chopra and Meindl (2013) recommend the following actions to improve value chain coordination: Aligning of goals and incentives: whole-part-whole approach and win-win scenarios needed to grow the overall value surplus. This means every stage of the supply chain is to focus on the total value chain surplus, not just the firm’s individual component of it. This avoids ‘money being left on the table’ at each stage of the value chain and therefore value chain coordination improvement. Improving information visibility and accuracy: including implementing collaborative forecasting and planning. Improving operational performance: including suggestions relating to reducing replenishment lead times and reducing order lot sizes through reduction in the fixed costs related to each order. Designing pricing strategies to stabilise orders: which encourage retailers to order in smaller lots and reduce forward buying therefore reducing the inefficiencies related to the bullwhip effect. Building strategic partnerships and trust: to better facilitate these recommendations. In general, higher levels of trust allow a supply chain to become more responsive at lower cost. This is underpinned by clear identification of roles and decision rights for all parties, effective contracts and good conflict resolution mechanisms. This is particularly important in the case of the grains industry where we see global players owning various parts of the supply chain and only acting in their own best interest (for example Viterra bulk loading port monopoly in South Australia), or owning key inputs such as cropping machinery and seeking to create closed data systems. The current and emerging technology available to the grains industry has the potential to result in better value chain coordination, by a process of the industry achieving a data-driven responsive strategic fit and saving costs. Ros Harvey, Founder and Managing Director of agri-technology company The Yield, claims her company can increase value throughout the supply chain by better forecasting weather through artificial intelligence:



“We use artificial intelligence to predict it [the weather] then put that into solutions for our customers, ranging from when they irrigate to how to manage disease risk and nutrition…What happens on farms ricochets through the supply chains, with things like when to optimally harvest to extend shelf life. We add value right through the supply chain.” (Swan, 2017). Farm Weekly (2017) provides an example of how a grain grower in Western Australia is utilising technology and data to reduce his costs, highlighting the strategic use of data. Brad Jones of Bungulla Farms says: “Adoption of latest technologies and the data these provide is helping to lower variable costs in an environment of tight business margins through better management of fertiliser, herbicide and other inputs and minimising risk exposure…We collect vast amounts of farm data to help in our decision-making and closely monitor how each of our paddocks are performing…We have individual paddock rotation plans mapped out for three years in advance, so the data helps us make decisions for products with a decent horizon out in front." Farm Weekly (2017). See also the presentation on Bungulla farms in Jones (2018), which details the following workflow map.

Figure 10. Bungulla farms workflow map

Source: Jones (2018)

Possible Intervention: Emerging Technology has the Ability to Improve Demand Forecasting to Assist in Value Chain Coordination This analysis suggests that grain growers could expedite their uptake of useful emerging technologies to assist in increasing the profitability of their farming operations. Some of the technology identified results in the ability to save on input costs, increase operational efficiency and better forward plan, resulting in greater coordination. In turn, this should result in increased margins and overall surplus for value chain participants, particularly the grower. Other emergent technology promises improved levels of cooperation and collaboration among firms to achieve a more integrated value chain.



Chemical inputs KPMG (2016) investigated the use of big data by chemical companies. Big data is utilised in the development of both short- and long-term market forecasting to assist in demand planning in order to support effective production, procurement and investment planning (KPMG, 2016). The report further highlights that this information is particularly useful as these large chemical companies are operating globally across multiple regions. Through powerful data analytics, Dow Chemical Company is able to forecast sales within a margin of 10 per cent, whereas previously the error band was 40 per cent (KPMG, 2016). This has positive implications for the value chain, where improving information visibility and accuracy through collaborative forecasting and planning can increase overall efficiency of the supply chain. Machinery inputs Appendix A provides examples of John Deere’s latest technology innovation where the machinery dealer is provided with live updates on machine performance and, through various sensor and real-time equipment monitoring, the dealer can be provided with live feeds when a machine breaks down or, ideally, ahead of time so preventative maintenance can be enacted before a major breakdown. This cooperation and information sharing has immense impact for the value chain whereby the dealer can better plan staff workload and spare parts inventory, while the farmer can reduce repairs and maintenance expense and avoid costly downtime. Figure 11 summarises the benefits of this strategy for customers and the dealer network as actors in the grain value chain. This is an example of building strategic partnership and trust which, according to Chopra and Meindl (2013), allows a supply chain to become more responsive at lower cost, therefore improving strategic fit. Additionally, this example also exhibits the facilitation of information visibility and accuracy which has the same effect on strategic fit and overall chain surplus (Chopra and Meindl, 2013).

Figure 11. John Deere highlights the value chain benefits of their technology including the effect on customers and dealers

Source: Assink and Lok (2014)



Precision agriculture and variable rate technology Variable rate technology and precision agriculture is another technology-based innovation available to grain growers to reduce input costs. This technology utilises satellite systems and other surveying instruments to map the surface area of farmland to determine topography, soil type, salinity and other subsoil constraints. This is then combined with yield maps and rainfall data to determine production zones. Through this process, the grower can identify yield-limiting characteristics of the soil and address these areas with amelioration together with matching of crop inputs (seed and fertiliser rates) to production potential. Also, precision agriculture and satellite systems have the ability to reduce overlap by automating the shutoff of inputs and reducing human error resulting in cost savings. Another emerging innovation is real-time protein monitoring at harvest where growers can segregate areas of the field by protein level therefore increasing the likelihood of achieving a premium grade of wheat to derive further value and increased returns. These technologies, through an ability to decrease costs or increase price received, can therefore result in increased value chain surplus for the grain grower. Conclusion This paper has discussed the various elements of the grains industry value chain in Australia. Grain-growing firms, facing declining terms of trade and competing in global markets as price takers, are required to focus on being low-cost producers in order to maintain adequate returns. Some current and emerging technologies have been identified which have the potential to facilitate better value chain coordination with the goal for the grower to extract greater returns from the value chain. Such examples include real time monitoring of high protein for premium grade wheat as a way of increasing the price received per unit. On the input side for grain growing, various technologies including variable rate technologies and section control have the ability to decrease overlap and expenditure on costly inputs. Additionally, technology such as predictive analytics is able to be used to assist the farm in preventative maintenance programs to minimise downtime in the field and increase the level of coordination with their dealer, potentially decreasing repairs and maintenance expenditure and increasing overall profit. Similarly, for the machinery dealer, this technology should result in better overall coordination of their service delivery resulting in increased efficiencies leading to lower cost of delivery of their services. All of these can combine to create additional value for the actors in the grain value chain or realignment of value distribution to increase grain growing margins. Many of the developments discussed here have been tested in the United States grains industry where online connectivity is a given. In much of the Australian grain producing areas, connectivity, if existent, is sporadic and subject to very restrictive uploads. This implies that on farm ‘edge’ computing based on ‘small data’ that provides decision support and co-ordination outcomes could be a useful base for aggregated, select uploads of data in which privacy is also safeguarded (Brad Plunkett, pers. com., September 2018). There are also broader opportunities arising out of efficient information capture, storage and use. Data management conceivably has an important role to play in maximising logistics efficiency and creating chain transparency to lower transaction costs of contract service and delivery. Data management and transparency may also play a role in more efficiently sourcing capital as the transaction costs around ownership and management of broadacre enterprises are lowered (Plunkett et al., 2018).



References ABARES (2017), Farm performance: broadacre and dairy farms, 2014-15 to 2016-17, ABARES, viewed 26 October 2017. Available at: http://www.agriculture.gov.au/abares/research-topics/surveys/farm-performance Assink, A. and Lok, G. (2014), Connecting Old and New through innovations, SAP, viewed 27 October. Available at: https://symposium.ch.tudelft.nl/2014/slides/6.pdf Chopra, S. and Meindl, P. (2013), Supply Chain Management: Strategy, Planning and Operations, 5th edition, Pearson Education Limited, Essex. Economic Development Board (2015), Using value chain mapping to build comparative advantage, Economic Development Board SA, Adelaide. Farm Weekly (2017), ‘Keeping an eye on farm data management’, Farm Weekly, April 1, viewed 26 October 2017. Available at: http://www.farmweekly.com.au/news/agriculture/agribusiness/general-news/keeping-an-eye-on-farm-data-management/2754911.aspx?storypage=2# Fegatilli, J. and Molina, R. (2017), ‘Smart Machines. Smart Decisions’, paper presented at Grain Growers Innovation Generation Conference, Adelaide, 3-5 July. Gordon, C. (2016), State of the Australian Grains Industry, GrainGrowers Limited, Canberra. Hajkowicz, S. and Eady, S. (2015), Rural Industry Futures – Megatrends impacting Australian agriculture over the coming twenty years, Rural Industries Research & Development Corporation, Canberra. Johnson, S. (2017), Grain Growing in Australia Industry Report, IBISWorld, Sydney. Jones, B. (2018), ‘Bungulla – business philosophy and innovation’, viewed 12 March 2019. Available at: http://www.ioa.uwa.edu.au/publications/industry-forum#Consolidation Keating, B. (2015), ‘Soil Big Data and Australian Agriculture – looking back, looking forward,’ CSIRO, viewed 26 October 2017. Available at: http://soilbigdata.com.au/sites/soil/media/470.pdf KPMG (2016), ‘Reaction Chemical Magazine What do data and analytics and changes in the personal care industry mean for chemical companies today?’ KPMG International Cooperative, viewed 26 October 2017. Available at: https://assets.kpmg.com/content/dam/kpmg/pdf/2016/07/REACTION-20-v2.pdf Nguyen, N., Mobdby, D. and Goesch, T. (2016), Farm-to-retail price spread and farm share in food supply chains, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. NMa (2009), Pricing in the agri-food sector, Netherlands Competition Authority. Available at: http://acm.nl/download/documenten/nma/NMa_Pricing_in_the_agri-food_sector_(2-12-2009).pdf Plunkett, B., Duff, A., Kingwell, R. and Feldman, D. (2018), “Capital structures for large scale Australian agriculture: issues and lessons”, Australasian Agribusiness Perspectives, Volume 21, Paper 9.

http://www.agriculture.gov.au/abares/research-topics/surveys/farm-performance

http://www.agriculture.gov.au/abares/research-topics/surveys/farm-performance

http://www.ioa.uwa.edu.au/publications/industry-forum#Consolidation



Rural Directions (2017), Opportunity for profit management guideline RDP00013. Rural Directions on behalf of the Grains Research and Development Corporation, Canberra. Spencer, S. (2016), Farm to retail – how food prices are determined in Australia, report prepared for the Rural Industries Research and Development Corporation, Canberra. Swan, D. (2017), ‘Bosch, KPMG, AgFunder, David Paradice back The Yield’, The Australian, April 6, viewed 26 October 2017. Available at: http://www.theaustralian.com.au/business/bosch-kpmg-agfunder-david-paradice-back-the-yield/news-story/4a161dc1061c6d1c9fa3149d8668337f



Appendix A

Source: Fegatilli and Molina (2017)

Documents

Australasian Agribusiness Perspectives 2019, Volume 22 ... · value chain and its competitiveness for firms. Grain production is a major industry in Australia and an important sector