Innovating Down the Cost Curve by Trial and Adoption of Successive Innovations on a Large Scale, Specialist Grain Enterprise in Western Australia

Abstract

A study by Kingwell et al (2014) identified that while scale economies underpin Total Factor Productivity (TFP) growth, farm performance was also critically important. Farm performance was driven only by ‘the farm manager’s preparedness and ability to use cropping innovations and their skill in organisation and time management’. This paper uses the Jones’ property in Tammin to provide a case study of how a farm can achieve improved TFP through adoption of cropping innovations, organisation and time management. The Jones’ dominant business philosophy for their 10,000 ha grain enterprise is to achieve target returns with secure cashflow. To secure cashflow, they seek to avoid negative event outcomes that have unknown probabilities. Secure cashflow implies that they can use comparatively small amounts of that cashflow to de-risk innovations designed to lift returns by lowering costs and securing market opportunities. The outcomes from selected innovations are trialed, measured and assessed and are subsequently adopted across the farm if target returns are met. Their approach is the detailed subject of this paper, with specific exploration of the efficiencies they plan through logistical efficiencies via the adoption of WiMesh intra farm connectivity coupled with sensors across the farm (trial, 20% of farm: 2018/full adoption: 2019) in boom operation. Similarly, their connectivity is also employed to deliver a range of other benefits, particularly related to weather event decision making. Cumulatively, these changes are designed to drive all in per hectare costs to comparatively low levels and increased TFP across the farm. The Jones’ report that IT integration has resulted in a definite improvement to the farm’s modus operandi, as it compliments other means to reduce management complexity. The Jones’ won the ‘2016 Cropper of the Year award maximising technology and science, (that works) on an impressive 15 per cent return on investment – more than three times that achieved by the average WA grain farm’ (Wagstaff, 2017). Importantly, the Jones’ have sufficient management depth and financial resources to devote to the adoption of on-farm AgTech; indeed, it is integral to their business success. However, ongoing competitive pressure from emerging competitors imply that further efficiency gains at farm level and along supply chains, in addition to innovations to deliver a greater array of value to customers (i.e. value chain formation), will be needed for the industry to remain competitive. From a public policy perspective, at issue is the best means to affect the greatest industry wide gains through adoption of an objective of TFP growth. Opportunities to create and encourage the adoption of low cost options for smaller, more capital constrained farms to access the benefits of digital agriculture, defined as ‘having the right farm data at the right time to make a better decision’ will be essential to reduce costs. To this may be added securing the human capacity to use this data on farm, and in keeping with Kingwell et al (2014) above, the role of targeted public extension to drive adoption and TPP growth by skills that lessen management complexity. On farm trialing to establish the validity of claims made about new technologies may also play an important role in accelerating the adoption of these technologies (Jackson and Malcolm, 2018). This is the first of a six paper series on farmer AgTech adoption in the WA grains industry that use open source information technology devices to solve for production problems that increase productivity. The first five papers are case studies outlining the business drivers of the adoption of differing types of AgTech adoption. A key proposition under investigation is that the particular circumstances of a farm implies that each farm will have a different path of open source information technology adoption that will give it the best risk adjusted rate of return. They consider the variables of farm scale (three are at or above minimum efficient scale – MES – for cropping), enterprise mix (three are mixed farm operations), rainfall zone and management structure/depth and group vs. single farmer adoption. The sixth paper is a summary document of key themes, – including the interaction of adoption with on and off farm connectivity and data integration – and public policy implications. It will discuss private and public structural and strategic options to deal with connectivity and complexity issues that are necessary for WA agriculture to access the productivity gains possible from adopting the full suite of available technologies. Interested readers are directed to a video link of a ‘farm table discussion’ on Bungulla’s system between Mr. Jones and the author.