ABSTRACT
Precision agriculture (PA) has been identified as one of several technologies with the potential to assist in enhancing agricultural productivity (Carberry et al., 2011) and eco-efficiency (Keating et al., 2010). In particular, it is seen as a means of maintaining agricultural output while reducing the risk associated with producing that output (Figure 12.1). This is an idea that is consistent with the suggestion that a basic principle of PA is to increase the likelihood of a beneficial outcome by better targeting of inputs to production potential (Cook and Bramley, 1998; Lawes and Robertson, 2011). Note that in Figure 12.1, ‘risk’ is synonymous with inputs (e.g. amount of fertilizer applied) or the cost of inputs. Of interest in the context of this chapter is the suggestion (Figure 12.1) that, whereas PA improves productivity through reduction of risk, the harnessing of genotype by environment by management (G × E × M) interactions is one of the principal avenues by which productivity may be increased for the same level of risk. Here, we contend that PA may also assist in raising productivity for the same level of risk by exploiting the G × E × M interactions that may exist at the sub-field scale. A return-risk framework and technologies that have the potential to affect Australian dryland agriculture over the next 20 years and beyond (Carberry <italic>et al</italic>., 2011, adapted from Keating <italic>et al</italic>., 2010). https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780203128329/a011d8fc-9f32-4393-94eb-d61d75d70272/content/fig12_1_B.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/>
Note
A and D are points on the efficiency frontier (solid line) for the best technologies used in 2010, and C and F are points on a new efficiency frontier (dashed line) which arises through the use of new technologies. B represents a position below the current efficiency frontier. GM = genetically modified; G × E × M = genotype by environment by management interactions; ICT = information and communication technologies. Note that Carberry et al. (2011) also identified technologies that affected Australian dryland agriculture in the 30 years prior to 2010. These included controlled traffic (B to D), conservation agriculture (D to F) and fertilizer management (D to C).
