Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate

Abstract

In Australia, a land subject to high annual variation in grain yields, farmers find it challenging to adjust crop production inputs to yield prospects. Scientists have responded to this problem by developing Decision Support Systems, yet the scientists’ enthusiasm for developing these tools has not been reciprocated by farm managers or their advisers, who mostly continue to avoid their use. Preceding papers in this series described the FARMSCAPE intervention: a new paradigm for decision support that had significant effects on farmers and their advisers. These effects were achieved in large measure because of the intensive effort which scientists invested in engaging with their clients. However, such intensive effort is time consuming and economically unsustainable and there remained a need for a more cost-effective tool. In this paper, we report on the evolution, structure, and performance of Yield Prophet®: an internet service designed to move on from the FARMSCAPE model to a less intensive, yet high quality, service to reduce farmer uncertainty about yield prospects and the potential effects of alternative management practices on crop production and income. Compared with conventional Decision Support Systems, Yield Prophet offers flexibility in problem definition and allows farmers to more realistically specify the problems in their fields. Yield Prophet also uniquely provides a means for virtual monitoring of the progress of a crop throughout the season. This is particularly important for in-season decision support and for frequent reviewing, in real time, of the consequences of past decisions and past events on likely future outcomes. The Yield Prophet approach to decision support is consistent with two important, but often ignored, lessons from decision science: that managers make their decisions by satisficing rather than optimising and that managers’ fluid approach to decision making requires ongoing monitoring of the consequences of past decisions.

Wheat physiology: a review of recent developments

“…Approximately 30% of ET must occur after AN for the maximum HI, as determined by cultivar and other aspects of climate, can be reached. Use of the wheat simulation model APSIM (see later) has further improved consideration of the effect of rainfall distribution, and simulations with historical weather suggests that PY w is best represented by a boundary function of about the same slope as determined by French and Schultz (1984), but which cannot be reached in all years because of poor rainfall distribution or water losses through deep drainage, run-off, or very late rain events (Hochman et al 2009b).…”

“…CERES 0.90 t/ha, and Sirius 0.90 t/ ha]. In Australia, APSIM-Wheat was developed from CERES and now dominates (Asseng et al 1998(Asseng et al , 2011Wang et al 2003;Hochman et al 2009aHochman et al , 2009bLudwig and Asseng 2010). The model uses daily time steps and includes a phenological framework, leaf expansion, crop growth driven by either radiation interception and RUE or transpiration and TE (whichever is most limiting), root penetration and layered soil water and N uptake, water and N stress indices feeding back into leaf area and RUE, and finally estimates of GN and GW.…”

“…weeds, disease) could be part of the error; inadequate physiology is presumably the rest. Interestingly, the model, using 'best bet' values for plant density, N and time of sowing, gives an estimate of PY w against which to benchmark the farmers' yields; the latter averaged 77% of PY w (Hochman et al 2009b).…”

“…APSIM-Wheat is certainly sufficiently accurate and user friendly to inform farm management decisions, especially in dryland situations where rain is uncertain, and yield response risk needs to be quantified, and adjusted according to somewhat skilful seasonal forecasts and unfolding seasonal weather; in this role in Australia it has been renamed Yield Prophet (Hochman et al 2009b). Model accuracy across a sample of 334 crops of 'elite' farmers over 2004-07, however, remains an issue; farmers want 0.5 t/ha accuracy while currently the RMSD of model versus observed is 0.8 t/ha, and there was a bias towards yield underestimate at high yield levels (Hochman et al 2009a, Fig.…”

Confronting Climate Change Challenges to Dryland Cereal Production: A Call for Collaborative, Transdisciplinary Research, and Producer Engagement

“…Decision support tools based on agrometeorological models have been developed or are in development for various semiarid regions to serve large-scale mechanized agriculture or smallholder systems (Sadras et al, 2003;Hochman et al, 2009;McCown et al, 2009;Chen, 2017;Prokopy et al, 2017; http:// climateengine.org, https://www.agbizlogic.com). The increasing accessibility of data, continuing innovation, and improvement of user interfaces, and improvement of climate models is certain to accelerate development and deployment of these tools into the future.…”