Excerpt from “Fast Graphs for slow thinking– an example using nitrogen” by Peter Hayman (SARDI Climate Applications) and Barry Mudge (Mudge Consulting)
Fast Graphs for Slow Thinking is a reference to the book ‘Thinking Fast and Slow’ by Daniel Kahneman (winner of Nobel Prize for economics). Kahneman distinguishes between fast thinking, which is instinctive, recognises patterns and jumps to conclusions, and slow thinking, which is more deliberative and logical. Fast thinking is efficient, and part of that efficiency is the quick creation of a coherent, plausible narrative.
Fast Graphs for Slow Thinking is one approach to simulate thinking for improved decision making. Comparing the upside and downside of a decision involves weighing a range of possible futures. This is mentally demanding, but relatively easy in a spreadsheet. Our idea is to get the information quickly into a graph that shows the upside and downside of a Nitrogen fertiliser investment (we estimate less than 20 minutes), so that we can then have a useful conversation about the risky decision.
Analysis to explore upside & downside of N applications
The aim of the tool development was to explore how the upside, downside and probability weighted average of N decisions are changed by the cost of N and price of wheat, levels of carryover N, and seasonal climate forecasts. In doing this, we were testing the usefulness of a simple decision analysis to run the N budget across deciles, rather than pick a single target yield.
Fast Graphs for Slow Thinking output showing the water and nitrogen limited yield (left hand panel) and the profit by decile graph (right hand panel). The profit by decile graph is for the application of 40kg N, which is similar to aiming for decile 8 where the gap between the water and N limited yield is 1t/ha.
Using Fast Graphs for Slow Thinking, using the budget assumptions, the worst case is a loss of $70/ha ($60 of Urea for 40kg N + $10 for application). The best case (or decile 10) is profit of $280/ha. The upside wedge is substantially better than the downside, and the probability weighted average profit is $70/ha (Figure 2 right hand panel at the bottom).
Impact of N carry over and seasonal forecasts
Uncertainties of the proportion of extra N applied that may be available to subsequent crops and/or how climate forecasts may influence the outcome can also be explored.
The graphs below show how carryover N of 50% reduces the loss in poor seasons but has no impact in good seasons because there is no unused N in rainfall deciles 8 and above. The long-term average improvement in this example for including N carryover is $18/ha ($88/ha up from $70/ha) where there is an equal distribution of rainfall deciles (left).
A shift in the odds from 50% chance of above median rainfall (left) down to only 30% will adjust the shape of the upside and downside (right). Importantly, a forecast doesn’t change the possible profit. A forecast doesn’t change the future, it changes the likelihood of different future outcomes occurring. In other words, the forecast changes the width of the downside and upside wedge, not the height. If we assume 50% carryover of N, the climate outlook change from >50% of mean rainfall down to >30% of mean rainfall reduces the probability weighted average from $88/ha down to $33/ha, and the frequency that a loss occurs increases from 42% to 61% of predicted outcomes (left compared to the right). Interestingly, the average improvement of including N carryover where there is a negatively skewed distribution of rainfall increases to $23/ha because there is a predicted increase in likelihood of lower rainfall years when this inclusion is more important.
Return (profit/ha) across rainfall decile outcomes as influenced by assuming no carry-over of extra unused N (orange line) vs assuming 50% of extra N was available to offset costs in the next crop, under a climate forecast of 50% (on the left where all coloured rectangles are evenly weighted) down to 30% (on the right where there is more weighting placed on the lower decile years).
Towards considered decisions
As the psychologist Paul Slovic says, ‘our emotions are not good at arithmetic, we tend to think of future events as 100% or 0%’. Revising the likelihood of deciles based on a forecast is easily done in a spreadsheet and growers easily recognise patterns of shifts in graphs, especially if they were involved in providing the underlying information.
The RiskWi$e project is about better understanding risk and reward in all parts of the grain farm and it provides a rich opportunity for conversations about the risk and reward of N use in our grain production systems. Because getting N fertilisation exactly right is almost impossible due to the variable climate, it is better to consider the consequences of erring on the side of applying a bit extra N or too little N. The cautious approach of too little N can have a substantial cost of missing the opportunity of turning 40kg of N to 1t of wheat.
Want to learn more?
Click here to view a video of Barry Mudge and Peter Hayman discussing N topdressing in 2024 using the spreadsheet tool.
Copies of the spreadsheet for evaluation are available from Peter.Hayman@sa.gov.au