Professor James Hunt explains that it is difficult to make the ‘right’ N decision every year, but ‘good’ decisions are relatively easy and are more likely to be ‘right’, particularly when reviewed over several years.

“The ‘right’ N fertiliser decision maximises profit, replaces N taken out in grain plus any N lost from the soil, and minimises chances of losing money in any given year.”

Link to the paper here – Nitrogen fertiliser decisions – the good, the right and the risky – GRDC

Background

Risk is a term that has taken on many definitions, but is commonly used to describe the chance of something negative happening. However, the more formal definition is that there is a range of outcomes associated with an action (both positive and negative), and that the probabilities of the different outcomes are known. This definition perfectly describes N fertiliser decisions in Australian cropping systems. When a decision is made about how much fertiliser N to apply to a crop and when, there is a range of possible outcomes in terms of the effect on yield, quality, logistics, labour and profit (some positive, some negative). The probabilities of these different outcomes can be estimated, based on soil tests for water and mineral N, and historic climate data, either using a complex tool like Yield Prophet® or simpler tool based on rainfall deciles like Yield Prophet Lite.

‘Good’ and ‘Right’ decisions

Nitrogen decisions are a clear demonstration of the difference between ‘good’ and ‘right’ decision making. ‘Right’ or ‘wrong’ relates to the outcome of a decision. For this discussion, ‘good’ or ‘bad’ relates to the process of deciding. A ‘good’ decision is based on available evidence and information, with appreciation of both likelihood (probability) and consequences (outcomes) of any action that is taken (Nicholson 2019). A ‘bad’ decision is made in the absence of evidence and relies on assumptions and guesses. ‘Good’ decisions can be both right and wrong when reviewed in hindsight. Whether an N fertiliser decision is ‘right’ or ‘wrong’ largely depends on seasonal conditions following N application (rainfall, temperatures, etc.), which, in Australia, are highly variable. Whether an N decision is ‘good’ or ‘bad’ depends on the evidence used to make the decision, and if the performance of the decision-making process or system is reviewed and adjusted over the longer term.

How to make ‘good’ N fertiliser decisions

GRDC RiskWi$e has been evaluating different N fertiliser rate decision-making processes (Table 1) in a national network of field experiments. An emerging finding from this network of experiments is that there are lots of ways to make ‘good’ N decisions that use different sources of evidence, and that there are ‘good’ decision-making processes available that suit a broad range of decision-making preferences. Some of the decision-making processes are highly active and analytical, and require a lot of input information and time on behalf of the decision maker, e.g. Yield Prophet. Others, such as N banks or Financial approaches, are more passive, require less information, and don’t explicitly consider risk in the decision-making process, but an appreciation of risk is embedded in their derivation.

Early results from RiskWi$e experiments across the country show that long-term average N rate explains >90% of variation in yield. This means that ‘bad’ decisions are not based on evidence in the year of application can be ‘right’ if, over the long term, they achieve the average optimum N rate for a given environment. This means that very passive N decision-making processes (e.g. same N rate on every crop in every year) can also achieve close to maximum yield and profit, provided the outcome of the decision process are reviewed over the short and long term, to ensure the decision is ‘right’. Reviewing and adjusting simplistic decisions in this way validates them with evidence, so even the simplest of decisions need to be evidence-based to be ‘good’.