Weed Control Decisions Research Articles

Weed economic thresholds in cereals in the Federal Republic of Germany

The determination of fixed guide values for weed economic threshold in cereals is briefly reviewed. A 2-year experimental series in winter cereals to validate these threshold values in the Federal Republic of Germany is described. It was found that the use of fixed threshold values to decide when to control annual weeds was more profitable than prophylactic herbicide application. The fixed threshold values are 20–30 plants m −2 for grass weeds, broad-leaved weeds 40–50 plants m −2, Galium aparine 0.1–0.5 plants m −2 and Fallopia convolvulus 2.0 plants m −2. Alternatively, broad-leaved weeds have to be assessed as percentage ground cover, 5–10% being the threshold. In order to improve further the predictions from the economic thresholds a more flexible computer-based decision model has been developed. This includes updated biological data and simple economic calculations. Further experiments have been done to validate the model. Assessments based on the calculated net return indicate that weed control decisions were slightly improved by model recommendations compared with the fixed threshold concept. The basic assumptions in the decision model are discussed and possibilities for future applications under practical conditions are described.

RESPONSES TO RISK IN WEED CONTROL DECISIONS UNDER EXPECTED PROFIT MAXIMISATION

Risk is an important characteristic of decisions about weed control in crops. In this paper it is shown that risk can affect weed control decisions even if the objective of the decision maker is to maximise expected profits: that is, even if the decision maker is ‘risk‐neutral’ in the usual economic sense. This is shown for two decision frameworks: the optimal rate approach and the economic threshold approach. Empirical results are presented for control of ryegrass in wheat in Western Australia. It is found that, in general, risk reduces the optimal level of herbicide use under expected profit maximisation. Although individual sources of risk have a small impact on the optimal decision rules, combinations of uncertain variables can have a relatively large effect.