Climate forecasts and dryland cotton growing

Seasonal climate forecasts were found to improve the profitability of dryland cotton systems by between $0 and $517/ha by improving decisions about planting.

How can seasonal climate forecasts provide economic value to farming enterprises?

Seasonal climate variability is a key source of year on year variability in farm profitability. Seasonal climate forecasts provide opportunities for farmers to better match farm decisions with upcoming climatic conditions. These forecasts can provide economic value if they change management decisions to capitalise on opportunities in good seasons or minimise losses in poor seasons.

While seasonal climate forecasts help manage production risks associated with climate variability, they do not remove the impact of a particular climatic event. For example, a skilful forecast can reduce uncertainty about drought occurrence, but drought influences productivity and profitability however well farmers are able to anticipate it.

Dryland cotton growing

An important management decision for dryland cotton growers is whether to leave the field for fallow or to plant cotton, and if planted, when and to what planting configuration. Planting earlier will lead to larger yields (due to a longer growing season) but there may be insufficient in-crop rainfall to support the crop. Later planting is likely to increase the chance of crop success but at potentially lower yields. Fallow for winter cropping may be more profitable if in-crop rainfall is low.

A skilful seasonal climate forecast may influence this decision due to the relationship between rainfall and cotton yield.

Can seasonal climate forecasts improve cotton production?

A case study cotton enterprise located at Bungunya in Queensland was used to test how a seasonal climate forecast could help growers make cotton planting decisions.

A decision model identified the most profitable of five planting strategies (plant in October or November to single or double skip or leave to fallow) with and without a climate forecast. Increasingly skilful climate forecasts provided greater levels of certainty about the occurrence of one of three climatic states (dry, average and wet), allowing growers to choose more profitable planting options.

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Key findings

Initial soil moisture had a strong influence on the cotton planting decision. High soil moisture levels at planting led to an optimal decision to plant cotton in November at single skip row configuration in the absence of forecast information. In contrast, under low initial soil moisture fallow was predominately selected.

Cotton prices and whether sowing rains were received were also important. When cotton prices were high and sowing rains were received, cotton was planted when initial soil moisture was 50% of plant available water content (PAWC). When cotton prices were low and sowing rains were absent, fallow was selected when initial soil moisture was 75% PAWC.

Different planting decisions were selected under dry and wet forecasts. In general, a dry forecast more often led to fallow being selected. This reflected greater certainty of dry conditions and changed the optimal decision from planting a crop to fallowing the field to store water for a subsequent crop. Conversely, a wet forecast modified decisions towards planting cotton. In this case, greater certainty of a wet forecast allowed for more certainty of sufficient in-crop rainfall to carry a profitable crop.

Seasonal climate forecasts for dry or wet conditions were found to be potentially valuable to growers. Returns were improved by $517/ha (wet) to $352/ha (dry) when the decision to fallow or crop was reversed, and initial soil moisture was low. Forecasts of average conditions were found to have no value.

While the value of seasonal climate forecasts increased as forecast skill improved, the relationship between skill and value was influenced by initial soil moisture, cotton price and whether sowing rains were received.

Important: The results for other sites, systems and decisions will differ from those in this case study. However, it is likely that the general findings around the circumstances for which forecast value was found will provide insights for the use and value of seasonal climate forecasts for cotton growers more generally.