Untangling the complex mix of agronomic and economic uncertainties inherent in decisions on rainfed cotton

• Published in: Crop and pasture science Vol. 74; no. 12; pp. 1223 - 1237
• Main Authors: Godfrey, Sosheel S., Nordblom, Thomas L., Anwar, Muhuddin Rajin, Ip, Ryan H. L., Luckett, David J., Bange, Michael P.
• Format: Journal Article
• Language: English
• Published: CSIRO Publishing 09.05.2023
• Subjects: dryland cotton; Gossypium hirsutum L; gross margin; management strategies; OZCOT for cotton; probabilistic model; Research Paper; risk
• ISSN: 1836-0947; 1836-5795
• DOI: 10.1071/CP22145

Context. Production of rainfed (dryland) cotton (Gossypium hirsutum L.) occurs in many places globally, and is always burdened with greater uncertainties in outcomes than irrigated cotton. Assessing farm financial viability helps farmers to make clearer and more informed decisions with a fuller awareness of the potential risks to their business. Aim. We aimed to highlight key points of uncertainty common in rainfed cotton production and quantify these variable conditions to facilitate clearer decision-making on sowing dates and row configurations. Methods. The consequences of these decisions at six locations across two states in Australia, given estimates of plant-available water at sowing, are expressed in terms of comparable probability distributions of cotton lint yield (derived from crop modelling using historical weather data) and gross margin per hectare (derived from historical prices for inputs and cotton lint yield), using the copula approach. Examples of contrasting conditions and likely outcomes are summarised. Key results. Sowing at the end of October with solid row configuration tended to provide the highest yield; however, single- and double-skip row configurations generally resulted in higher gross margins. Places associated with higher summer-dominant rainfall had greater chance of positive gross margins. Conclusion. In order to maximise the probability of growing a profitable crop, farmers need to consider the variabilities and dependencies within and across price and yield before selecting the most appropriate agronomic decisions. Implications. Given appropriate data on growing conditions and responses, our methodology can be applied in other locations around the world, and to other crops.