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Assessing the risk of cotton ‘earliness’ management strategies with crop simulation.

D.Q. Richards, M.P. Bange and G.N. Roberts

Australian Cotton Co-Operative Research Centre
Cotton Research Unit, CSIRO Division of Plant Industry, Narrabri, New South Wales.


Earlier crop maturity (earliness) in the Australian cotton crop is an attribute many growers seek to attain, with the primary aim to avoid climatic influences on yield and quality, and late season insecticide applications, allowing a reduction in costs and helping to avoid insecticide resistance. Crop simulation models offer the ability to assess many crop management options that affect earliness over many locations. This paper compares the results of field measurements of crop yield and maturity, with information generated by the model. The OZCOT model simulated yield accurately but maturity was not modelled accurately, indicating an area for future work. Long-term climate information is also used to identify “windows of opportunity” for growers to manage crops for earliness and avoid crop losses due to climatic influences.


Earliness, models, climate, risk, irrigation, nitrogen.


‘Earliness’ is a term peculiar to the cotton industry, and embodies the desire to harvest the crop as soon as possible without incurring a significant yield penalty. Delayed crops increase chemical costs and risk exposure to greater degrees of climatic risk, which may affect parameters such as fibre quality. Factors affecting earliness include sowing date, insect control, soil nitrate level at sowing and the timing of last irrigation. Simulation models offer an opportunity to simulate crops over a wide range of environmental and management conditions, and are a means of easily achieving understanding of a system and not suffering the consequent pain and cost of real-life experiments (2). Australian cotton is grown in one of the world’s most variable climates, particularly rainfall, however an analysis of the rainfall distribution during March, April and May will provide some insight into ideal dates for cotton harvest.


A simulation model is simply a mathematical representation of the physiology of a crop. The OZCOT computer simulation model uses historical weather data, basic soil parameters and defined crop management options to give estimates of potential crop yields. The OZCOT model has been developed over the past 30 years, and incorporates research into the response of cotton to climatic variables (5). When used in conjunction with accurate measurements of soil water and nitrogen just prior to sowing, OZCOT has been shown to successfully compare alternative agronomic management options. A validation sequence of model runs was conducted using information collected during 1999/2000 (7), covering two different climatic regions. Further details about these earliness trials can be found in Roberts and Constable (7) contained in these proceedings. These simulations used the attributes listed in Table 1 to initialise the model and were used to assess the application of the OZCOT model in quantifying earliness strategies.

Table 1. Initialisation information used for OZCOT validation runs: long season variety (L), short season variety (S).

Strategic modelling aims to give probability distributions of potential crop yield by changing only one parameter at a time. During the simulations, a crop is grown using identical sowing conditions every year, with the model deciding when to irrigate the crop based on a defined soil moisture deficit. By running the simulation over the 40 years, each crop will experience a wide range of climatic conditions and will give an indication of variability in cropping response and yield. In this paper we present strategic runs based on soil nitrogen level (50kg to 300kg, in 50kg increments) and the date of last irrigation based on percent open bolls (1%, 20%, 40%, 60%).

For the purpose of assessing earliness in the validation and strategic simulations, the day of year (DOY) when 60 percent bolls were open was chosen as the definition of maturity, while yield was measured in bales per hectare of lint.

Climate risk analysis involved compilation of rainfall records for Dalby, Goondiwindi, Boggabri, and Warren. These sites were chosen to represent a range of temperature and rainfall distributions where cotton is grown. Assessments of monthly rainfall totals and the optimum harvest window were made, and their implications for crop earliness discussed.


The results for yield and maturity from the validation simulations is presented in Figure 1. The potential yield predictions that are presented are between 0.16 to 0.7 bale/ha above actual yields, and indicate that the model can accurately simulate yield. Overestimation results from not being able to simulate factors such as insect damage and other crop management impacts. The application of these potential values and trends between yield predictions will be useful in the strategic simulations presented later. The maturity predictions were not accurate, showing no significant trend between actual and predicted maturity dates.

Figure 1. Earliness validation simulation results, actual against predicted yield.

The strategic simulations assessed two factors known to affect earliness, soil nitrate level (kg/ha) at sowing and the timing of last irrigation, using the long season variety grown at Goondiwindi and Boggabri. Figure 2 shows the results using soil nitrogen increments of 50kg/ha, indicating an optimum yield is achieved at 200kg/ha, which is consistent with the results of Rochester et al (3). As the validation simulations indicated the model did not predict maturity accurately, the maturity information for the strategic simulations was not used. Research findings from the United States presented by McConnell et al (6) show that increasing the nitrogen rate reduces earliness. This is consistent with findings under Australian conditions at Narrabri, NSW, where every 50kg of nitrogen delayed maturity by 2 days, and significant delays were experienced above the optimum nitrogen rate of 200kg (I. Rochester, CSIRO, unpublished data).

Figure 2. Soil nitrogen at sowing simulation results for Goondiwindi and Boggabri showing potential yield: Points shown are average (), 20th percentile () and 80th percentile ().

The timing of last irrigation simulations are presented in Figure 3 and show a different response at the two locations, possibly due to climate. Very little change in yield could be achieved at Boggabri by varying the date of last irrigation from 1% to 60% open bolls, possibly indicating that other parameters, such as temperature, are impacting on crop yield. An analysis of Goondiwindi results shows a potential to increase yield with later irrigations, but this may have negative implications for maturity. This yield increase is consistent with results presented by Hearn (4), and Hearn and Constable (5), where yield was shown to increase with later irrigations but the duration of protection from insects was prolonged and harvest delayed due later maturity.

Figure 3. Last irrigation simulation results for Goondiwindi and Boggabri showing potential yield: Points shown are average (), 20th percentile () and 80th percentile ().

The primary aim of an earliness strategy must be to match maturity and harvest with the most ideal climatic conditions. An analysis of monthly rainfall at Goondiwindi and Boggabri indicates that April is the optimum month for cotton harvest based on mean monthly values (Table 2). Dalby and Warren have the same result, however as there was little difference between April and May totals; growers in these regions have more flexibility at harvest. Probabilities can be used to put some error bounds on these mean values, and often give a better indication of true variability. The percentile values indicate that in 50% of years, April is likely to have the lowest monthly rainfall at Goondiwindi, Boggabri and Warren, and at Dalby has only 1mm more than in May. By bringing a crop to maturity earlier in March, all sites show an increased probability of receiving rain. A greater percentage of open bolls would experience this rainfall, which would negatively affect fibre quality and attract possible quality penalties. Temperature interactions may influence the impact of this rain, as warmer temperatures during March will dry the crop and soil faster, and allowing harvesting to proceed sooner than in later, cooler months.

Table 2. Probability of exceedance statistics for amounts of rain (mm) received during March, April and May at Dalby, Goondiwindi, Boggabri and Warren. Source: Australian Rainman.


Many earliness strategies are site specific and have the potential to negatively affect yield and maturity if adopted. The OZCOT model has been shown to provide qualitative yield information on the effectiveness of a limited number of management strategies. Further work is needed to help refine the model ability to simulate crop maturity and physiological studies currently being conducted by Bange and Milroy (1) will assist this aim. An analysis of monthly rainfall by the Southern Oscillation Index may reveal further trends that would assist cotton growers to form an earliness strategy that would be flexible from season to season.


Thanks to Clare Felton-Taylor for technical assistance, and to the growers involved with the earliness trials.


1. Bange, M.P. and Milroy, S.P. 2000. Field Crops Research. 68, 143-155

2. Carberry, P. and Bange, M. 1998. Proceedings 9th Australian Cotton Conference. Broadbeach, p153-160

3. Rochester, I.J., Peoples, M.B, Constable, G.A. and Gault, R.R. 1998. Aust J. Exp. Agric. 38, 253-260

4. Hearn, A.B. 1975. J. Agric. Sci. Camb. 84, 419-430

5. Hearn, A.B. and Constable, G.A. 1984. Irrig Sci. 5, 75-94

6. McConnell, J.S., Baker, W.H., Miller, D.M., Frizzell, B.S., and Varvil, J.J. 1993. Agron. J. 85, 1151-1156

7. Roberts, G.N. and Constable, G.A., 2001. Proceedings 10th Australian Agronomy Conference, These Proceedings

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