SO1 PHASES AND CLIMATIC RISK TO PEANUT PRODUCTION: A CASE STUDY FOR NORTHERN AUSTRALIA

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 16, (1996) SO1 PHASES AND CLIMATIC RISK TO PEANUT PRODUCTION: A CASE STUDY FOR NORTHERN AUSTRALIA ti. MEINKE, R. c. STONE AND G. L. HAMMER DPI/CSIRO Agricultuml Production Systems Research Unit, PO Box 102, Toowoomba, Qld 4350, Australia holgetmni@apsiusg. sth. dpl. qld.gov. au Received 16 December I994 Accepted 31 October 1995 ABSTRACT Phases of the Southern Oscillation Index (SOI) in AugusdSeptember are used in conjunction with a dynamic peanut simulation model to quantify climatic risk to peanut production in northern Australia. Specifically, we demonstrate how a simulation model can assist to forward estimate production risk based on historic climate records and hown atmospheric conditions prior to planting a crop. The SO1 phase analysis provides skill in assessing future rainfall probability distributions during the growing season and thus allows an estimate of likely crop performance. Such knowledge can provide valuable information for producers and processors. For instance, the analysis shows that for negative SO1 patterns prior to sowing the expected median yield potential for dryland peanut production in northern Australia is 1.25 t ha; or 27 per cent below the long-term median. Conversely, a positive SO1 pattern shows a median potential yield of t ha-, an increase of 23 per cent over the long-term median. Other production variables, such as date and frequency of planting opportunities, also differ significantly depending on SO1 patterns. KEY WORDS: Southern Oscillation Index; rainfall probability; peanut simulation model; climatic risk; agricultural production; northern Australia. INTRODUCTION Seasonal rainfall in many areas of eastern Australia is influenced by the ocean-atmosphere El Niiio-Southern Oscillation (ENSO) phenomenon (McBride and Nicholls, 1983; Allan, 1988). Significant correlation, both lag and non-lag, exist between an index of ENSO, the Southern Oscillation Index (SOI) and rainfall in eastern Australia. An ENSO warm-event (El Niiio), which generally corresponds to negative SO1 values, usually lasts for about 12 months, beginning its cycle in the austral autumn period of one year and terminating in the autumn period of the following year. During the termination of an ENSO warm-event the SO1 may rise sharply. Stone and Auliciems (1 992) have shown how phases of the SO1 are related to rainfall variability and rainfall forecasting for a range of locations in eastern Australia. They have shown that a rapid rise in SO1 over a 2-month period is related to a high probability of above long-term average rainfall. Conversely, a consistently negative or rapidly falling SO1 pattern is related to a high probability of below average rainfall. Because the SO1 pattern tends to be phase-locked into the annual cycle (from austral autumn to autumn), the SO1 phase analysis also provides skill in assessing future rainfall probabilities during the following growing season. Stone and Auliciems (1992) suggested that such information could assist in making decisions related to agricultural production. The SO1 phase analysis provides skill in assessing hture rainfall probability distributions for the forthcoming growing season and thus allows estimates of likely crop performance. Several studies have already demonstrated how such SO1 information can be applied and how decisions based on such information can contribute to increased profitability of rural enterprises (e.g. Hammer et al., 1991; Meinke and Stone, 1992; Stone et al., 1993). Australian peanut production is particularly susceptible to adverse weather conditions, such as drought and excessive rainfall at harvest. It has been suggested that the profitability of the Australian peanut industry could be increased by using future knowledge of atmospheric conditions obtainable prior to planting to forward estimate production levels and risk (Meinke and Hammer, 1995b). Such information could be used by individual growers for CCC /96/ by the Royal Meteorological Society

2 784 H. MEINKE, R. C. STONE AND G. L. HAMMER tactical decision making and by the industry for forward planning of resource allocation, such as storage capacity or drying facilities. This is particularly true in regions with high rainfall variability, such as subtropical areas of eastern Australia where variations in rainfall frequency and amount cause most of the production uncertainties faced by producers (Hammer et al., 1987; Meinke et al., 1993). An assessment of climatic risk to crop production can be improved by simulating likely crop yields using dynamic crop simulation models, providing that long-term climatic data are available (e.g. Hammer et al., 1987; Carbeny et al,, 1993; Meinke et al., 1993). Using this approach, a wider range of climatic variability, along with its impact on production, can be included in the analysis (Meinke and Hammer, 1995b). In this paper we combine probabilistic climatic forecasting systems with simulation techniques for a region. For this case study we selected the main peanut producing area in Australia, the region around the rural centre of Kingaroy in Queensland ( , E), which is characterized by a subtropical, summer-rainfall-dominated climate (Meinke and Hammer, 1995a,b). We demonstrate how the SO1 based forecasting system developed by Stone and Auliciems (1 992) can have direct application to agricultural decision making by providing production forecast estimates for rural producers as well as processors. SO1 phases METHODS The forecast method used for this study is based on knowledge of SO1 phases in the two months just prior to the period for planting the peanut crop, i.e. August and September. Briefly, the method uses principal components analysis and cluster analysis to categorize SO1 values into five phases or types (Stone and Auliciems, 1992). The SO1 phases comprise: (i) cons -ve (consistently negative SOI) with a mean SO1 value over a 2-month period of and a mean difference between the 2 months of 2.3; (ii) cons +ve (consistently positive SOI) with a mean SO1 value over a 2-month period of +9.5 and a mean difference between the 2 months of 1.7; (iii) rapid fall (rapidly falling SOI) where the mean SO1 value for the initial month is +2.7, the value for the subsequent month is - 9.9, and the mean difference in values is ; (iv) rapid rise (rapidly rising SOI) where the mean SO1 value for the initial month is -4.4, the mean SO1 value the subsequent month is f6.6, and the mean difference in values is + 11.O; (v) near zero (SO1 values consistently near zero with little change over the preceding month) with a mean SO1 of and a mean difference of Simulation procedures The peanut simulation model used in this study predicts peanut yields for given soil moisture and climatic conditions. The model is able to appropriately simulate potential yield and is described in detail by Hammer et al., (1 995). The model requires daily meteorological data (i.e. minimum and maximum temperature, solar radiation and rainfall) as inputs. Leaf area index is determined as a function of mean daily temperature and daily biomass accumulation is calculated as a linear function of the intercepted solar radiation. Both leaf area and biomass production, are sensitive to the amount of soil water available for transpiration. A simple soil water balance takes account of rainfall, evaporation, and transpiration throughout the year. Yield is calculated as a function of biomass production and growing conditions during yield formation. Although a case study for a site in northern Australia is presented here the model can be applied generally and has been tested on a range of data sets from Indonesia, USA, and Australia. Developmental phases of the crop are related to thermal time. The model does not account for factors such as suboptimal nutrient status, waterlogging, pests, or diseases. Long-term, daily rainfall records for Kingaroy were available from late Some actual temperature and solar radiation data were also available and used to calibrate the weather data generator WGEN (Richardson and Wright, 1984), which was then used to recreate a daily record of

3 CLIMATIC RISK TO PEANUT PRODUCTION, NORTHERN AUSTRALIA 785 temperature and radiation data corresponding to the recorded rainfall data. The adequacy of this approach for simulation analyses is demonstrated elsewhere (Meinke et al., 1995). In this study, we used simulations described in detail by Meinke and Hammer (1995b). Briefly, the model was run for 85 successive years for a peanut monoculture followed by a winter fallow (1 907 to ). A peanut crop could be sown (i) between 15 October and 3 1 December and (ii) if a minimum of 30 mm of rain fell within 5 days, and (iii) if a minimum of 35 mm of plant available soil moisture was stored in the soil profile. All these rules were derived in accordance with local practices and knowledge. Up to two successive planting opportunities were considered. Losses due to rain at harvest were estimated. Such losses occur due to fungal attacks of pods, which is associated with wet conditions causing delays in harvest. No harvest losses were predicted if the total crop was harvested within 15 days after reaching maturity. Thereafter harvest losses were incurred at a rate of 5 per cent per day, resulting in a total crop loss after 35 days. For further details see Meinke and Hammer (1 995b). To quantify the impact of current skill of long-term rainfall forecasts, simulated output variables (i.e. date of first planting opportunity, potential yield, harvest losses, harvestable yield, and the number of planting opportunities) for the first and second planting opportunity in the season were categorized according to their preceding austral spring SO1 category. For each SO1 category cumulative distribution functions (CDFs) were calculated and tested for significant differences applying the Kolmorogov-Smirnov test. The advantage of the Kolmorogov-Smirnov test is that it detects all types of differences that may exist between two unknown distribution functions rather than just differences in median or mean values (Conover, 1971). The test detects the largest difference between two distributions, calculates a test statistic for that difference and then compares it to a tabulated test statistic at a given probability level. Analysis of model output RESULTS AND DISCUSSION The CDFs for each output variable associated with the first planting opportunity in the season were calculated for each SO1 phase and compared statistically. A number of model output variables showed significant variation according to SO1 phase prior to planting (Table I). This indicated a useful degree of predictability of the type of the forthcoming season. None of the output variables were normally distributed and because mean and median values of non-parametric distributions can differ considerably, both are presented in Table 1. In some months or seasons, due to the phase locking nature of ENSO, low numbers of cases occur in some SO1 phases. This is the case in the austral spring and, consequently, low numbers of cases in phases rapid fall and rapid rise resulted in nonsignificant differences for most variables in these groups (Table I). The significance levels indicated are derived by testing the whole distributions for differences rather than just their means or medians. This explains the apparent contradiction of having significant differences between identical values. In considering the second planting opportunity in the season, we first calculated the CDF of the yield difference for potential and harvestable yields between first and second planting opportunity for each year by subtracting the value for the first opportunity from the value of the second opportunity. Consequently, a negative yield difference indicates a yield advantage of the first opportunity over the second. We found no significant differences in yield between SO1 phases and therefore only present the all years case of all 85 seasons in Figure 1. It shows that in 70 per cent of years both potential and harvestable yields are higher for the first planting opportunity and in only 30 per cent of years can a slight yield advantage be expected from the second planting opportunity. Considering that in 33 per cent of years no second planting opportunity occurred, delaying the planting of a crop after a planting opportunity has occurred should be avoided. Based on this result we excluded the second planting opportunity from subsequent analyses. Crop performance and yield of dryland crops in northern Australia is largely a function of plant available soil moisture, which in turn is controlled by the amount and timeliness of rainfall (Meinke et al., 1993; Meinke and Hammer, 1995b). Potential yield, i.e. yield not adjusted for harvest losses, differed strongly among SO1 phases (Table I). As the highest probability of below average rainfall is expected following a negative SO1 pattern, it was not surprising that the lowest median potential yield (1.25 t ha- ) was obtained following a negative SO1 phase in AugustBeptember.

4 786 H. MEMKE, R. C. STONE AND G. L. HAMMER Table I. Simulation results by SO1 phases (n =number of years in each phase). Shown are (a) median and (b) mean values for the first simulated planting opportunity in each season. Output variables considered are: sowing date, potential yield (PYield), estimated harvest losses (Loss), harvestable yield adjusted for losses due to rain at harvest (HYield), and number of planting opportunities per season ("). Values followed by the same letter are from distributions that did not differ between SO1 categories at P < 0.05 using the Kolmorogov-Smirnov test. (a) Median SO1 Phase n Sowing date PYield Loss HYield NP (t ha- ') (per cent) (t ha-') Cons -ve Novembe? 1.25a 0' 1.13' 1.5' Cons +ve Octoberb 2.11b Ob 14Bb 2.Ob Rapid fall 6 18 Novembefb 1.48ab 0' 1.15ab 2.0ab Rapid rise NovembePb 1.44bc 040bc 2.0ab Near zero Novembeflb 1.84abc 15' 1. 18abc 2.0ab All Years November (b) Mean SO1 phase n Sowing date PYield Loss HYield NP (t ha-') (per cent) (t ha-') Cons -ve November" 1.24" 12' 1.12' 1.7a Cons +ve Novemberb 2.24b 14ab 1.93b 2.6b Rapid fall 6 19 Novembefb 1.25nb 10' 1.13ab 1.8ab Rapid rise NovembeFb 1.50k 22abc 1.09bC 24ab Near zero Novembefb 1.63'& 27' 1.14abc 2.2ab All years November Figure 1. Cumulative probability of yield differences between the second and the fust planting opportunity. Negative differences indicate a yield advantage of the first sowing opportunity, positive differences a yield advantage of the second sowing opportunity.

5 CLIMATIC RISK TO PEANUT PRODUCTION, NORTHERN AUSTRALIA 787 Conversely, as there is a high probability of above average rainfall following a positive SO1 phase pattern, median yield was highest following a positive SO1 phase (2.1 1 t ha- ). A plot of yield distributions associated with these phases against the all years distribution shows that the yield potential in cons +ve versus cons -ve season types is increased by about 1 t ha- at any probability level (Figure 2). This higher yield potential was caused by (i) more in-season rainfall, (ii) more opportune planting rains, and (iii) a longer crop duration due to earlier planting and cooler conditions resulting in a prolonged pod filling phase. The large increase in potential yield following positive SO1 phase at low probability levels (e.g. -= 0.2) is associated with particularly adequate and timely rainfall (e.g. in years 1955 and 1981, both years with a strongly positive SO1 In AugustBeptember) (Meinke and Hammer, 1995a). The median date at which the first planting opportunity occurred was 21 days earlier following a cons +ve compared with a cons - ve SO1 phase (1 1 days earlier than the all-years distribution), suggesting an earlier onset of planting rain following a positive SO1 pattern (Figure 3). The number of days from sowing to maturity was less following either a rapid fall or cons - ve SO1 phase (i.e. El Niiio type years) than it was following a cons +ve phase (data not shown). This reflects higher temperatures due to increased radiation input in the drier, less cloudy El Niiio-type years (Hammer et al., 1991), thus favouring earlier maturation. Mean yield loss due to rain at harvest was highest following a near-zero SO1 pattern (27 per cent loss). Lowest mean loss followed either a rapidly falling SO1 phase in the previous austral spring (10 per cent significant at p = 0.08) or a consistently negative SO1 (12 per cent significant atp = 0.07) (Table lb). These two SO1 phases (rapidly falling and consistently negative SOI) also had the lowest mean potential yield caused by a combination of lower than average in-season rainfall and a missed planting opportunity in each category (1941/42 and 1951/52). The two remaining years with no planting opportunity both fell into SO1 category near zero. Median loss values were zero for all SO1 categories except near zero and the all-years distribution. The large difference in loss value depending on whether median or mean values are used highlights the highly skewed nature of these distributions. The higher mean loss values are due to the occasional high rainfall event occurring during the April-June harvesting period over the past 85 years. Although no losses were predicted for more than 50 per cent of all simulated first planting opportunities their magnitude differed according to SO1 categoy (Table 11). For example, in SO1 phase rapid rise, three seasons (out of 11) were predicted to have significant losses (1926, 1949, and 1989) with losses of 50, 100 and 90 per cent, respectively, giving an average 61 per cent yield loss in years when losses occurred. Under near zero SO1 conditions 64 per cent of years were affected by losses, but their magnitude was substantially lower. We were surprised by the relatively low loss values predicted following negative or falling SO1 periods (El Niiio years), ::: W i! 0.3 g II -.- SO1 phase cons -ve SO1 phase cons +ve all years Potentlal Yield (t ha-1) Figure 2. Probability of exceeding a certain potential yield level by SO1 phases cons + ve, cons - ve and the all years case. J

6 I_ 788 H. MEINKE, R. C. STONE AND G. L. HAMMER all years - SO1 phase cons -ve SO1 phase cons +ve ct 31Qct 16-Nov 30-Nov l6-dec 3O-Dec 1Wan Sowing Date Figure 3. Probability of the fist planting opportunity of the season occumng after a certain date by SO1 phases cons + ve, cons - ve and the all years case. because harvest periods coincide, generally, with the subsequent breaks of El Niiio and high rainfall during the austral autumn. However, on closer inspection of rainfall and SO1 data, it appears the precise timing of the high rainfall break in El Niiio induced droughts varies considerably. Since 1905, 61 per cent of high rainfall events ( > 50 mm) at Kingaroy that occurred at the termination of a prolonged period of low SO1 have occurred in either March or July, i.e. those months just before or after the peanut harvesting period. Just 28 per cent of high rainfall events at the termination of an El Niiio have occurred in April-May. In some El Niiio-break years (autumns), nil harvest losses were recorded because no crop was planted. As may be expected, the highest mean number of planting opportunities occurred either following consistently positive SO1 conditions or following rapid rise SO1 conditions (Table I). In contrast, categories cons - ve and rapid fall exhibited the lowest number of planting opportunities, once again reflecting significantly different rainfall patterns under those different atmospheric conditions. However, although types cons +ve and rapid rise had very similar mean number ofplanting opportunities, they differed in their planting dates, with type rapid rise having a later mean planting date. This suggests that a type cons +ve indicates already prevailing conditions conducive for above average rainfall, whereas type rapid rise indicates that such conditions may occur in the near future. Information obtained by this type of analysis can be valuable when used for tactical decision making. A farmer s decision on whether or not to plant under marginal soil moisture conditions might well be influenced by knowing Table 11. Percentage of years affected by losses (Years), average loss in a loss year (average Loss), lowest, median, and highest loss recorded in loss years by SO1 category and total number of losses in each SO1 category. Significant differences between distributions are identical to those shown in Table I for harvest losses. SO1 phase Years (per cent) Average loss Lowest loss Median loss Highest loss Total number (per cent) (per cent) (per cent) (per cent) of losses Cons - ve Cons +ve Rapid fall Rapid rise Near zero All years

7 CLIMATIC RISK TO PEANUT PRODUCTION, NORTHERN AUSTRALIA 789 that there is or is not a reasonable chance of further planting opportunities or follow-up rain. Likewise the considerable difference in potential yield distributions (Figure 2) can be used to adjust crop management depending on expected profitability. Additionally, processors can use such information for forward planning and adjust their operations. An analysis of this type can be performed for any location where (i) long-range climate forecasting based on the SO1 has proven useful and (ii) necessary climatic input data are available. CONCLUSION This case study shows that combining simulation models with seasonal forecasting tools, such as the SO1 phase analysis, allows the derivation of probability distributions for a wide range of production variables, such as yield potential or date of first planting rain. Significant differences in such distributions can provide valuable information for producers and processors for the tactical management of their respective operations prior to commencing these operations. In the Kingaroy region of north-eastem Australia, multiple planting opportunities are rare and later plantings tend to have lower yields. Therefore it is recommended to utilize planting opportunities whenever they arise. Higher yield potential, earlier planting rain, and a higher number of planting opportunities per season are generally associated with consistently positive SO1 values in the two months just prior to the start of the sowing season. The highest losses due to rainfall at harvest tend to be associated with either rapidly rising or near zero SO1 values, whereas lowest yields usually occur following consistently negative SO1 patterns in the austral spring. REFERENCES Allan, R. J El NifieSouthern Oscillation influences in the Australasian region. Prog. Phys. Geog., 12, Carbeny, P. S., Muchow, R. C. and McCown, R. L A simulation model of kenaf for assisting fibre industry planning in northern Australia. IV Analysis of climatic risk. Aust. 1 Agric. Res., 44, Conover, W. J Practical Nonparametric Statistics. Wley, New York, 462pp. Hammer, G. L., Woodruff, D. R. and Robinson, J. B Effects of climatic variability and possible climatic change on reliability of wheat cropping-a modelling approach. Agric. Forest Meteoml., 41, Hammer G. L., McKeon, G. M., Clewett, J. E and Woodruff, D. R Usefulness of seasonal forecasts in crop and pasture management, Conference on Agricultural Meteorology, extended abstracts, National Committee on Agrometeorology (eds), Melbourne, Australia, July 1991, pp Hammer, G. L., Sinclair T. R., Boote, K. J., Wright, G. C., Meinke, H. and Bell, M. J A peanut simulation model. 1. Model development and testing. Agronomy 1, 87, McBride, J. L. and Nicholls, N Seasonal relationships between Australian rainfall and the Southern Oscillation. Man. Wea. Rev., 111, Meinke, H. and Hammer, G. L. 1995a. A peanut simulation model. 11. Assessing regional production potential. Agronomy 1, 87, Meinke, H. and Hammer, G. L. 1995b. Climatic risk to peanut production: a simulation study for northern Australia. Aust. J Exp. Agric., 35, Meinke, H. and Stone, R. C Impact of skill in climate forecasting on tactical management of dryland sunflower-a simulation study. Proceedings: 13th International Sunflower Conference, Vol. 1, 1992, Pisa, pp Meinke, H., Hammer, G. L. and Chapman, S. C A crop simulation model for sunflower. 11. Simulation analysis of production risk in a variable sub-tropical environment. Agronomy 1, 85: Meinke, H., Carbeny, P. S., McCaskill, M. R., Hills, M. and McLeod, Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models. Agric. For. Meteorol., 72, Richardson, C. W. and Wright, D. A WGEN: a Model for Generating Daily Weather Yariables, US Department of Agnculture, Agricultural Research Service, ARS-8, pp. 83. Stone, R. C. and Auliciems, A SO1 phase relationships with rainfall in eastern Australia. Int. 1 Climatol., 12, Stone, R. C., Hammer, G. L. and Woodruff, D Assessment of risk associated with climate prediction in management of wheat in northeastern Australia. Proceedings: 7th Australian Agronomy Conference, September 1993, Adelaide, Australian Society of Agronomy, pp