Modelling of wheat black point incidence based on meteorological variables in the southern Argentinean Pampas region

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1 CSIRO PUBLISHING Australian Journal of Agricultural Research, 2006, 57, Modelling of wheat black point incidence based on meteorological variables in the southern Argentinean Pampas region Ricardo C. Moschini A,D, M. N. Sisterna B, and M. A. Carmona C A Instituto de Clima y Agua, CNIA, INTA Castelar, CP: 1712, V. Udaondo, Buenos Aires, Argentina. B Facultad de Ciencias Agrarias y Forestales, UNLP, C.I.C. (Prov. Bs. As.), CC 31, CP: 1900, La Plata, Argentina. C Fitopatología, Facultad de Agronomía, Universidad de Buenos Aires, Avenida San Martín 4453, CP: 1417, Capital Federal, Argentina. D Corresponding author. rmoschini@cnia.inta.gov.ar Abstract. Studies were undertaken during 3 growing seasons at several locations on the Argentinean Pampas to investigate the relationships between environmental factors and black point incidence, and to develop predictive models. The strongest associations were observed throughout the critical period starting at 543 degree-days from heading to 861 degree-days (base temperature = 0 C). After a selection process, the best regression equation was: PI % = DPrDDTd DRH, where PI is predicted disease incidence, DPrDDTd is a product of days with precipitation and the total degree-day accumulation of mean daily temperatures greater than 17 C(DDTd), and DRH is the total days with relative humidity above 62%. The equation accounted for 87% of the total variance in the disease incidence. Using logistic regression techniques, a model including precipitation frequency and DDTd could satisfactorily explain the probability of occurrence of severe, moderate, and light epidemics. Additional keywords: Alternaria alternata, Bipolaris sorokiniana, linear regression models. Introduction The Pampas region, located in the central-eastern part of Argentina, is mostly a flat and temperate area bounded by 600 and 1000 mm western and eastern isohyets, respectively. Cropping is a frequent feature of land use, covering about 34 million hectares. The main rain-fed grain crops grown in the region are wheat, maize, soybean, and sunflower (Hall et al. 1992). Durum wheat (Triticum durum Desf.) cultivation is concentrated in the southern Buenos Aires province, which belongs to the Pampas region, and the production area is increasing. Black point is a dark discoloration of the embryo end of wheat and barley grains. The discoloration is not limited to the area around the embryo but can also extend to the ventral surface of the kernels (Conner and Davidson 1988). The disease adversely affects grain quality, impairing flour, semolina, and their products (King et al. 1981; Dexter and Matsuo 1982; Lorenz 1986). In a range of studies, symptoms have been associated with the presence of particular fungi on the grain, such as Alternaria spp., Bipolaris sorokiniana, Fusarium spp., Curvularia spp., Cladosporium sp., and Epiccocum sp. (Wiese 1987; Mathur and Cunfer 1993; Özer 2005). However, a study of the infection process in wheat (Williamson 1997) found no direct link between the presence of such fungi and the development of black point symptoms. Similarly, studies in barley have failed to establish any causal fungal association with black point (Jacobs and Rabie 1987; Ellis et al. 1996). Black point incidence (% of discoloured kernels) has increased in recent years, depending on locality (Fernández et al. 1994; Sisterna and Sarandón 2001). Symptom severity is largely dependent on seasonal conditions and is usually most serious under irrigation (Madariaga and Mellado 1988; Maloy and Spetch 1988), or when frequent rainfalls or heavy dews occur during kernel development (Kilpatrick 1968; Sheehy and Huguelet 1972; Southwell et al. 1980). The development of resistant cultivars is generally considered the most practical way to control black point, but no current variety is fully resistant (Conner and Davidson 1988; Sisterna and Sarandón 2001). Durum wheat is generally more susceptible to black point than bread wheat (Triticum aestivum L.) (Machacek and Greaney 1938; Greaney and Wallace 1943). In previous studies, the observed variability in disease incidence levels between locations and years was explained by environmental condition variance rather than crop genetic factors (Fernández et al. 1994). Due to the assumption that the disease expression has a strong relationship with the environmental factors, the aim CSIRO /AR /06/111151

2 1152 Australian Journal of Agricultural Research R. C. Moschini et al. of this study was to identify the meteorological variables most closely related to black point incidence and to derive prediction models, in southern locations of Buenos Aires province (Argentinean Pampas region). Materials and methods Disease and meteorological data From 1995 to 1997, black point incidence was recorded on trial plots sown with several durum wheat varieties at 5 locations in southern Buenos Aires Province (Barrow, Balcarce, and Bordenave: INTA experiment stations; La Dulce: private crop breeding station; Miramar: Buenos Aires province experiment station). Nine varieties were used in 1995 and 1997 and 16 in Disease observations were not available in Balcarce for the 1995 growing season. A random sample of 200 seeds of each variety from every trial was evaluated for black point incidence (percentage of discoloured seeds). Seed health was tested using the blotter test (Neergaard 1974) to identify the pathogens associated with the disorder. Although the same group of commercial durum wheat varieties was sown in the 5 locations in a particular year, they varied between years. The annually planted varieties were categorised into 2 groups, based on their annual mean incidence values evaluated by the Waller-Duncan test (locations were the replicates). The worst affected group (A) included the varieties whose symptom expression did not differ significantly from the variety with the highest mean incidence. The other least affected group (B) included the varieties with no significant differences with respect to the variety with the lowest mean disease incidence. Within each group, the available disease incidence and heading date observations of the included varieties were averaged by location and year, resulting in 28 mean observed incidence values, 14 per group (Table 1). Differences in heading date and black point incidence between both groups (A and B) were calculated by location and year (n = 14). Paired comparison t-test showed that both average differences were significantly different from zero (Table 1). Heading was recorded for each plot when 50% of the heads were fully emerged, equivalent to GS 59 (Zadoks et al. 1974). Readings of daily maximum (Tx; C) and minimum (Tn; C) temperatures (recorded by liquid-in-glass thermometers), relative humidity (RH, %; recorded by a psychrometer), and precipitation (Pr; mm; recorded by metal rain gauge) were collected from standard weather stations located at the 5 experiment stations where the trial plots were sown. Temperature and humidity instruments were placed in a wooden weather shelter. Daily average temperature (Td) was calculated as half the sum of the daily minimum and maximum temperatures. Based on these meteorological data, weather variables were calculated over periods with different starting times and lengths of time after heading. These variables were: total days with precipitation (DPr), cumulative precipitation (TPr), and total days with daily relative humidity (average of the 0800, 1400, and 2000 hour observations) above different threshold values that ranged from 60 to 85% (DRH). From daily maximum and minimum temperature observations, their corresponding mean values were calculated (MTx, MTn). Adding the exceeding amounts of daily maximum temperature from several threshold values (ranged from 28 to 32 C) through the entire critical period of the infection, a degree-day variable named DDTx was calculated. Also when daily minimum temperatures were lower than different thresholds ranging from9to13 C, the accumulated differences between the thresholds and minimum temperature values established the variable named DDTn. The cumulative effect of daily average temperature on pathogen infection was evaluated by calculating the accumulation of positive degree-days above different base mean temperatures (base Td) that ranged from 7to17 C(DDTd). A combined variable (DPrDDTd) was created by multiplying the days with precipitation (DPr) byddtd. Statistical analysis To assess the association between environmental and disease data, a computer program was developed using the SAS language (Statistical Analysis System 1988). Throughout the period extending from heading date to ripening, several time periods, with starting days moved in Table 1. Mean observed incidence of black point in durum wheat at 5 locations in the southern Pampas region The number of varieties assessed and mean heading date are also detailed per group of varieties (A, worst affected; B, least affected) Year Location Group A Group B Variety Heading date Incidence Variety Heading date Incidence (N) (Julian day) (%) (N) (Julian day) (%) 1995 Barrow Bordenave La Dulce Miramar Balcarce Barrow Bordenave La Dulce Miramar Balcarce Barrow Bordenave La Dulce Miramar t-test value A P > t A Paired comparisons t-test: differences in heading date and black point incidence between both groups (A and B) were calculated by location and year. The average of these differences for heading date was significantly different from zero (t = 9.649; P = ). A similar result was obtained for black point incidence (t = 5.417; P = ).

3 Modelling wheat black point incidence Australian Journal of Agricultural Research 1153 irregular steps and lengths expressed as accumulated degree-days (base Td = 0 C), were analysed. The Rsquare routine from SAS was chosen to identify the time period in which the associations between black point incidence and meteorological variables were strongest, taking into consideration the coefficients of determination (R 2 ). This period was regarded as the critical period length (CPL). The model selection was based on both statistical procedures of SAS such as Rsquare and Stepwise, and biological criteria. Stepwise procedure calculates, for each of the independent variables, F statistics that reflect the contribution of the variable to the model if it is included. Options allow criteria for entry into the model (SLE) and for staying in the model (SLS) to be specified. After examining the biological sense and stability of regression coefficient signs, many models were excluded (Berenson et al. 1983; Coakley et al. 1985; Moschini and Fortugno 1996; Carmona et al. 1997). To classify the annual black point incidence values into 3 epidemic categories, disease values with 50% (median) and 80% of probability were identified (4.725 and 11.4%, respectively) from the available disease incidence observations. Based on these thresholds, the epidemic categories (identified by a code number that ranged from 1 to 3) were defined as: nil to light (incidence 4.725%; code 1), moderate (incidence >4.725 and 11.4%; code 2), and severe (incidence >11.4%; code 3). The SAS Logistic procedure fits a parallel lines regression model for ordinary response data by the method of maximum likelihood, based on the cumulative distribution probabilities of the response levels (calculated in descending order). A logit function (natural logarithm of (Pr/1 Pr), where Pr is the cumulative probability of the epidemic categories) provides the link between the stochastic component and the explanatory variables (meteorological variables described above). The assumptions required by parametric linear regression analyses are not necessary for logistic regression to be valid. The stepwise logistic regression procedure was used for selecting the most appropriate model. To obtain an epidemic category forecast from a probability estimation, a decision rule must be applied. It was to forecast the epidemic category with the maximum associated probability of occurrence. Validation Model performances (best parametric lineal model and the fitted logistic equations) were evaluated with an independent validation set, consisting of 5 (one for each location) paired (for June and July sowing dates) mean disease observed values in the 1998 crop season. Each mean value resulted from averaging disease observations recorded on plots sown with 6 commercial durum wheat varieties (2 of them were used in 1995 and 4 in 1996 and 1997 crop seasons) by sowing date and location. The correspondence between observed and predicted values was evaluated by the mean difference t-test and the analysis of the deviations (Moschini et al. 2001). Results The blotter test revealed that black point was associated primarily with Alternaria alternata, which was found at various percentages in most of the samples analysed. The average of the 3 years and locations was 10.9%. B. sorokiniana was the second most common fungus isolated from the kernels and although this microorganism was observed at lower levels (1.58%), it caused a greater effect on seed germination than A. alternata. This effect on emergence and on the yield of the subsequent crop was also reported by other authors (Machacek and Greaney 1938; Hanson and Christensen 1953). Other organisms found were: B. spicifera, Curvularia lunata, Drechslera siccans, Fusarium graminearum, F. oxysporum, F. equiseti, F. verticillioides, and F. poae, but at levels lower than 1.5%. Black point incidence data (161 observations) were modelled separately as simple linear functions of both A. alternata and B. sorokiniana seed contamination (%). A significant (F = 18.55; P > F = ) but weak relationship (R 2 = 0.106) was found when the black point incidence was regressed against A. alternata presence. The overall F statistic for the model including B. sorokiniana seed content was not significant. Nevertheless, when the modelling included 2 classification variables (year and location) and the 2 continuous covariates (A. alternata and B. sorokiniana seed contamination) analysed separately, the inclusion of neither putative pathogen was significantly justified. These latter results were similar to those reported by Conner and Davidson (1988) with regard to the weak relationship existing between black point incidence and the percentage of kernels infected by these fungi. Closer associations between black point incidence and meteorological variables were identified using a CPL starting after accumulating 543 degree-days from heading, and finishing when the temperature sum reached 861 degree-days (base Td = 0 C). The CPL varied from 14 to 20 days, with the last day between 5 and 31 December (ripening stage), for the 28 observations analysed. The susceptible time interval approximately covered the milk and mealy-dough wheat stages (Zadoks scale: 71 87) until ripening (hard kernel). By comparing the coefficients of determination (R 2 ), the aptness of regression models could be analysed up to a maximum of 3 independent meteorological variables, monitored throughout the CPL. Table 2 shows the intercept and slope values of the main regression equations, with R 2 coefficients. The combination of the moisture and thermal effect expressed by the variable DPrDDTd (P < 0.05) (base Td = 17 C) (Table 2) resulted in the strongest explanation Table 2. Simple and multiple black point incidence prediction (PI%) equations based on meteorological variables The equations were fitted using disease-meteorological data from Balcarce, Barrow, Miramar, La Dulce, and Bordenave ( growing seasons). DDTn, if daily minimum temperatures <11 C, the differences between 11 C and minimum temperature values are accumulated; TP, total accumulated precipitation in millimetres; DPr, days with precipitation; DRH, days with relative humidity >62%; DPrDDTd, multiplies DPr by DDTd, which is the total accumulation of positive degree-days above 17 C (base Td); CPL, critical period length, from heading date degree-days to accumulated 861 degree-days Equation Determination coefficient [A] PI% = DPrDDTd [B] PI% = TP [C] PI% = DPr [D] PI% = DDTn [E] PI% = DPrDDTd DRH

4 1154 Australian Journal of Agricultural Research R. C. Moschini et al. of the disease variation, accounting for 84% of the total variance in black point incidence (Eqn A, Table 2) in a positive linear relationship. Regarding the simple moisture variables, the best positive correlations with disease incidence were obtained with the total amount (TP) and frequency of precipitation (DPr), which attained coefficients of determination of and 0.475, respectively. Among the 2 variable models, the Eqn E (Table 2) showed the highest coefficient of determination (R 2 = 0.87). This model combines the DPrDDTd variable with the occurrence of days with air relative humidity above 62% (DRH). The Stepwise procedure, with significant levels of 0.05 for both SLE and SLS, defined Eqn E as the most appropriate. The stepwise logistic regression routine, with significance levels of 0.05 for both SLE and SLS, defined the following models (Eqns 1 and 2) as the most appropriate for estimating the cumulative probabilities of having a severe epidemic (Pr3) and a moderate to severe epidemic (Pr23): Pr3 = exp( DPr DDTd)/ (1 + exp[ DPr DDTd]) (1) Pr23 = exp( DPr DDTd)/ (1 + exp[ DPr DDTd]) (2) The probability of a moderate epidemic (Pr2) was obtained by the difference between Pr23 and Pr3, whereas the probability of having a nil to light epidemic resulted from subtracting Pr23 from 1. When observed epidemic categories (n = 28) were compared with those predicted by the logistic equations with the maximum probability of occurrence, 22 out of 28 cases were correctly classified. Validation Deviations between mean observed black point incidence values and those predicted by Eqn E (Table 2), in the 10 location sowing date points (1998 wheat growing season), showed equilibrium in their signs (Table 3). The t-test determined no significant differences between mean observed and predicted values (Eqn E, Table 2). In 8 out of 10 cases, the observed epidemic category (nil to light, moderate, or severe) agreed with the one predicted by logistic equations with the maximum probability of occurrence (Table 3). Table 3. Deviations between mean observed black point incidence values (%, calculated from observations of 6 wheat varieties) and those predicted by Eqn E (Table 2), in 10 location sowing dates points (1998 wheat growing season) The epidemic categories (nil to light, moderate, or severe) observed at the 10 location sowing dates were contrasted with those predicted by the logistic equations with the maximum probability of occurrence (yes: coincidence). Eqn E: PI% = DPrDDTd DRH, where PI% is the black point predicted incidence; DPrDDTd multiplies the days with precipitation (DPr) byddtd, total accumulation of positive degree-days above 17 C (base Td); DRH, days with relative humidity >62%; CPL, critical period length, from heading date degree-days to accumulated 861 degree-days Location Sowing Observed Predicted Deviation Logistic date incidence incidence (observed predicted) equations A Balcarce June Yes July Yes Barrow June Yes July No Bordenavve June Yes July Yes La Dulce June Yes July Yes Miramar June Yes July No Mean t-test value B 0.47 P > t 0.64 A Logistic equations: cumulative probabilities of having a severe epidemic (Pr3) and a moderate to severe epidemic (Pr23): Pr3 = exp( DPr DDTd)/(1 + exp[ DPr DDTd]), Pr23 = exp( DPr DDTd)/(1 + exp[ DPr DDTd]). The probability of a moderate epidemic (Pr2) was obtained by the difference between Pr23 and Pr3, whereas the probability of having a nil to light epidemic resulted from subtracting Pr23 from 1. DPr, Days with precipitation; DDTd, the total accumulation of positive degree-days above 17 C (base Td). B Mean difference t-test: degrees of freedom = 18. Mean observed black point incidence (2.25%) was contrasted with 2.73% (mean predicted incidence by Eqn E, Table 2).

5 Modelling wheat black point incidence Australian Journal of Agricultural Research 1155 Discussion Strong links between meteorological variables and wheat black point were found during the period that approximated the milk to dough kernel development stage (Zadoks scale: 71 87). The beginning of the critical period occurred around 30 days after heading and extended for days (depending on location and year). The growth stages in which the meteorological factors most influenced the incidence of wheat black point are in accordance with the findings of other workers. Southwell et al. (1980) showed that the incidence of black point increases with the stage of grain development at the time of inoculation, in experiments conducted in a controlled environment. Accordingly, Conner (1989) demonstrated that disease incidence significantly increased with heavier irrigation and precipitation during early stages of kernel formation (milk and mealy-dough stages). In addition, the same author concluded that during boot or anthesis stages, the amount of moisture has no influence on black point incidence. Some literature, contradictory to our results, pointed out the importance of weather conditions in earlier wheat growth stages for black point occurrence. Machacek and Greaney (1938) suggested that the infection of grains by A. alternata can only occur at anthesis and at grain development stages when the glumes are in an expanded position and allow entry of airborne spores. Languasco et al. (1993) found the strongest associations between A. alternata infection and meteorological factors (especially rainfall) evaluated during the first 10 days after heading. This study found that warm weather during the CPL, expressed by the positive slopes of variables such as DDTd (base Td: 17 C) and MTn, favoured the incidence of black point. The variable DDTn established a high negative association between disease incidence and cool weather, here expressed by the sum of residuals of minimum temperatures bellow 11 C. A positive relationship was found between disease incidence and simple moisture variables such as the total amount of precipitation and the precipitation frequency. On the assumption that there is a direct link between the presence of fungi such as A. alternata and black point, the main role of precipitation variables (amount and/or frequency) is as source of wetting, needed for meeting the long wet period required for infection. Southwell et al. (1980) reported that black point incidence in durum wheat increased with length of wet period up to a maximum of 48 h (temp. 25 C). Several authors agree that high temperature and moist conditions during grain filling favour black point incidence (Sheehy and Huguelet 1972; Rees et al. 1984), which supports the biological meaning of the disease prediction equation (E, Table 2) including DPrDDTd and DRH as independent meteorological variables. Besides explaining 87% of the variation in disease incidence, the disease incidence estimations were satisfactorily compared with the observed disease values available for the 5 locations (2 sowing dates) in 1998 (Table 3). Also the developed logistic regression equations showed an accuracy of 78.5% for predicting epidemic categories (22 out of 28 location years were correctly classified). Running the predicted models in the year reserved for the validation, it is worth pointing out the sensitivity of the fitted model predictions to small changes in wheat heading date. Our predictive system simulates, by simple heat accumulation, the next wheat growing stages from heading date by determining the start of the wheat critical period when 543 degreedays were accumulated. Other factors, such as wheat variety and soil water status, were not considered when defining the crop critical period. Very small changes in the length and position of this period produced remarkable changes in disease incidence predictions. The empirical models developed in this study included real-world factors that accounted for the influence of environment on disease, and these variables served as the primary predictors for the CPL. Although the disease response could be differentiated by variety heading date, the models did not include any variable that accounted for genetic resistance within wheat varieties. This study helps to clarify and quantify the environmental effects on black point disease, showing the effect of a warm, rainy period during the grain filling stage in durum wheat. These results can be useful to assist producers in rational, tactical, and strategic disease management. Chemical control of black point is difficult to analyse because of the concurrence of many interactive factors. Ellis et al. (1996) observed that especially early fungicide applications leading to large increases in 1000-grain weight are at risk of increasing the intensity of black point. The authors concluded that reliable chemical control would be more likely if fungicides were sprayed on the wheat heads after anthesis. Available disease forecasting systems would be needed for predicting high levels of black point in order to economically justify such late fungicide applications. Our model may play an important role in this regard. Acknowledgments The authors thank Dr Mike J. 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