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Article

Assessing PREDICTA®B as a Potential Disease Risk Management Decision Tool for Sorghum Charcoal Rot Based on Disease Levels, Lodging, and Associated Yield Loss

by
Dante L. Adorada
Centre for Crop Health, Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Agronomy 2023, 13(10), 2494; https://doi.org/10.3390/agronomy13102494
Submission received: 14 July 2023 / Revised: 31 August 2023 / Accepted: 25 September 2023 / Published: 27 September 2023
(This article belongs to the Section Pest and Disease Management)
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Abstract

:
Charcoal rot, caused by Macrophomina phaseolina (Mp), is the most common and the most important root and stalk rot disease of sorghum. Survey data for sorghum charcoal rot disease and pathogen loads were analysed to assess the potential of PREDICTA®B as a disease risk management decision tool. The pathogen, at sowing and pre-harvest, was found to be strongly correlated. The results showed a strong quadratic relationship between the incidence of charcoal rot and the percent of lodging, supporting the notion that charcoal rot is one of the causative agents in sorghum lodging. An Mp load of 2.5 k copies per gram of soil on a log10 scale was established as an indicator of the high incidence of charcoal rot, and its associated risk of serious lodging can be used as a management decision tool. Rainfall and location were also shown to influence lodging rates. Some varieties of sorghum were found to be less susceptible to charcoal rot than others. This study found that disease incidence was a better predictor of damage than the disease rating index. As set by PREDICTA®B, at low or non-detectable levels of Mp, the risk of disease is low. With high levels of Mp, the incidence and severity of disease depend on the susceptibility of the variety and the conduciveness of the environment. High levels of Mp do not mean that disease will occur, but that there is a high risk if the conditions are favourable for disease development. While this study has provided much useful information, it has highlighted the need for further data collection to be able to develop reliable disease risk categories based on test results and its associated yield loss.

1. Introduction

Grain sorghum (Sorghum bicolor (L.) Moench) is the main summer grain crop in the northern grains region and is a significant part of the dryland cropping system of north-eastern Australia, which supplies feed grains to the animal industries [1]. About 60% of the Australian sorghum crop is grown in Queensland, and the rest is grown in northern NSW. It is primarily a summer crop, with an extended season at higher latitudes including Central Queensland and farther north, and can be a good rotation crop, tolerating moisture and heat stress, as well as performing better than maize on soils with minimal potassium (K) levels [1].
The most common and the most important root and stalk rot pathogen of sorghum is Macrophomina phaseolina (Mp), a soilborne fungus causing charcoal rot which, in most cases, can lead to lodging. It can infect via the roots of sorghum plants at almost any stage of plant growth but develops more rapidly after post-flowering stress [2]. It is also a late-season disease that causes yield loss through poor grain fill, but more commonly through plant lodging, which impedes harvest and reduces grain quality. Unfavourable environmental conditions are key stressors known to promote disease development. The fungus is extensively distributed throughout Australia, infecting the root and stems of over 500 weed and crop species including winter cereals [3]. Its microsclerotia can survive in the soil and on stubble for more than four years, and it is unknown what soil conditions are necessary to reduce the survival of microsclerotia in Australia, but overseas studies show that wet soil can significantly impede their survival [4].
Numerous reports on the destruction of sorghum crops by charcoal rot are available; however, sound and reliable quantitative data on yield losses are not provided [5]. A 35% reduction in the 1000 grain weight based on the combined effects of drought and charcoal rot that caused plants to lodge has been reported, with the loss in grain yield being due to a reduction in the size of the grain. Anahosur and Patil [6] reported losses in the weight of sorghum seeds ranging from 15.18 to 54.59% in different genotypes depending on the lodging levels (21.99–78.37%), therefore concluding that there was a direct correlation between lodging and a loss in seed weight. Overseas yield losses due to charcoal rot have been estimated at more than 50% [6]. Despite the lack of any formal quantification in Australia, significant yield losses have been associated with lodging, where prevailing hot and dry conditions have resulted in the widespread high incidence of charcoal rot and subsequent lodging [4]. Lodged plants do not produce full heads of grain or fully formed heads and are difficult to harvest [7]. The realised losses associated with lodging were varied and dependent on the capability of individual growers to recoup lodged heads with the harvesting equipment available. Yield loss quantification through in-field assessments is challenging, and varies depending on several factors including the weather, soil, time of infection, cultivar susceptibility, and the degree of lodging.
The early detection and quantification of stalk rot pathogens in sorghum paddocks is paramount to the development and implementation of disease management strategies. PREDICTA®B, a quantitative polymerase chain reaction (qPCR) DNA test [3,8], developed by the Primary Industries and Regions, South Australia (PIRSA), through the South Australian Research Development Institute (SARDI), helps grain and pulse producers to identify which soilborne pathogens pose a significant risk to their crops before seeding, so that steps can be taken to minimize the threat of yield loss [9]. PREDICTA® B has been found to have the potential to provide an accurate and dependable system to estimate charcoal rot severity in sorghum, and it could be a useful tool to assess inoculum levels before sowing sorghum [10]. PREDICTA®B can also help pathologists to understand the relations between the pathogens within a paddock and to advise growers on management options to limit the impact of these disease complexes.
Macrophomina phaseolina is often detected using PREDICTA®B in Australia’s northern region. These results are reported with categories based on population density so that growers and advisers can benchmark the levels of pathogen DNA detected in paddocks against the rest of the industry [11]. Likewise, when the relationship between the initial pathogen level and disease has been defined, the level detected in the sample is reported with a disease risk rating. However, test results for the charcoal rot pathogen are still reported only as relative population densities rather than as a disease risk, since the level of yield loss associated with the pathogen DNA level is yet to be determined. Gray et al. [12] have shown that the incidence of charcoal rot in sorghum was positively correlated with soil populations of Mp. Still, the more recent investigations on this test, with only two seasons of data, were not sufficient to determine any correlation between the population density and disease severity, lodging, or effects on yield [13]; therefore, further work is required in this area.
This study tried to determine if Mp inoculum loadings found in soil and stubble based on PREDICTA®B can be correlated with end-of-season disease levels, lodging, and associated yield loss. Accordingly, this can be developed into disease risk categories, to provide growers with a risk assessment for the likelihood of potential disease before planting, and to help inform crop and variety decisions and guide management to minimize losses.

2. Materials and Methods

2.1. Sampling Sites and Locations

For the 2019/20 summer season, available commercial sorghum crops across the northern cropping region, which included Central Queensland (CQ), Southern Queensland (SQ) and Northern New South Wales (NNSW), were sampled for a charcoal rot PREDICTA®B test. Sample collection sites were identified in areas representative of sorghum-growing regions. In consultation with growers and agronomists, paddocks were selected to include a range of pathogen levels based on the earlier history of sorghum lodging, as well as sites with no earlier history of lodging. Due to the extreme drought that hit the northern cropping region in 2019 [14,15], very few or no sorghum crop was planted in SQ and NNSW. Add to that the travel restrictions that were enforced at the peak of the COVID-19 pandemic in 2020 [16,17]. It was only CQ that had a reprieve from the drought and received ample moisture from rainfall in January 2020, which allowed several growers to plant their sorghum crop. Growers of very few paddocks in SQ that received rain in January took advantage of putting sorghum in the ground as a late-planted crop, despite the risk of sorghum not reaching maturity before frost events.
At sowing or the early sorghum growth stage, soil sampling was performed in 30 CQ and 4 SQ sorghum paddocks [18] (Figure 1, Table A1).

2.2. Soil Sampling Procedure

Soil sampling was performed following a modified sampling procedure for the northern regions [19,20]. From fifteen locations within a one-hectare area per paddock (referred to from hereon as “Site”), two 5 cm soil cores per location were taken, ensuring that the total sample weight did not exceed 500 g. A stubble piece per location, or a total of 15 stubble pieces taken from the previous crop row, were added to the soil core samples to form the “Site” soil samples. The soil samples were sent to the SARDI for PREDICTA®B testing on the Mp population densities. To ensure that re-sampling at pre-harvest was performed in the same locations, a marker was placed to identify each site. Data on the location (latitude, longitude, and altitude), the crop history for at least the immediate prior crop, stubble management (e.g., no till, minimum till, cultivated), the sorghum cultivar planted, earlier disease occurrences, the irrigation regime (irrigated or dryland), and seasonal weather conditions (rainfall, temperature) were also recorded.
At the end of the growing season (pre-harvest), all sites were re-visited when the sorghum was mature. Soil sampling, as carried out previously, was performed to see if there would be a change in the densities of the charcoal rot pathogen. Sampling was conducted in the same locations. Disease, lodging, and yield assessments based on ten plants along the row from each location for the 15 locations per site (or paddock) were performed. Lesion length was measured from the base of the stem, and the infected internode between the lodged and standing plants were recorded to see if the amount of infection was a significant cause of lodging. The heads of the standing sorghum crops were harvested at each sampling location, but not the infected lodged plants, as they were already considered as yield loss. The sorghum heads were sun-dried and threshed, and the grain weight adjusted to a 12% moisture content was determined. Correlation analyses between the PREDICTA®B results, the disease incidence and severity, the percent of lodging, and the grain yield against the Mp population densities were performed, for use in developing disease risk levels.

2.3. Data Analysis

Statistical analyses were performed by Rho Environmentrics Pty Ltd. (Highgate, Australia) using R software Ver. 4.0.2 [21]. Regression and correlation analyses were performed to determine the relationships between the different factors under investigation.

3. Results

There was a total of 30 sites in CQ and 4 sites in SQ that were sampled for soil at the sowing or early growth stage of the crop. At the end of the growing season, only 26 sites in CQ and 3 sites in SQ still had standing crops, as several growers decided to harvest their crops earlier than expected due to the extreme drought in their respective areas. It was also during this period that travel restrictions were enforced due to the COVID-19 epidemic, when traveling earlier than the second week of May 2020 was not allowed. Pre-harvest sampling and disease assessments were able to be completed despite the travel restrictions.

3.1. Disease Incidence–Lodging Analysis

There is a strong quadratic relationship between the incidence of charcoal rot and lodging (Figure 2), and the strength of the relationship is summarized in Table 1. The amount of lodging was extremely low for low levels of incidence of charcoal rot, and the predicted amount of lodging is only 1.4% when the incidence is 20%.

3.2. Comparison of Mp Loads at Each Sampling Time

The correlation between the sowing and pre-harvest log pathogen levels was 0.771. The difference between the two samplings would be in part due to the sampling variations at both times, and in part due to the real changes that occurred between those times. The moderate correlation implies that the sampling had at least a reasonable degree of precision. There was a positive association between the two sampling times; that is, as the density of Mp at sowing increases, so does the Mp density at pre-harvest (Figure 3). This is useful because the Mp density at sowing can be used to predict the density at pre-harvest, and vice versa.
Further details of the changes in the number of copies of Mp for the different sites are shown in Table 2 and Figure 4. Moreover, there were significant differences (p < 0.05) evident between the sites (nearest town). In Brookstead, both sites showed an average increase in the Mp population density of 5.36 times. The change was mixed at Arcturus, where six sites showed an increase and four a decrease, but on average there was an increase by a factor of 1.43. At four sites, there was a decrease—the largest decrease was at Chimside, where the pre-harvest amount was 0.23 of the amounts presented at sowing. These inconsistencies could have been due to environmental factors affecting the pathogen dynamics, i.e., more robust plants during the season resulted in more disease.

3.3. Relationship between Mp in Soil and Lodging

Overall, there was a positive (but not statistically significant) relationship between the amount of Mp present and the amount of lodging based on simple linear regression (Table 3).

3.4. Between Region Analysis

There were marked differences observed between regions (as defined by the nearest town) in the percent of lodging (Table 4). Arcturus had both the largest number of sampled paddocks and the highest percent of lodging. The effect of the regions is shown in Figure 5.

3.5. Within-Region Analyses

Averaged across the regions, there was no significant effect of the pathogen level on the percent of lodging (Table 5). Most regions had only a few paddocks sampled, and three of those had no lodging, i.e., Chimside, Clermont, and Crinum (Table 4). There were ten paddocks with a range of lodging values at Arcturus. The relationships between the pre-planting Mp levels and the percent of lodging were positive but not statistically significant (Table 6). However, they were based only on limited data.

3.6. Effect of Mp on Disease Incidence

In general, sites with high Mp levels in the soil had a high incidence of charcoal rot (Figure 6). There were a few exceptions, as there was no incidence of charcoal rot in Crinum despite high Mp levels, as well as low incidence in Chimside. The relationship between the amount of Mp in the soil and the incidence of disease in the standing and lodged plants are shown in Figure 7. The incidence of charcoal rot was reduced to a binomial variable that was assessed using a general linear model with binomial errors. The amount of Mp present was related to whether there was an infection in the upright plants (p < 0.05). All the lodged plants had charcoal rot lesions.

3.7. Effect of Mp on Disease Severity

The disease severity was recorded as the length of the lesions on either the lodged or the standing plants. No lesion length data on the lodged plants were available when no lodging occurred. There was no relationship between the amount of Mp present in the soil and the length of the lesions (Figure 8). A comparison was made between the lesion length in the lodged and erect plants, where it was expected that the lesion length of Mp would be greater in lodged than in erect plants (Table 7). The lesion length was longer in the lodged plants, but the difference was not statistically significant. An alternative approach was to plot the lesion length of the lodged plants against those of the erect plants (Figure 9); however, no clear effect was apparent.

3.8. Effect of Mp on Yield

Preliminary yield data were recorded as kg per 150 plants. These data were re-scaled using the approximation of 45,000 plants per ha to give yield in kg/ha. There was no statistically significant relationship between the amount of Mp present and the measured yield (Table 8, Figure 10). However, due to limited data used in the analyses, these need to be confirmed with further data. There was no statistically significant relationship between the amount of Mp present and the amount of potential crop yield. (Table 9 and Figure 11).

3.9. Effect of Variety on the Incidence of Charcoal Rot

Some varieties showed less effect on lodging under conditions of high Mp load. MR Buster and MR Taurus had low incidences of charcoal rot (Figure 12) and lodging (Figure 13), suggesting varietal effects on the incidence of charcoal rot.

3.10. Effect of Rainfall on the Incidence of Charcoal Rot

A strong relationship was observed between rainfall and the incidence of charcoal rot (Figure 14). This is based on the average rainfall and temperature (30 days) from January to June 2020 in the SQ and CQ sorghum paddocks sampled. The effect of rainfall is partially confounded with the variety—for example, Sentinel was only sown in low-rainfall sites. The single sample with Taurus was, again, an outlier in having no incidence despite being in a high-rainfall site. There are numerous variables affecting the disease. A useful measure is the amount of lodging—for display, the measure was grouped into three classes (<10%, 10–30%, and >30%.). The amount of lodging, together with varieties, is shown in Figure 15. The sites with both high rainfall and high Mp levels would be considered high-risk. Some varieties (MR Bazley, Pioneer A66, and Taurus) were in that high-risk part of the plot but had <1% lodging.

3.11. Disease Index to Quantify Disease

The disease index of Das et al. [22] is defined as CRI2 = CRP × 0.4 + MLS × 0.6, where CRP is the incidence and MLS is the length of lesions in mm. The formula was not applied to the current data, as the incidence and lesion length were recorded on both lodged and erect plants when both plant types were present. Therefore, a weighted average of the lesion length was used, CRP.average = [CRP.lodged × % lodging + CRP.erect × (100 − % lodging)]/100, where CRP.lodged and CRP.erect are the lesion lengths in the lodged and standing sites. In the case of no lodging, the formula is simply the mean of the incidence in the erect plants. In the case of 50% lodging, the incidence is the average of the lodged and erect plants. The use of the disease index is presented in Table 1. Other combinations of incidence and lesion length were tried, but the best prediction was obtained using just the incidence.

4. Discussion

It is acknowledged that drought stress alone can cause lodging without aid from pathogens where an inoculum is absent [23]. However, where pathogens are present, drought-stressed plants are invariably invaded by them, and this leads to increased damage to plants. Low or intermediate levels of drought stress may be tolerated by the plant except when combined with the pathogen. While drought alone must have contributed to some yield reduction, the compounded effects of charcoal rot and drought causing plants to lodge must have elevated the degree of yield loss [5].
For this study, the basic hypothesis is that Mp causes symptoms in the plants and that those symptoms result in crop loss. It has been shown that the logarithm of the amount of disease present is a useful indicator of the potential harm caused. In some cases, there were zero diseases recorded, and in those cases, it is not possible to take a logarithm of the data. That problem is typically avoided by using the transformation y = log10(x + k), where k is taken as 1.0 and x is the untransformed value. The yield potential was estimated as Measured yield × 100/(100 − % lodging).
The analyses presented here show that the cause of the lodging of sorghum depends on numerous factors including rainfall, region, and variety, as well as the number of pathogens in the soil. No doubt there are other factors, including soil properties, cultural practices, and other meteorological factors that were not assessed in this report. The data analysed included the amount of Mp in the soil. No direct method of assessing the sampling variation was available for those data, but it was inferred from the correlation between the sampling times that at least half of the information from one sampling was contained in the second sampling. That variation would have included the sampling variation from both times and the variation in the actual changes between the two times. From that, it was concluded that the sampling method used was satisfactory.
The current study only analysed site variation, so no data on the amount of variation within a paddock were provided. Such a study would be of aid in designing future sampling schemes. Some exact duplicates would be useful in assessing field sampling variation.
The results showed that there is a strong relationship between the incidence of charcoal rot and lodging. It was also noted that there is a quadratic relationship between rainfall and lodging, but with an adjusted R2 of 0.326 compared to the value of 0.899 based on the incidence of charcoal rot. This implies that charcoal rot is a causative factor in lodging. Furthermore, the fitted relationship showed that there is minimal lodging when the incidence of charcoal rot is less than 20%. There seems to be a relationship between the pathogen inoculum density in the soil and disease intensity, and between disease intensity and yield loss [6,24,25]. It has been proven previously that there is a definite relation between the sink source and the charcoal rot severity, where plants become susceptible to diseases at the post-flowering to grain filling stage, when the food reserves are translocated from the stem to the ears and the food supply to the roots is reduced [25]. Thus, some agricultural practices have intended to reduce the inoculum density, such as the use of bio solarization [26], a high soil moisture above 60% [27,28], and rotation with non-host crops [29,30]. Moreover, due to Mp having heterokaryotic mycelium, there are varied asexual sub-phases, along with phenotypic, geographical, and genetic variations, making it a challenging pathogen for designing effective and long-lasting disease management [31]. Therefore, charcoal rot is frequently controlled by applying cultural measures that reduce plant stress. Utilizing resistant cultivars and adhering to crop- and time-specific cultural practices that preserve soil moisture are two ways to manage charcoal rot.
As set in the PREDICTA®B disease risk thresholds, a low or non-detectable level of inoculum means that the risk of disease is low, while at high levels of inoculum, the occurrence and severity of disease depend on the susceptibility of the variety and the conduciveness of the environment [20]. There is evidence from this study that at an Mp load of ≥2.5 k copies per gram of soil on a log10 scale could result in a high incidence of charcoal rot, and its associated risk of serious lodging can be used as a management decision tool. However, that cut-off is set using only the results from twenty-nine sites, and further data across other sites and seasons are needed to refine that figure.
There is no evidence of crop loss associated with the amount of Mp present. The relationship between disease and crop loss may be seasonally dependent. There may be some compensation for healthy plants using the available water more rapidly and thus restricting later plant growth. Further information concerning the effect at different growth stages may be obtained in future studies by including data on the 1000 grain weight, panicle size, and the number of plants (panicles) per square meter.
There is a strong suggestion of differences in the susceptibility of varieties to charcoal rot. For example, as shown in Figure 13, the single sample of Taurus was very resistant. The varietal effects may be particularly important and further data should be sought to establish the relative vulnerability to disease. Conversely, varietal effects may be masking other effects in this study and these should be considered in future studies.
The two sampling times provided some interesting information, especially on the sampling precision. The similarity between the two data sets suggests that sampling before sowing would also give useful information that could be used to aid in management decision making. The current data do not give useful information on the persistence of Mp in soil, or when soil sampling can be undertaken to aid with future management decisions. It may be possible to use DNA samples, taken months before sowing, to aid with management practices.
The disease index of Das et al. [30] combines the incidence (which is a ratio) with the amount of disease present (measured as the length of lesions in mm). The mixture of units could be removed by considering the fraction of a given length of stem that was affected by lesions. The current study found that disease incidence is a better predictor of damage than the disease index of [30], and that it avoids the problem of a mix of units. If future studies confirm that incidence alone is an effective predictor, it could make disease assessment simpler.
To our knowledge, this is the first paper on assessing PREDICTA®B as a potential disease risk management decision tool for sorghum charcoal rot. From the obtained results, it should be noted that high levels of inoculum do not mean that disease will occur, but that there is a high risk if the conditions are favourable for disease development. If available, disease risk categories should be used as a general guide only. Other factors such as the climate, management practices, soil type, crop type, variety, and seasonal conditions should be considered in interpreting PREDICTA® results and assessing disease risk. Moreover, the repeated use of PREDICTA® tests within a cropping system for patterns observed in pathogen levels and disease occurrence can be used to refine interpretation.
While this study has provided much useful information, it has highlighted the need for further data collection. Further data should be collected to confirm the relationship between the incidence of charcoal rot and the amount of lodging. This should include data across the northern cropping region, hopefully with rainfall levels above the bottom quartile. A study should be undertaken to collaborate the suggested log of 2.5 copies per gram as an indicator of the high incidence of charcoal rot and the associated risk of serious lodging to use this as a management tool. Such a study should be designed so that it isolates background effects and is sufficiently precise to give reliable estimates of the effects. The persistence of Mp in soil should be established by taking a time series of samples over a year. Those data could then be used in the development of a sampling scheme for a predictive model. Other agronomic measures including the 1000 grain weight, panicle size, and plant density should be collected to enable the growth stages to be quantified. Basic field sampling variation across a large paddock should be quantified—this would include a spatial part within a paddock, as well as sampling variation from adjacent points, to prove the sampling precision of various strategies. Information should be obtained on the susceptibility of varieties to charcoal rot. Such a study should include information on the disease load in the soil and be replicated across sorghum growing regions over several years. Where possible, studies should be limited to a few varieties to simplify the experimentation.

Funding

This research was funded by the Grains Research Development Corporation for the project, “Integrated disease management tools to manage summer crop diseases in the Northern Region,” with funding code USQ1907-001RTX.

Data Availability Statement

Data are available upon request.

Acknowledgments

I would like to thank the growers, agronomists, and industry partners who have taken part and helped in the conduct of this research. Special thanks to Yuriy Tsupko, Igor Kutnetsov, and Peter Buyoyu who provided technical assistance in sample and data collection. I thank Alexandros Georgios Sotiropoulos for assistance in creating the site map image, and Alan McKay of the SARDI for reviewing and supplying valuable suggestions in this report. The statistical data analyses and interpretations were provided by Raymond Correll of Rho Environments, SA.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

Table A1. GPS coordinates of sites sampled for PREDICTA®B test.
Table A1. GPS coordinates of sites sampled for PREDICTA®B test.
SiteRegionGPS Coordinates
OrionCentral Queensland−24.230583;148.471065
OrionCentral Queensland−24.236667;148.480278
OrionCentral Queensland−24.237500;148.494167
ClermontCentral Queensland−22.642091;147.759482
ClermontCentral Queensland−22.635722;147.756300
ClermontCentral Queensland−22.623522;147.750201
KilcumminCentral Queensland−22.584814;147.628012
KilcumminCentral Queensland−22.567799;147.628725
KilcumminCentral Queensland−22.585285;147.627314
KilcumminCentral Queensland−22.585290;147.627320
CrinumCentral Queensland−23.219433;148.119647
CrinumCentral Queensland−23.219434;148.119655
CrinumCentral Queensland−23.156504;148.206798
CrinumCentral Queensland−23.156500;148.206800
ChimsideCentral Queensland−23.139142;148.057989
ChimsideCentral Queensland−23.139147;148.057995
ChimsideCentral Queensland−23.141474;148.079976
ChimsideCentral Queensland−23.141480;148.079980
Lawrence Lane Central Queensland−23.138333;148.061667
Lawrence LaneCentral Queensland−23.138400;148.061670
ArcturusCentral Queensland−23.841507;148.403789
ArcturusCentral Queensland−23.841508;148.430795
ArcturusCentral Queensland−23.843781;148.396694
ArcturusCentral Queensland−23.843785;148.396700
ArcturusCentral Queensland−23.934352;148.340796
ArcturusCentral Queensland−23.934360;148.340800
ArcturusCentral Queensland−23.962057;148.327431
ArcturusCentral Queensland−23.962057;148.327440
ArcturusCentral Queensland−23.981195;148.326462
ArcturusCentral Queensland−23.981200;148.326470
BrooksteadSouthern Queensland−27.758518;151.477055
BrooksteadSouthern Queensland−27.747404;151.478963
BrooksteadSouthern Queensland−27.758377;151.483498
BrooksteadSouthern Queensland−27.758380;151.483488

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Figure 1. The 34 sites of sorghum paddocks in Central Queensland (CQ) and Southern Queensland (SQ) that were sampled for the charcoal rot PREDICTA®B test.
Figure 1. The 34 sites of sorghum paddocks in Central Queensland (CQ) and Southern Queensland (SQ) that were sampled for the charcoal rot PREDICTA®B test.
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Figure 2. Relationship between the percent incidence of charcoal rot and the percent of lodging.
Figure 2. Relationship between the percent incidence of charcoal rot and the percent of lodging.
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Figure 3. Relationship between the data from sowing and pre-harvest sampling times for Mp.
Figure 3. Relationship between the data from sowing and pre-harvest sampling times for Mp.
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Figure 4. Changes in Mp copies between sowing and pre-harvest. The line is the 1:1 line.
Figure 4. Changes in Mp copies between sowing and pre-harvest. The line is the 1:1 line.
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Figure 5. Relationship between the amount of Mp and the percent of lodging per region (as defined by the nearest town).
Figure 5. Relationship between the amount of Mp and the percent of lodging per region (as defined by the nearest town).
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Figure 6. Effect of amount of pre-sowing Mp on incidence of charcoal rot.
Figure 6. Effect of amount of pre-sowing Mp on incidence of charcoal rot.
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Figure 7. Relationship between Mp levels and disease incidence on standing and lodged plants.
Figure 7. Relationship between Mp levels and disease incidence on standing and lodged plants.
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Figure 8. Relationship between the amount of Mp in the soil and the lesion length.
Figure 8. Relationship between the amount of Mp in the soil and the lesion length.
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Figure 9. Comparison of lesion lengths in lodged and erect plants.
Figure 9. Comparison of lesion lengths in lodged and erect plants.
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Figure 10. Relationship between the amount of Mp present and the observed crop yield. The key to symbols representing towns is given in Figure 8.
Figure 10. Relationship between the amount of Mp present and the observed crop yield. The key to symbols representing towns is given in Figure 8.
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Figure 11. Relationship between the amount of Mp present and the potential grain yield. The key to the symbols is given in Figure 8.
Figure 11. Relationship between the amount of Mp present and the potential grain yield. The key to the symbols is given in Figure 8.
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Figure 12. Effect of sorghum variety on incidence of charcoal rot.
Figure 12. Effect of sorghum variety on incidence of charcoal rot.
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Figure 13. Relationship between the amount of pathogen present and the lodging, showing varieties.
Figure 13. Relationship between the amount of pathogen present and the lodging, showing varieties.
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Figure 14. Effect of rainfall and variety on the incidence of charcoal rot.
Figure 14. Effect of rainfall and variety on the incidence of charcoal rot.
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Figure 15. Relationship between rainfall and the amount of Mp at sowing, showing the amount of lodging (coloured dots) and the cultivar (plotting characters).
Figure 15. Relationship between rainfall and the amount of Mp at sowing, showing the amount of lodging (coloured dots) and the cultivar (plotting characters).
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Table 1. Adjusted R2 for the relationship between the amount of disease present and lodging caused by Macrophomina phaseolina (Mp).
Table 1. Adjusted R2 for the relationship between the amount of disease present and lodging caused by Macrophomina phaseolina (Mp).
ParameterLinearQuadratic
Incidence0.7920.899
Lesion Length0.2580.265
Index of Das et al.0.3300.433
Table 2. Changes in the log counts of Mp and the ratio of the number of copies between samplings.
Table 2. Changes in the log counts of Mp and the ratio of the number of copies between samplings.
Nearest TownObservationsSowingPre-HarvestChangeFactor
Arcturus103.283.440.161.43
Brookstead33.854.580.735.36
Chimside43.132.50−0.630.23
Clermont32.161.98−0.180.66
Crinum23.993.52−0.470.34
Kilcummin42.282.26−0.020.95
Orion34.223.70−0.520.30
Table 3. The overall relationship between amount of Mp present and the percent lodging.
Table 3. The overall relationship between amount of Mp present and the percent lodging.
Sample TimeSlopeSEt-Testp-Value
Sowing3.573.041.180.250
Pre-harvest5.502.851.930.064
Table 4. Comparison of percent of lodging across each region, as defined by nearest town environment.
Table 4. Comparison of percent of lodging across each region, as defined by nearest town environment.
Region
(Nearest Town)		CountLodging
Mean		Mp
k CountLogarithm
Arcturus1025.519003.28
Brookstead3671103.85
Chimside4013503.13
Clermont301462.16
Crinum2096803.99
Kilcummin41.51922.28
Orion32166004.22
Table 5. Averaged relationship across regions between the amount of Mp present and the percent of lodging.
Table 5. Averaged relationship across regions between the amount of Mp present and the percent of lodging.
Sample TimeSlopeSEt-Testp-Value
Sowing0.0420.0890.480.639
Pre-harvest−0.350.16−2.230.037
Table 6. Relationship between the amount of Mp present and the percent of lodging using Arcturus data only.
Table 6. Relationship between the amount of Mp present and the percent of lodging using Arcturus data only.
Sample TimeSlopeSEt-Testp-Value
Sowing9.155.631.630.14
Pre-harvest10.468.461.240.25
Table 7. Comparison of lesion lengths in lodged and erect plants using data from sites with some lodging.
Table 7. Comparison of lesion lengths in lodged and erect plants using data from sites with some lodging.
Length (mm) in Lodged PlantsLength (mm) in Erect PlantsDifferenceSE of Differences
2722343823
Table 8. The averaged relationship between the amount of Mp present and the measured yield, expressed as kg/ha.
Table 8. The averaged relationship between the amount of Mp present and the measured yield, expressed as kg/ha.
Sample TimeSlopeSEt-Testp-Value
Sowing−200.5277.7−0.7220.476
Pre-harvest235.5269.90.8730.391
Table 9. The averaged relationship between the amount of Mp present and the potential yield, expressed as kg/ha.
Table 9. The averaged relationship between the amount of Mp present and the potential yield, expressed as kg/ha.
Sample TimeSlopeSEt-Testp-Value
Sowing−200.5277.7−0.7220.476
Pre-harvest235.5269.90.8730.391
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Adorada, D.L. Assessing PREDICTA®B as a Potential Disease Risk Management Decision Tool for Sorghum Charcoal Rot Based on Disease Levels, Lodging, and Associated Yield Loss. Agronomy 2023, 13, 2494. https://doi.org/10.3390/agronomy13102494

AMA Style

Adorada DL. Assessing PREDICTA®B as a Potential Disease Risk Management Decision Tool for Sorghum Charcoal Rot Based on Disease Levels, Lodging, and Associated Yield Loss. Agronomy. 2023; 13(10):2494. https://doi.org/10.3390/agronomy13102494

Chicago/Turabian Style

Adorada, Dante L. 2023. "Assessing PREDICTA®B as a Potential Disease Risk Management Decision Tool for Sorghum Charcoal Rot Based on Disease Levels, Lodging, and Associated Yield Loss" Agronomy 13, no. 10: 2494. https://doi.org/10.3390/agronomy13102494

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