Southern stem rot (SSR) of peanut, caused by
Plant microclimate conditions have frequently been reported to significantly influence development of diseases in many crops (
Under field conditions, peanut crops are commonly exposed to multiple biotic and abiotic stresses that may lead to defoliation and subsequent microclimate changes. Defoliation from late leaf spot (LLS), caused by
Artificial canopy modifications in peanut have been reported to influence development of diseases such as Sclerotinia blight (
Earlier studies on the relationship between LLS and SSR have examined the effect of peanut residues on the development of SSR, in which defoliated peanut leaves may serve as a stimulant or food base for
Among the available literature, studies to date have not been reported to evaluate differences in plant microclimates as a function of LLS defoliation and how these differences may affect SSR incidence. The current study’s objectives were 1) to investigate the effect of LLS defoliation on plant microclimate environmental parameters (canopy temperature, soil temperature, and soil moisture); and 2) to evaluate the relationship of LLS defoliation and SSR development.
Experiments were conducted in 2016, 2017, 2018, and 2019 at Clemson University’s Edisto Research and Education Center in Blackville, SC. In 2016, the study was conducted in three fields: D13A; B5A; and B9A. In 2017, the study was performed in two fields: D13A and L1C. In 2018, D13A and L1D fields were used for the experiment. L1C and L1D fields were two different fields located next to each other. In 2019, only D13A was used for the study. Soil type in examined fields was a Barnwell loamy sand. D13A and B5A were not irrigated, while B9A, L1C, and L1D were irrigated. Irrigation was performed using a Reinke lateral move irrigation system (Reinke Manufacturing Co., Inc., Deshler, NE, USA) scheduled for 19 mm irrigation per two weeks. If <19 mm rainfall occurred within the two-week period, supplemental irrigation was used to reach a total equivalent of 19 mm.
To encourage different levels of LLS development, three fungicide spray programs were applied over four peanut cultivars according to a split plot experimental design. Spray program (main plot) consisted of three different frequencies of chlorothalonil (Bravo WeatherStik; Adama USA; Fungicide Resistance Action Committee [FRAC] M5) applied at 1.26 kg active ingredient (a.i.)/ha: (i) 3 sprays at 75, 90, and 105 days after planting [DAP]; (ii) 4 sprays at 60, 75, 90, and 105 DAP; and (iii) 6 sprays at 45, 60, 75, 90, 105, and 120 DAP. Four runner market type peanut cultivars (sub plot) were selected for varying relative susceptibility to LLS and SSR: (i) Georgia 12Y (resistant to LLS and SSR); (ii) Georgia 06G (moderately resistant to LLS and susceptible to SSR); (iii) TUFRunner 511 (very susceptible to LLS and moderately resistant to SSR); and (iv) Georgia 13M (very susceptible to LLS and susceptible to SSR) (Anco and Thomas 2019). Twelve treatments (combinations of sprays and cultivars) were replicated four times per field, with the exception of D13A in 2016 where treatments were replicated 6 times. Blocks were separated by a 3-m wide alley.
In 2016, peanut was planted in B5A, D13A, and B9A on 21 April, 27 April, and 16 May, respectively. In 2017, planting dates were 4 May in D13A and 18 May in L1C. In 2018, peanut was planted in D13A and L1D on 1 May and 1 June, respectively. Peanut was planted in D13A in 2019 on 13 May. Seeding rate was 19 seed/m at a depth of 5 cm. Sub plots were four 96-cm spaced rows by 12-m in length. The four rows were further separated into two yield rows and two traffic rows.
Yield rows of each sub plot were inoculated with
Fungicide treatments and agrochemical maintenance applications were applied parallel to sub plots while driving over adjacent traffic rows of contiguous plots whereas yield rows were used for data collection and were not defiled by tire travel. Fungicide applications were performed with two DG8002 flat fan nozzles/row (48.26 cm spacing) delivering 142.5 L/ha at 345 kPa. Line between the two traffic rows were traveled and the adjacent two yield rows were sprayed.
Percent LLS leaflet defoliation per plot was visually estimated from 6 subsamples of non-overlapping 0.3-m sections of the 2 yield rows, excluding plot ends. In 2016, defoliation was rated prior to inversion. Defoliation ratings in 2017, 2018 and 2019 were taken at biweekly interval starting from 75 DAP to 140 DAP in each sub-plot. Area under LLS defoliation curve (AUDC) from 2017 to 2019 experiments was calculated using the following formula:
Southern stem rot incidence per plot was evaluated at inversion. Incidence was quantified by summing non-overlapping discrete 0.3-m sections of windrow (an inverted peanut row from two yield rows in each sub-plot) expressing symptoms of SSR or showing signs of
Canopy and soil temperature were monitored in 15 min intervals using RC-5 USB temperature data loggers (Elitech Technology, Inc., Milpitas, CA, USA) starting from 3 to 11 weeks after planting to the day of peanut inversion during the 2016 through 2019 growing seasons. Data loggers were placed under the canopy and secured on the surface of the ground using wires. Soil temperature was monitored with loggers buried in the pegging zone at a depth of 7 to 10 cm. In 2016, 2017, and 2018, canopy and soil temperature data loggers were placed at one of the yield rows of each sub-plot in one set of treatment replication (12 treatments, 1 replication) per field. Therefore, each field had the total of 24 data loggers: 12 for canopy temperature and 12 for soil temperature. Temperature data (microclimate) were combined (pooled) from multiple fields within a trial year, and when treatments were compared, field functioned as a level of replication and was included as a random effect in the model. In 2019, canopy and soil temperature data loggers were placed in two sets of treatment replications in the field as there was only one trial field. The 2019 field had the total of 48 data loggers where 24 data loggers recorded canopy temperature and another 24 data loggers recorded soil temperature.
Cumulative canopy degree days from when temperature data loggers were placed in the fields (8 Jun 2017 and 29 Jun 2018) to 140 DAP (selected to match with LLS defoliation observation days) in each treatment was calculated by the formula:
Where Y(t) is canopy temperature at time t, t is time, alpha is canopy maximum temperature, beta is canopy temperature at t0 (initial canopy temperature or canopy minimum temperature), gamma is the rate of temperature shift (the slope of temperature increase),
Soil moisture was monitored and recorded using soil moisture sensors S-Smx-M005 and HOBO data loggers (ONSET Computer Corporation, Bourne, MA, USA) in the 2017 and 2018 experiments. In 2019, soil moisture was monitored using WATERMARK sensors and Soil Moisture Meters (IRROMETER Company Inc., Riverside, CA, USA). Soil moisture or watermark sensors were placed in the pegging zone at a depth of 7 to 10 cm. In 2017 and 2018, soil moisture sensors (probes) were placed at one of the yield rows of each sub-plot (the same row and sub-plot where temperature data loggers were placed) in one replication of treatments per field. Each field in 2017 and 2018 had the total of 12 soil moisture sensors (probes). With this setup, when soil moisture data (microclimate) were combined (pooled) from multiple fields within a trial year, and when treatments were to compare, then field functioned as a level of replication and was included as a random effect in the model. On the other hand, in 2019 where the experiment was performed in one field, watermark sensors were placed in two sets of treatment replications (12 treatments, 2 replications). Therefore, the 2019 trial field had the total of 24 watermark sensors in the field.
Cumulative daily soil moisture loss between two rainfall events at early season (75 to 89 DAP) and when LLS defoliation was significant (103 to 117 DAP) were calculated and compared across treatments. Soil moisture loss between two rainfall events at both early- and mid-season were calculated only in the periods when no irrigation was applied in between the two rainfall events.
The GLIMMIX procedure of SAS was used to analyze treatment effects for LLS defoliation, AUDC, SSR severity, daily maximum canopy and soil temperature, cumulative canopy and soil degree day (DD15), cumulative daily soil moisture loss, and yield according to generalized linear mixed modeling. Laplace approximation was used to improve standard error estimation (
To evaluate the influence of LLS defoliation on canopy and soil temperature, LLS defoliation was linearly regressed against daily maximum canopy and soil temperature at each observed DAP in each trial year. Regression was also performed over the pooled data including year as a random effect. To investigate if LLS defoliation was influential to the rate of temperature change within plant canopy, AUDC were regressed against the slope of fitted canopy temperature curves. Relationship of LLS defoliation and cumulative canopy degree day (DD15) was assessed with linear regression using AUDC and DD15 data. The influence of LLS defoliation on soil moisture loss between two rain events was investigated by regressing AUDC differences between early and mid-season against soil moisture loss differences during the same time. Lastly, linear regression analyses using AUDC and percent SSR data from each trial year (with field and replication within the field as random effects) as well as using combined year AUDC and percent SSR data (with year, field within year, and replication within field within year as random effects) were performed to evaluate the relationship between LLS and SSR.
Establishment of varying LLS defoliation in peanut has been frequently achieved with application of fungicides that either differed in individual efficacy (
Late leaf spot (LLS) defoliation at days after planting (DAP) as affected by number of chlorothalonil applications and peanut cultivar in 2016, 2017, and 2018.
Estimated area under late leaf spot (LLS) defoliation curve (AUDC) as affected by treatment in 2017 and 2018.
Significant positive linear relationships between LLS defoliation and daily maximum canopy temperature were observed in both 2017 (P = <0.0001 and R2 = 0.223) and 2018 (P = <0.0001 and R2 = 0.294) (data not shown). A slightly stronger relationship was associated with the combined data from 2017 and 2018 (P = <0.0001 and R2 = 0.305) (
With regards to maximum daily soil temperature, relationships with LLS defoliation were weaker overall (
Linear regression analyses on AUDC (defoliation progress from 75 to 140 DAP) and the slopes of daily canopy temperature increase from minimum to maximum during the same time showed a non-significant and significant positive linear relationships in 2017 and 2018, respectively (
Regression analysis showed non-significant linear relationships between AUDC and DD15 in 2017 and 2018 with P-values 0.2660 and 0.6141, respectively (data not shown). These consistent results across years suggests that LLS defoliation in the experiments did not substantially influence accumulated heat units within the canopy.
Following excessive sensor and logger failings in 2018 to record soil moisture data and the absence of LLS defoliation in 2019, investigation on the relationship between LLS defoliation and daily soil moisture loss was only conducted for the 2017 data. Soil moisture loss at both early- and mid-season were calculated in the periods between two rainfall events and only when no irrigation was applied within the duration between the two events. Soil moisture data within the periods between 24 July 2017 to 2 August 2017 (75 to 89 DAP) and between 9 to 24 August 2017 (103 to 117 DAP) were used to calculate soil moisture loss at early- and mid-season, respectively. Cumulative daily soil moisture loss between periods of rainfall at early- (75 to 89 DAP) and mid-season (103 to 117 DAP) when AUDC ranged from 0 to 14 and 2 to 161, respectively, did not differ among treatments (
Cumulative daily soil moisture loss between two rainfall events at early season and mid-season and the corresponding area under late leaf spot defoliation curve (AUDC) as affected by spray program and peanut cultivar in 2017.
Non-significant linear relationships between AUDC and SSR were observed from the regression with the combined data from 2017 and 2018 (P = 0.0800) (
In conclusion, while LLS defoliation may to a slight degree appear to affect canopy maximum daily temperature and the rate of temperature shift within the canopy, its inconsistent effect on observed microclimate components across years indicates that its overall impact (direct or indirect) on SSR development was minimal. Canopy structure is dynamic throughout the growing season as the population of leaves and stems generally will increase. While plant (re-)growth was not simultaneously measured along with LLS development in this study, LLS defoliation data over time suggest that percent of leaves in each cultivar continue to decrease along with the increase percent of LLS defoliation toward the end of cropping season. This indicates that newly formed leaves (re-growth) on each cultivar may be insufficient to offset LLS defoliation and therefore, it may not be a substantial factor diminishing the influence of LLS defoliation on plant microclimates. However, differences in canopy characteristic (e.g. open and close) among Runner type peanuts, as reported in another study (
Humidity within plant canopy has been thought as one important factor affecting SSR development (
Thanks to James Thomas, Justin Hiers, and Trevor Zorn for assistance in establishing and maintaining field plots. Support for this project was provided by the South Carolina Peanut Board. This material is based upon work supported by NIFA/USDA, under project number SC-1700532. Technical Contribution No. 6926 of the Clemson University Experiment Station.
Edisto Research and Education Center, Clemson University, Blackville, SC 29817