Sensor Placement and Floating Row Cover Impact on Fruit Rotting Diseases in Strawberries
Various sensors can be used to monitor environmental variables in fields, including ambient temperature, relative humidity, rain depth, wind speed, leaf wetness (LWD), soil temperature, and soil moisture. These can be critical variables for decision-making for crop protection or yield prediction. These data (namely ambient temperature and LWD) can also be useful for disease prediction models such as those used for Botrytis (BFR) and anthracnose (AFR) fruit rots of strawberry (Hu et al. 2021). A traditionally placed weather station at the edge of a field (see ‘elevated’ station below) may also not fully capture the conditions in the field. Floating row covers are a common tool in Mid-Atlantic strawberry fields for manipulating the crop microclimate for facilitating crop development under cold conditions and for protecting from freeze events. Therefore, covered strawberry plants should have a vastly different microclimate than would be reported from traditionally placed weather stations or non-covered plants. We evaluated differences in sensor readings based on placements either in the canopy or in the traditional, elevated setting. We also evaluated the effect of row covers on the sensor readings. Lastly, we evaluated how these different placements would affect disease prediction models utilized in a fungicide spray program.
This was conducted in six field trials with plasticulture strawberries located in Maryland and Virginia. Temperature and LWD sensors (Teros 12 and Phytos 31, Meter-Group, Inc.) were placed in different positions at each trial (Fig. 1). The difference in microclimate between beds with and without row covers applied for a portion of the fall season was evaluated in two of the trials.
Air temperature and LWD data from the sensors in the trials were uploaded to AgZoom and logistic regression models developed by Wilson et al. (1990) and Bulger et al. (1987) were used to predict disease infection risk for AFR and BFR on immature and mature strawberries as integrated by Mackenzie and Peres (2012a; b). Risk values were checked once per day during the ripening period.
Fungicide treatments were arranged in each of the six trials in a randomized complete block design. The three fungicide treatments included (1) model-timed sprays based on W and T input from the elevated sensor station; (2) model-timed sprays based on W and T input from the canopy sensors, and (3) grower standard (i.e., weekly sprays). For treatments 1 and 2, fungicides were applied within 48 h after the disease risk from either model crossed the INF threshold (Table 1).
For all trials, ripe berries were harvested twice a week and the number of fruit with AFR and/or BFR symptoms, marketable asymptomatic fruit (weighing 10 g or greater), and other culls (i.e., smaller than 10 g, and/or damaged by other biotic or abiotic causes) were counted. In addition, marketable berries were weighed. AFR and BFR incidence, marketable fruit count and cull fruit (non-AFR/BFR, non-marketable fruit) count was noted.
Row cover effects. The application of row covers during two to three weeks during fall at the two trials increased the crop growing degree days by an average of 112. Sensors under row covers recorded more of an increase in canopy temperatures during the day (about 4 °C) than at night (about 1 °C) compared to the elevated station. Whether covered or non-covered, sensors placed on strawberry beds appeared to report more widely fluctuating temperatures than were recorded at the elevated weather station. This could be significant for accurately determining the severity of freeze events or for calculating growing degree days.
During the spring, the plots that were covered or non-covered in the fall reported very similar temperature and leaf wetness conditions (the two variables used to determine AFR and BFR risk). Also, the incidence of AFR and BFR were similar, whether the plots were covered or not. Interestingly, the average marketable yield per plant tended to be lower for the plots that were covered during fall. Due to these results, the usage of fall row covers may not have been helpful for the two trials that we conducted. These results were in contrast to a North Carolina study that found fall row cover applications to increase crop growth, leading increased yield in the spring (Fernandez 2001). A later application date for the fall row covers may have promoted more flower bud development and an increase in yield as was observed in the North Carolina study.
Sensor placement effects. During the ripening period, the canopy sensor placements on the exterior and interior beds were significantly warmer and wetter than the elevated sensors. There was not a major difference between interior beds and exterior beds, other than interior beds tending to be slightly wetter. These warmer and wetter conditions led to the disease models reporting more days with moderate/high infection risk for AFR and BFR. In turn, this caused more fungicide applications to be triggered based on placement of sensors at the canopy level vs. the elevated position. Despite this, the usage of the disease models resulted in a reduced number of fungicide applications compared to the weekly grower standard treatment. The AFR and BFR incidence between the canopy sensor placement treatment and the grower standard treatment were statistically equivalent, but the disease incidence was significantly higher in the elevated station treatment than the grower standard treatment.
These experiments demonstrated the importance of sensor placement in strawberries, especially for predicting disease risk. AFR and BFR are two fruit rotting diseases, and sensors placed within the fruiting zone (such as the interior bed placement) may more accurately report whether conditions are conducive for disease. Furthermore, these field fungicide trials showed the effectiveness of utilizing disease prediction models for effective disease control with reduced fungicide inputs.
Acknowledgments - This research was conducted in collaboration with Chuck Johnson, John-Lea Cox, and Jayesh Samtani. This publication was funded by the Northeastern IPM Center through Grant # #2018-70006-28882 from the National Institute of Food and Agriculture, Crop Protection and Pest Management, Regional Coordination Program.
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This article appears in October 2022, Volume 13, Issue 7 of the Vegetable and Fruit News.