Mengjun Hu1, John Lea-Cox1, Jayesh Samtani2, Roy Flanagan III2, and Chuck Johnson2
1 Department of Plant Science and Landscape Architecture, University of Maryland 2 Virginia Cooperative Extension

Plasticulture growers in production areas outside Florida and California, such as the coastal plain or piedmont areas of the eastern US, typically use lightweight spun bound or nonwoven row covers to promote floral bud initiation (degree-day accumulation) in late fall, as well as for frost or freeze protection in spring. Growers in colder climates, such as the Appalachians or the Midwest, use row covers to protect their strawberry crop over longer periods, from December into March, as well as during cold snaps throughout flowering. Current IPM tools are typically not designed to monitor environmental variables at the canopy-level. Understanding environmental conditions within plant canopies, with or without row covers, is valuable for risk management throughout the production period.

Figure 1. Sensor placements at one site: CBS 1 (non-edge row); CBS 2 (edge row); and ATMOS

During the 2019/20 and 2020/21 strawberry growing seasons, canopy-based sensor stations with multiple environmental sensors (Meter Group Inc., Pullman, WA) were installed at four farms in Maryland and Virginia. Five-minute resolution temperature and leaf wetness duration data were uploaded from these stations and informed the disease models previously developed for anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR), which were incorporated into the cloud-based AgZoom software (Verdu, Spain).  Real-time summary risk data for each model were provided via the AgZoom app. A two-year evaluation of this microclimate-based disease forecasting system was conducted in each location. Fungicide treatments were arranged in a randomized complete block design, and applications were based on three strategies: (1) predictive data from canopy-based sensors (CBS), (2) predictive data from sensors on an on-farm weather station (ATMOS 41 sensors) installed at the side of each field at 6 ft height), and (3) grower standard (GS) sprays. GS plots were sprayed every 7 to 10 days, depending on weather conditions. For the ATMOS and the CBS treatments, fungicide applications were independently guided by the risk determined from each disease model output, starting at bloom. AFR and BFR incidence and marketable fruit yield were determined every week. The main goals of this study were to understand the differences among environmental variable inputs and increased model precision due to sensor placement (within the canopy – CBS and 6 ft above the canopy – ATMOS; Fig. 1) and to validate the canopy-based disease risk models for timing fungicide applications to control AFR and BFR.

Temperature. The use of floating row covers significantly increased air temperatures in the strawberry canopy (CBS) compared to those in the weather stations (the ATMOS sensors, 6 ft above the canopy). Differences in daily temperatures between CBS and ATMOS sensors were greatest during the ‘ripening’ period (spring/summer), with canopy temperatures being warmer. Across the four sites with the ‘spring covered’ and ‘winter covered’ periods, the difference in temperature between canopy-based CBS sensors and weather station ATMOS sensors was larger during the ‘spring covered’ than the ‘winter covered’ period. Sensors under row covers reported higher average daily temperatures compared to non-covered sensors during the ‘fall-covered’ period. Furthermore, strawberry canopies appeared to be warmer during the day than at night time, compared to temperatures in the weather stations (ATMOS). Similarly, row covers increased canopy temperatures more during the day than at night time.

Leaf wetness. In general, leaf wetness duration tended to be longer in the strawberry canopy compared to the weather stations. Among the canopy-level sensors, non-edge rows tended to be wetter compared to edge-rows. Interestingly, the covered plot, regardless of sensor placement on edge or non-edge rows, was drier than the non-covered plot during the fall, at one and only site, where two adjacent plots were included to determine fall cover effects. However, this trend was not noted in the following year, when the covered sensor on the edge row had statistically lower wetness duration than the covered sensor on the non-edge row or non-covered sensors.

Infection risk and disease management implications. The predicted infection risk for AFR and BFR tended to be lower based on the weather station ATMOS sensors 6 feet above the strawberry canopies compared to the CBS sensors within the strawberry canopies, leading to more predicted infection events from weather data from within the canopies. These infection events triggered more fungicide applications for the CBS treatment than the ATMOS treatment, yet both treatments resulted in fewer fungicide applications than the GS treatment. Differences in AFR and BFR incidence were observed at two sites in 2019/20 and 2020/21 seasons, where the GS and CBS treatments had the least average disease incidence. While the row cover did not greatly affect predicted AFR or BFR risk during the ‘fall covered’ period, sensors placed on non-edge rows predicted more infection days than the edge-row or ATMOS. Although disease is unlikely to occur early in the growing season, conducive conditions may or may not increase pathogen loads latently present in plants during the fall that could affect disease severity later on in the spring. No differences were observed between treatments in marketable yield, presumably due to generally lower than normal disease pressure in both years.

In conclusion, leaf wetness varied significantly between measurements made outside the strawberry canopy (ATMOS) and measurements made within the canopy (CBS), affecting disease model predictions. While row covers increased temperature significantly, its effect on leaf wetness duration is less clear. The enhanced sensitivity of disease predications from CBS (especially non-edge row placement) may benefit cultivars with high disease susceptibility.


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This work was funded in part 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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