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Funded Project |
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Funding Program:
IPM Partnership Grants |
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Project Title:
Development and evaluation of microclimate-based decision support tools, for sustainable strawberry production |
Project Director (PD):
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Lead State: MD Lead Organization: University of Maryland |
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Cooperating State(s):
Virginia |
| Undesignated Funding: $49,802 |
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Start Date: Apr-01-2019 End Date: Mar-31-2021 |
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No-Cost Extension Date: Sep-30-2021 |
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Pests Involved: Strawberry Botrytis and Anthracnose fruit rot |
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Site/Commodity: Strawberry |
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Area of Emphasis: Advanced production systems |
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Summary:
Strawberry Botrytis fruit rot (BFR) and anthracnose fruit rot (AFR) both are devastating diseases that typically drive fungicide applications. Based on critical environmental factors (i.e. temperature and leaf wetness), the Strawberry Advisory System (SAS) was developed to predict real-time BFR and AFR risks to better time fungicide applications, avoiding unnecessary sprays and costs. However, SAS uses on-farm weather stations for data inputs, which are not capable of monitoring environmental conditions in modified environments. In areas outside Florida and California, row covers are critical to strawberry plasticulture production systems, to increase yield potential by accumulating degree days and increasing floral initiation during fall, and to minimize winter and frost damage. In this project, we propose to examine the precision of BFR and AFR predictive models, comparing canopy-based data inputs vs. weather-station data inputs into these models. Trials at three locations including one organic farm in Maryland and Virginia will allow us to assess the efficacy of the canopy-level sensor system in both “covered” and “non-covered” environments, to understand how row covers alter the environmental conditions related to disease infection. We anticipate that our proposed microclimate monitoring systems will improve the efficacy of disease forecasting, leading to higher marketable fruit yield and lower AFR or BFR incidence for both organic and conventional productions, in comparison with the SAS or calendar-based sprays. These sensor systems will also simultaneously provide frost and soil moisture information to growers for additional risk-management decisions. Furthermore, we will facilitate adoption of the microclimate system through a variety of extension activities.
Objectives: Obj.1. Test a commercially-available microclimate monitoring system for plasticulture strawberries, and analyze differences among environmental variable inputs due to sensor placement. Obj.2. Validate the canopy-based disease risk models for timing fungicide applications to control AFR and BFR, and integrate interactive tools for the use of the microclimate monitoring system. Obj3. Facilitate adoption by educating growers on efficacy and use of the microclimate system through outreach efforts. |
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Final Report: |
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Outputs a) • A sensor network equipped with six different sensors (Meter Group Inc., Pullman, WA) were installed at four farms in Maryland and Virginia, feeding the disease models for anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR) that were integrated into AgZoom. Real-time weather data and disease infection risk can be visualized and retrieved for these three sites at AgZoom at any time. A two-year evaluation of the microclimate-based disease forecasting system was conducted at the four locations noted above. b) • Fungicide treatments including the microclimate systems, traditional on-farm weather station (ATMOS), and grower standard sprays were arranged in the trials in a randomized complete block design. Grower standard plots were sprayed every 7 to 10 days, depending on weather conditions. For ATMOS and the microclimate treatments, fungicide applications were independently guided by the risk determined by each system model output throughout the season. AFR and BFR incidence and marketable fruit yield were investigated on a weekly basis. Treatment was considered as a fixed effect, and block was considered as a random effect. Means were compared for significant differences. c) • Weather variables affecting disease infection. Leaf wetness. In general, the leaf wetness and relative humidity tended to be higher at the canopy level sensors than the ATMOS sensor (the sensor placed adjacent to the field). The sensors placed on the outside of the canopy also tended to have a larger daily leaf wetness than the inner canopy sensors. During the Fall row cover period, the covered sensors generally had less wetness duration than non-covered sensors. Temperature. The temperature was not greatly affected by the use of the floating row covers. However, the floating row covers applied during the Fall resulted in higher average temperature than the non-covered sensors. • Infection risk. The infection risk for AFR and BFR tended to be lower at the ATMOS sensor as opposed to the canopy level sensors, leading to more infection events. These infection events triggered more fungicide applications on the microclimate treatments than the ATMOS treatments, yet both treatments resulted in less applications than the grower standard treatment. Differences in AFR and BFR incidence were only observed at one site in 2021, where the grower standard and microclimate treatments had the least average disease incidence. However, marketable yields seem to be comparable between treatments. |
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Outcomes This project does not measure changes. |
Report Appendices
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