We have studied weed phenology using cumulative degree-days to generate a phenological calendar from the flowering sequence of 94 indicator plants. A degree-day database is now accessible on the web in real-time. This database is linked on the web directly to the phenological calendar, which can be used with degree-days to predict pest activity (http://www.oardc.ohio-state.edu/gdd/). Over 5 years, the rank order of phenological events was very consistent, even when growing conditions varied greatly. Therefore, we can follow these events to determine the optimum time for scouting
This year we collected data for use in testing the accuracy of our prediction approaches and put the weed data into the biological calendar on the web. Next year we will use these predictions to time the application of control measures. The specific objective for this grant project was to add to the baseline dataset for weed emergence by expanding the species for which emergence data are available and increasing accuracy of emergence counts.
Weed emergence was monitored in eight 0.5-m2 permanent quadrats located about 10 m apart, and at least 10 m from the field edge. Farm managers conducted land preparation and planting operations according to their normal schedule. Emergence counts and indicator plant phenology observations were made 3 times per week. First and full bloom of indicator plants were determined by standard procedures. Various percentages of weed emergence were determined by developing best-fit models describing the progress of emergence with day of year and matching these to degree-days (base temperature = 0).
Data were plotted using Systat and percent emergence at various times were estimated by interpolating between sample points. Air temperature, soil temperature at a depth of 2.5 cm, and rainfall were monitored at each site, and soil samples were obtained to determine initial soil water content, water holding capacity, and water retention curves.
Seedling emergence curves were generated for 6 additional species: common groundsel, common mallow, dandelion, hairy galinsoga, common purslane, and velvetleaf (see attached figures). For all species except common mallow, we were able to detect the time of initial emergence, an indicator that is frequently absent in emergence data. Since rainfall was abundant in 2003, emergence curves reflect the response of seeds to soil temperature conditions.
We identified two general patterns of emergence: for dandelion and hairy galinsoga, peak emergence was reached during a relatively short time interval (~ 30 days). For other species, emergence continued over a large proportion of the growing season so that peak emergence occurred long after initial emergence (160 to 200 days).
Some weeds are considered early emerging species whereas others are considered late emergers. However, we also found that the emergence interval varied among these species. For example, the earliest emerging species in this group was common mallow, but this species was also one with the longest period during which emergence continued. The latest emerging species was common purslane, which reached its peak emergence about the same time as common mallow.
We selected three key emergence percentages (20, 50, and 80%) at which growers would likely make management decisions. The day of year (DOY) at which these events occurred for each species was calculated using a standard interpolation method (Figure 2). These estimated emergence times are shown in Table 1, which can be used to rank the phenological events relative to other weeds and relative to ornamental plants and insect pests.
Table 1. Estimated day of year when weeds reached 25, 50, and 80% emergence in studies at Wooster, OH in 2003.
| Emergence Percentage | |||
| Weed Species | 25 | 50 | 80 |
| ----------------Day of year 2003-------------------- | |||
| common groundsel | 152 | 185 | 218 |
| common mallow | - | 125 | 208 |
| dandelion | 140 | 152 | 158 |
| hairy galinsoga | 121 | 135 | 157 |
| common purslane | 158 | 190 | 205 |
| velvetleaf | 130 | 162 | 188 |
The data in this table will be incorporated into the biological calendar for all recorded pests and ornamental plants. Average rankings using a minimum of 5 years of data will be used to develop the calendar. An updated degree-day calculator is now available on the web (http://www.oardc.ohio-state.edu/gdd/). This will be linked to the biological calendar so that users can enter their zip code and obtain daily information about the progression of degree days as well as the progression of weed emergence and pest development.
Studies in 2004 will focus on application of control measures at different times corresponding to different percentages of weed emergence. This will allow us to determine what emergence percentage to target for different species, which may vary with control measures and cropping system.
Figure 1. Cumulative emergence percentage of six weeds during the 2003 growing season.
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