A Collaborative Phenology Modeling System to Enhance Crop Management on Vegetable Farms

A Collaborative Phenology Modeling System to Enhance Crop Management on Vegetable Farms

Summary

In March 2013 we held the first meeting of all collaborators. Participants learned about using degree-days (DDs) to make farm and business decisions and were introduced to using online DD modeling systems. CROPTIME was chosen as a name for the website and decision-support system we are developing with this grant. A draft CROPTIME website was launched for use during the development phase of the project (2013-2014). The educational “front page” (http://smallfarms.oregonstate.edu/croptime) has no public link yet and is only available to collaborators and others who know the url. The modeling tools are housed at OSU’s Integrated Plant Protection Center (http://uspest.org/cgi-bin/ddmodel.us?mdt=veg). We identified crops and weeds to be modeled with collaborators and drafted growth stage descriptions and record keeping templates for all crops of initial interest. We also drafted two online fact sheets that introduce DDs as a farm management tool and explain how to develop DD models using CROPTIME. We collected extensive field data to support development of vegetable and weed DD models and are starting to interpret the data and develop the first models.

Objectives/Performance Targets

1. Participatory research and system development.

1. Identify crops and varieties to model.
2. Identify weeds and N management parameters to model.
3. Gather input from growers on CROPTIME website functions.
4. Develop Extension expertise in DD modeling.
5. Conduct workshops to teach people how to use CROPTIME and develop models.
6. Develop online instructions to teach CROPTIME users about DD models and how to use the website. Develop the site to be user-friendly.

2. Online degree day planning tool.

1. Develop website with features prioritized by growers and Extension faculty.
2. Develop a restricted photo-period parameter for day-length sensitive crops and weeds.
3. Develop growth stage descriptions and record keeping templates for crops.

3. Phenology model development.

1. Identify lower and upper development thresholds for the crops and weeds modeled.
2. Determine DDs for at least 50 vegetable variety models, 6 weed models and a DD sensitive nitrogen management tool.
3. Investigate tomato and pepper development with and without black plastic mulch.

Accomplishments/Milestones

1.  Participatory research and system development.

1. Identify crops and varieties to model. In March 2013 we hosted a day-long CROPTIME seminar for all collaborators where we identified crops and varieties of common interest. Andrews, Myers, Coop and Noordijk prioritized these crops with additional feedback from other local farmers. We identified 78 varieties from 16 different crops to model. Crops are snap beans, broccoli, cabbage, carrot, cauliflower, cucumber, kale, head lettuce, parsnip, sweet pepper, pumpkin, summer squash, sweet corn, tomato and winter squash. The full list of varieties is at: Crop and Variety List. In 2014 we plan to collect DD data from Brussels sprouts as well.
2. Identify weeds and N management parameters to model. Participants selected pigweed, nightshade, common lambsquarters, crabgrass, chickweed and smartweed to model. Growers were most interested in identifying best dates for applying N in sweet corn, brassicas, pepper, beans, winter squash, celeriac and onions. The nitrogen management tool will be further developed in 2014-15 when our crop modeling output is formatted.
3. Gather input from growers on CROPTIME website functions. During the March seminar we surveyed growers about key functions for the website. Growers and vegetable processors would like to enter multiple start dates for different crops and seed company representatives would also like to be able to enter multiple varieties. They want CROPTIME to be able to model transplanted and direct-seeded crops, as well as day-length sensitive crops and weeds. They would also like to predict crop development on black plastic mulch (especially peppers and tomatoes). They would like to be able to set up a CROPTIME account that saves personal settings, varieties and start dates. They would also like to have options other than 30-year average for long-term forecasts and to be able to display average first and last frost dates. They were interested in being able to “dial up” or “dial down” DDs to maturity from known standard varieties (i.e. based on relative days to maturity reported in seed catalogs).
4. Develop Extension expertise in DD modeling. Throughout the project, Coop is teaching interested Extension collaborators how to develop DD models.
5. Conduct workshops to teach people how to use CROPTIME and develop models. Participants rated the quality of our 2013 seminar at 4.8/5. Insight from this workshop will help us enhance future public workshops planned for the outreach phase of this project (starting in 2015).
6. Develop online instructions to teach CROPTIME users about DD models and how to use the website. Develop the site to be user friendly. Andrews, Coop and Myers drafted two online fact sheets: “An introduction to vegetable degree-day models” and “How to develop a vegetable degree-day model using CROPTIME”. They are at http://smallfarms.oregonstate.edu/croptime-fact-sheets. With funding from Clackamas County (see impacts and contributions), we partnered with OSU Extension and Experiment Station Communications (EESC) to survey participants about preferred learning methods. Participants rated online fact sheets, ask an expert, informational “flash cards” and brochures, and pop-up instruction bubbles built into the CROPTIME website as their favored methods for learning. Other popular methods include a multi-media user’s manual (text and video), one-on-one training, face-to-face workshops and short instructional videos.

2. Online degree day planning tool.

1. Develop website with features prioritized by growers and Extension faculty. Coop developed a draft CROPTIME calculator in early 2013 which we are using in the project: http://uspest.org/cgi-bin/ddmodel.us?mdt=veg. With funding from Clackamas County, we are consulting with a Microsoft usability engineer to conduct usability testing. Early “cognitive walk-throughs” of the site showed it was difficult for users to select local weather stations. To improve usability Coop developed a google maps interface (see link above). More formal usability tests are planned for 2014-2015 when we have CROPTIME models to work with.
2. Develop a restricted photo period parameter for day-length sensitive crops and weeds. In 2013 Coop found a formula that can calculate day-length at any farm or weather station (using latitude). In 2014-15 we plan to incorporate this formula into model output and format simple day-length alerts for sensitive species.
3. Develop growth stage descriptions and record keeping templates for crops. In 2013, we drafted growth stage descriptions and record keeping templates for 17 crops (those mentioned above plus Brussels sprout): http://smallfarms.oregonstate.edu/croptime-vegetable-templates. 

3. Phenology model development.

1. Identify lower and upper development thresholds for the crops and weeds modeled. Our goal with this grant is to develop DD models for 50 vegetable varieties and six weed species. In 2013, we collected field data for 78 vegetable varieties. Data were from an average of three sites per variety (total of 223 observation sites). Data were also collected for all six weed species we are modeling, with about three sites per species. We are starting to use this data to determine lower and upper thresholds for the crops and weeds we are working with.
2. Determine DDs for at least 50 vegetable variety models, 6 weed models and a DD sensitive nitrogen management tool. Data collected in 2013-14 will be used to calculate average DDs to critical growth stages (i.e. maturity, beginning of period of rapid N uptake) when thresholds have been identified.
3. Investigate tomato and pepper development with and without black plastic mulch. In response to grower interest, we conducted a side experiment at the North Willamette Research & Extension Center to observe development of three sweet pepper varieties and five tomato varieties both with and without black plastic mulch. Plots were replicated three times and we recorded crop development, ambient air temperature and soil temperature at 2”.

Impacts and Contributions/Outcomes

At the March seminar we evaluated how much collaborating growers learned. They rated their knowledge before and after the workshop on a scale of 1-7. On average, growers rated their knowledge at 3.0 (low end of medium) before the workshop, and 5.4 (high) after the workshop. Their knowledge of all topics increased. Topics included the list below; average knowledge ratings are shown as (before/after):

• Understand the purpose of DD models (4.4/6.0)
• Identify good online weather stations (2.3/5.3)
• Ability to run vegetable DD models (2.3/4.3)
• Use crop models to make decisions on your farm (3.1/5.3)
• Understand how weed DD models could benefit your farm (3.1/5.8)
• Understand how DDs can help with nitrogen management (2.6/4.9)

All respondents plan to use CROPTIME to manage their crops. They all also intend to develop DD models for their own varieties. Farmers expect to tell 40 other farmers about CROPTIME. Collaborators from seed company and vegetable processors work with an estimated 550 farms managing >60,000 acres. They expect to share CROPTIME information with most of these growers. They all plan to develop DD models for their own varieties. One seed company expects to use DDs in their catalog. Another company may use them, and one does not yet plan to.

We secured additional local funding in 2013 (Clackamas County) to do usability testing of the CROPTIME website and work with OSU-EESC to identify and develop the most effective learning materials. This funding was renewed in 2014 to launch a variety trial program at NWREC which will allow us to rapidly collect DD data for a lot of crops.

We plan to estimate additional project impacts and outcomes towards the end of the project.

Collaborators: