- Vegetables: beans, broccoli, cabbages, carrots, cucurbits, parsnips, peppers, sweet corn, tomatoes
- Crop Production: crop rotation, continuous cropping, cover crops, double cropping, multiple cropping, nutrient cycling, organic fertilizers
- Education and Training: decision support system, extension, on-farm/ranch research, participatory research
- Farm Business Management: risk management
- Pest Management: cultural control, integrated pest management, physical control, weather monitoring, weed ecology
- Production Systems: organic agriculture
- Soil Management: nutrient mineralization
Our goal with this project is to develop an online crop management tool that alllows vegetable growers to time farming and marketing activities using degree-days. Initial conversations with grower collaborators indicated that vegetable variety models and weed models would be useful. Some were also interested in the role of thermal time in nitrogen mineralization. In this project our emphasis was on vegetable models and usability of the Croptime Calculator.
We used a collaborative process to develop an online vegetable crop management interface (the Croptime website) that is easy to use. The Croptime Calculator has been formatted to work on desktop computers, tablets and smart phones. Model output can also be copied into Excel or Word files for farm records.
From 2013-2015 we collected enough crop development data to develop at least 60 vegetable models from 15 different crops and three weed models. By March, 2016 we had completed 11 new vegetable models (broccoli and cucumber) and three new weed models. By March 2017 we expect to have at least 50 vegetable models online.
In 2015 and 2016 we have conducted extensive outreach to students, farmers and other agricultural professionals. Interest in the project and interest in using Croptime models appears to be very strong among farmers, seed companies, other agricultural professionals and students.
- Participatory research and system development.
Before this grant, the OSU phenology modeling system was primarily a pest management decision tool. We worked closely with fresh and processed vegetable growers and seed company staff using organic and conventional methods to identify top priority weeds, vegetables and varieties to model.
These collaborators helped us identify website features that would make Croptime more useful as a decision-tool for vegetable growers. They also provided input as we developed the Croptime Growth State Guide (adapted from BBCH). Interested farmers, students and agricultural professionals also volunteered to beta-test the Croptime website.
- Online degree day planning tool.
Coop incorporated high priority features into the Croptime Calculator. He is also working with Andrews, Heinrich and Peachey to post new vegetable and weed models online and to document modeling results. With supplemental funding he has developed 7-month degree-day forecasts derived from NOAA climate models.
- Phenology model development.
Crops prioritized by Croptime collaborators include snap beans, broccoli, cabbage, carrot, cauliflower, cucumber, kale, lettuce, parsnip, sweet pepper, spinach, summer squash, sweet corn, tomato and winter squash. We have collected five or more data sets (crop development data at one site and planting date) for 68 vegetable varieties. This is sufficient to develop degree-day models for 50-68 variety specific degree day models.
We have introduced degree-day models that predict time to germinable seed production for three weed species: hairy nightshade (Solanum physalifolium), lambsquarter (Chenopodium album) and redroot pigweed (Amaranthus retroflexus). We are also publishing information about the influence of seasonal temperatures on nitrogen cycles on organic farms. Weed species, crops and varieties were prioritized by growers using a participatory process.
An OSU Extension Publication that discusses nitrogen mineralization and the influence of temperature is in preparation: Plant-available nitrogen supplied by mineralization: Vegetable cropping systems (western Oregon). Sullivan (author) has identified a model that predicts nitrogen mineralization from total soil N measurements, estimated decomposition rates and mean soil temperatures.