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

Project Overview

Project Type: Research and Education
Funds awarded in 2012: $203,610.00
Projected End Date: 12/31/2015
Region: Western
State: Oregon
Principal Investigator:
Nick Andrews
Oregon State University

Annual Reports


  • 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

    Proposal abstract:

    Our goal is to improve the economic and environmental resiliency of vegetable growers by increasing their use of phenology (degree day or DD) modeling when making crop management decisions. We will develop a collaborative online vegetable crop planning system for vegetable growers. Researcher and farmer generated DD models will be integrated into a new vegetable crop management website to be hosted by the OSU Integrated Plant Protection Center (IPPC). Farm decisions supported by the system will include:

    1) variety selection and scheduling of successive planting and harvest dates,
    2) predicting whether a weed seedling will produce viable seed before vegetable harvest, and
    3) determining whether crop nitrogen (N) uptake matches N mineralization from non-fertilizer sources.

    We expect the system to include at least 40 researcher and 10 grower developed vegetable variety models, 6 weed models and the N model when launched, with potential for continued expansion over time.

    During development of the online system, collaborating growers will select species to be modeled and identify high priority features for the modeling system. Growers and extension faculty will work together to develop the first farmer generated DD models and develop a simple online system to facilitate ongoing DD model development by farmers and agricultural professionals. At least 60 farmers and agricultural professionals will be trained to develop DD models and use the online system during two intensive workshops, and more than 500 farmers and agricultural professionals will be introduced to the system at grower conferences and professional meetings.

    Vegetable growers currently use days to maturity as published in seed catalogs to plan cropping schedules and manage crops. These harvest date predictions from seed catalogs vary widely depending on location and climate. DD models dramatically improve predictions of crop and weed maturity and the N cycle, but they are not readily available to most vegetable growers. Researchers will determine model parameters such as temperature thresholds and create an online system which will enable agricultural professionals and farmers to develop and share DD models for a wide variety of vegetable varieties.

    The initial focus will be with organic and conventional fresh and processed vegetable growers supplying wholesale and direct markets in W. Oregon. Over $191 million in vegetables were produced on about 83,500 acres in Oregon in 2010 (NASS). The OSU IPPC web site serves the entire U.S., so we anticipate adoption of this new system across the Western region and potentially in other regions.

    Project objectives from proposal:

    1. Participatory research and system development. A team of innovative growers and agricultural professionals have been identified as who will guide the project. They will learn how to use the existing online phenology modeling system hosted by the OSU-IPPC at http://uspest.org/wea. This team will identify key system features that will support decision making on vegetable farms. Input from producers and other agricultural professionals will be used to develop an online vegetable crop management system that is useful to organic and conventional fresh market and processed vegetable producers and seed companies. Collaborators will help to determine priority species, varieties and processes for DD modeling. The system will be designed to serve the needs of farmers supplying both wholesale and direct to consumer markets (e.g. community supported agriculture or CSA, farmer’s markets, farm stands and restaurant sales). These groups will provide valuable insights to help make the system useful to their farm businesses.

    The leadership team and other interested growers will learn to develop crop GDD models for specific vegetable varieties to predict GDD to harvest and other relevant growth stages. Extension faculty active in the project will work with these producers to develop the first grower-developed GDD models at participating farms that can be integrated into the system. This on-farm research will inform development of the online collaborative DD modeling system. Collaborating growers and growers attending intensive workshops will be taught to develop and share participatory GDD models and use the online system in their businesses. Online instructions will be created and made available so that new growers can start to use the system after project completion.

    2. Online degree day planning tool. We will develop an interactive website that will support vegetable crop planning and management using DD models. Features of the site will be prioritized through the participatory process. The website will link to a network of weather stations and will feature DD models that are relevant to vegetable producers. A restricted photoperiod parameter will be incorporated into GDD models for day-length sensitive species. DD models will be validated by participating researchers and producers and launched online during the course of the project. Project PIs will write a GDD manual that explains how users can develop their own GDD models. Participating growers will review the manual. The system will include record keeping templates that ensure growers collect needed information for developing their own GDD models. Output from the models will be designed to facilitate crop planning and management by vegetable growers. Calendar and tabular format options will be explored with producers to select the most practical and useful format for their main uses of the system. Online instructions will support ongoing collaborative development and use of GDD models by and for vegetable producers and other agricultural professionals. The website will serve as a long-term outreach and education tool that will improve farm profitability and sustainability. It will be designed for ease of use by growers with a moderate level of experience using the internet.

    3. Phenology model development. We will develop new DD models for priority vegetable varieties and weed species and aspects of the N-cycle as determined by the participatory process. Discussions during proposal development indicate specific interest in modeling pumpkin, squash, pepper, brassica, lettuce, sweet corn, pickling cucumber, green bean and cover crop varieties, some weed species, and a DD-driven N management tool. We will conduct literature reviews, adapt existing data and conduct necessary trials to develop the models. Major growth parameters (e.g. lower and upper temperature thresholds) for related crop groups will be identified and made available for use by growers and agricultural professionals developing their own GDD models. Vegetable variety trials at OSU will provide data for new GDD models that will be incorporated into the online system. New weed species models will be developed to predict GDDs required for viable seed production and whether weed seeds will be produced before the harvest date predicted by crop GDD models. We will develop a DD-driven N management tool that predicts the timing of crop N uptake, rotation effects on N fertilizer requirements and forecasts the best times for soil nitrate sampling.

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.