Identifying Critical Criteria to Develop a Decision-making Model for Implementing Late Water Floods in Cranberry Production

Project Overview

Project Type: Research Only
Funds awarded in 2021: $189,340.00
Projected End Date: 11/30/2024
Grant Recipient: University of Massachusetts
Region: Northeast
State: Massachusetts
Project Leader:
Dr. Leela Uppala
University of Massachusetts


  • Fruits: berries (cranberries)


  • Education and Training: decision support system, on-farm/ranch research
  • Pest Management: integrated pest management

    Proposal abstract:

            A major and persistent challenge affecting Northeastern (NE) cranberry production is fruit rot disease. Fruit deliveries beyond 12% rot are heavily penalized, and deliveries >20% rot are rejected outright by handlers (processing industry). When Massachusetts (MA) cranberry acreage was largely planted in native cultivars (‘Early Black’ and ‘Howes’) and effective chemical control options were not available, growers relied on cultural control by late water (LW) flooding. LW is an affordable 1-month spring flooding practice, recommended once in 3 years, and is known to reduce fruit rot, insects, weeds and enhance fruit quality.

           After the release of high-yielding hybrid/newer cultivars, MA cranberry acreage shifted towards these newer cultivars, which were more susceptible to fruit rot compared to native cultivars. The introduction of effective broad-spectrum protectant fungicides (mancozebs/EBDCs-ethylene bisdithiocarbamate from 1960s and chlorothalonils from 1980s), largely replaced cultural controls. Anecdotal association of LW to occasional yield losses also contributed to this shift. To produce marketable fruit, growers often apply up to five fungicides per season. Interest in LW is resurging due to the loss of key pesticides due to pesticide restrictions by international markets, rigorous fruit quality demands by cranberry handlers, pest outbreaks and historically low cranberry prices. Based on recent stakeholder feedback, >75% of decision-makers are aware of LW benefits but did not know how to decide when and/or where LW could be most beneficial. No previous research has determined which factors contribute to LW outcomes. In our extension meetings, >65% of growers expressed interest in implementing LW given the proper guidance and confidence.

            We hypothesize that biotic and abiotic factors affect LW results and that these factors can be quantified and evaluated. We propose to identify the critical criteria that drive the results of LW and develop a web-based, data-driven, decision-making model (DMM) that produces grower-friendly output. For this, our multi-disciplinary team will work collaboratively with growers for three years on 15 paired sites (unflooded versus LW, 5 sites per year) and collect data for explanatory (e.g., historic and present crop data, plant carbohydrate status, water quality) and response variables (e.g., yield, fruit quality). We will analyze data using crop simulation modeling and identify the critical factors that are associated with successful LW, their relative contribution and interrelationships, and develop a LW-DMM. A LW-DMM would improve grower confidence and facilitate increased adoption of this under-utilized practice. Farm profitability will be enhanced through sustainable production, reduced pesticide applications, reliable fruit quality and reduced penalties/load rejections.

            We will disseminate results to the grower-industry-scientist network through education outreach with fact sheets, peer-reviewed publications, digital resources (e.g., webinars, ScholarWorks, social media), and extension meetings. We will gather feedback from the Advisory Committee (AC) and early adopters on the initial model and integrate improvements.

    Project objectives from proposal:

    To evaluate the relative importance of critical criteria (e.g., field characteristics, water quality, crop status, pest and yield history, and environmental conditions) that contribute to the short- and long-term outcome of late water (LW) floods and to develop a web-based, data-driven, decision-making model (DMM) that will generate grower-friendly outputs that promote appropriate LW use. A LW-DMM would improve cranberry grower confidence and facilitate increased adoption of this under-utilized practice. Farm profitability will be enhanced through sustainable cranberry production, increased revenue, and reduced pesticide applications. This could positively impact >420 farms in the Northeast (NE), affecting >16,000 acres of cranberries.

    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.