In many areas of the US, fresh locally grown berries are not available during the winter. With this in mind, a research study comprised of three experiments was conducted focused on cultivar selection for berry yield, number, sweetness and phytonutrient content. Using a capillary mat system with under bench heating within a double-layer polyethylene greenhouse, strawberries were grown in the Great Plains Region of the US during the winter. During experiment 1, 12 cultivars were grown; berries were weighed, counted and analyzed digitalcommons.unl.edu P a p a r o z z i e t a l . i n S c i e n t i a H o r t i c u l t u r a e 2 2 7 ( 2 0 1 8 ) 2 for sugars and phytonutrients. "Albion" plants produced a high number/mass of berries, had relatively high sugar content but a lower level of phytonutrients when compared to other cultivars. Sugar and phytonutrients concentrations overlapped across cultivars and thus, one cultivar could not be statistically singled out as best. As all cultivars flowered and fruited, two additional 8-month-long experiments were conducted. It took only 7 weeks from potting of dormant crowns for most cultivars to produce fruit. Certain cultivars fruited more successfully during certain months than others, but this was not associated with response time.

The American Society for Horticultural Science (Alexandria, VA) and Longwood Gardens (Kennett Square, PA) engaged FleishmanHillard (FH, Washington, DC), a nationally recognized communications and marketing firm, to conduct research with internal and external audiences to determine the public perception of horticulture and careers in horticulture. Through stakeholder focus groups and general public online and phone surveys, the importance of horticulture, career perceptions, and the need for the promotion of horticulture were examined. Students, faculty, industry, and administrators in horticulture have a broad understanding of the field, much more than the public, especially young, ethnically diverse, and lower income participants. Although lack of public awareness is one of the biggest challenges in horticulture, it is also its greatest opportunity. Sixty-five percent of all phone survey participants as compared with 41% of 18–24 years old revealed a general awareness of the word horticulture. General public found agreement (48% to 59%) with four statements about the essential, universal, and invaluable worth of horticulture; however, strong agreement was less, ranging from 26% to 46%. Only 26% of respondents felt strong agreement with the statement, “Horticulture is a diverse area of study, and it offers viable, fulfilling, and respected career paths that I would recommend to others.” The research found strong stakeholder support for a national promotion of horticulture.

Often, the goal of plant science experiments is to model plant response as a function of quantitative treatment factors, such as the amount of nutrient applied. As the number of factors increases, modeling the response becomes increasingly challenging, especially since the resources available for such experiments are usually severely limited. Typical methods of analysis, notably second-order response surface regression, often fail to accurately explain the data. Alternatives such as non-linear models and segmented regression have been used successfully with two-factor experiments (Landes, et. aI, 1999). This paper extends previous work to three-and-more factor experiments. Models are assessed to explain the relationship between the levels of nutrients applied and leaf, root, and shoot responses of Poinsettias from an experiment conducted by horticultural researchers at the University of Nebraska-Lincoln. These data illustrate problems that are representative of those that plant researchers typically face. Multiple regression using the Hoed function proved to be especially useful. These analyses suggest a feasible approach to design of experiments suitable for a wide variety of plant science applications with multiple factors and limited resources.

105Plant scientists are interested in measuring plant response to quantitative treatment factors, e.g. amount of nutrient applied. Response surface methods are often used for experiments with multiple quantitative factors. However, in many plant nutrition studies, second-order response surface models result in unacceptable lack of fit. This paper explores multi-factor nonlinear models as an alternative. We have developed multi-factor extensions of Mitscherlich and Gompertz models, and fit them to data from experiments conducted at the University of Nebraska-Lincoln Horticulture department. These data are typical of experiments for which conventional response surface models perform poorly. We propose design selection strategies to facilitate economical multi-factor experiments when second-order response surface models are unlikely to fit.

ABSTRACT:Research investigating dose-response relationship is common in agricultural science. Animal response to drug dose and plant response to amount of irrigation, pesticide, or fertilizer are familiar examples. This paper is motivated by plant nutrition research in horticulture. Plant response to level of nutrient applied is typically sigmoidal, i.e. no response at very low levels, observable response at mid-levels, point-of-diminishing returns and plateau at high levels. Plant scientists need accurate estimates of these response relationships 1) to determine lower threshold below which plants show deficiency symptoms and 2) to determine upper point-of-diminishing returns, above which excessive doses are economically and environmentally costly. Landes, at al. (1999 andOlson et al. (2001) did initial work identifying potentially useful models. Paparozzi, et al. (2005) investigated dose (micro-and macro-nutrient) response (elemental leaf and stem concentration) relationships in Poinsettia. They found that 1) nutrients must be considered as a system, hence multifactor experiments are essential, 2) resources are limited, meaning that experiments must use response-surface principles, and 3) nutrient-response relationships are rarely modeled adequately by 2 nd order polynomial regression models, so standard response surface methods are inadequate. This paper presents models and designs that address these requirements and a simulation study to assess and compare the small-sample behavior of these models and designs.