Management and Modeling of Winter-time Basil Cultivars Grown with a Cap MAT System

Basil (Ocimum basilicum) is a high value crop, currently grown in the field and greenhouses in Nebraska. Winter-time, greenhouse studies were conducted during 2015 and 2016, focusing on cultivars of basil grown on a Cap MAT II® system with various levels of fertilizer application. The goal was to select high value cultivars that could be grown in Nebraska greenhouses. The studies used water content, electrical conductivity, photosynthetically active radiation (PAR), and relative humidity, air and soil media temperature sensors. Greenhouse systems can be very complex, even though controlled by mechanical heating and cooling. Uncertain or ambiguous environmental and plant growth factors can occur, where growers need to plan, adapt, and react appropriately. Plant harvest weights and electronic sensor data was recorded over time and used for training and internally validating fuzzy logic inference and classification models. Studies showed that GENFIS2 ‘subtractive clustering’ of data, prior to ANFIS training, resulted in good correlations for predicted growth (R2 \u3e 0.85), with small numbers of effective rules and membership functions. Cross-validation and internal validation studies also showed good correlations (R2 \u3e 0.85). Decisions on basil cultivar selection and forecasting as to how quickly a basil crop will reach marketable size will help growers to know when to harvest, for optimal yield and predictable quantity of essential oils. If one can predict reliably how much essential oil will be produced, then the methods and resultant products can be proposed for USP or FDA approval. Currently, most plant medicinal and herbal oils and other supplements vary too widely in composition for approval. The use of fuzzy set theory could be a useful mathematical tool for plant and horticultural production studies

Management and Modeling of Winter-time Basil Cultivars Grown with a Cap MAT System

Basil (Ocimum basilicum) is a high value crop, currently grown in the field and greenhouses in Nebraska. Winter-time, greenhouse studies were conducted during 2015 and 2016, focusing on cultivars of basil grown on a Cap MAT II® system with various levels of fertilizer application. The goal was to select high value cultivars that could be grown in Nebraska greenhouses. The studies used water content, electrical conductivity, photosynthetically active radiation (PAR), and relative humidity, air and soil media temperature sensors. Greenhouse systems can be very complex, even though controlled by mechanical heating and cooling. Uncertain or ambiguous environmental and plant growth factors can occur, where growers need to plan, adapt, and react appropriately. Plant harvest weights and electronic sensor data was recorded over time and used for training and internally validating fuzzy logic inference and classification models. Studies showed that GENFIS2 ‘subtractive clustering’ of data, prior to ANFIS training, resulted in good correlations for predicted growth (R2 \u3e 0.85), with small numbers of effective rules and membership functions. Cross-validation and internal validation studies also showed good correlations (R2 \u3e 0.85). Decisions on basil cultivar selection and forecasting as to how quickly a basil crop will reach marketable size will help growers to know when to harvest, for optimal yield and predictable quantity of essential oils. If one can predict reliably how much essential oil will be produced, then the methods and resultant products can be proposed for USP or FDA approval. Currently, most plant medicinal and herbal oils and other supplements vary too widely in composition for approval. The use of fuzzy set theory could be a useful mathematical tool for plant and horticultural production studies

AFTER FURTHER REVIEW: AN UPDATE ON MODELING AND DESIGN STRATEGIES FOR AGRICULTURAL DOSE-RESPONSE EXPERIMENTS

Research investigating dose-response relationships is common in agricultural science. This paper is an expansion on previous work by Guo, et al. (2006) 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 for many reasons, including determining the lower threshold below which plants show deficiency symptoms and the point of diminishing returns, above which excessive doses are economically and environmentally costly. Guo et al. presented models and designs that address these requirements and a simulation study to assess and compare the small-sample behavior of these models and designs. This paper expands on that simulation study. In addition, a simulation study based procedure for exploring designs for experimental scenarios fitting this description is presented. This simulation study approach utilizes simulation based fit statistics in conjunction with various lack-of-fit plots to produce a design robust to multiple candidate models

A COMPARISON OF MODELS AND DESIGNS FOR EXPERIMENTS WITH NONLINEAR DOSE-RESPONSE RELATIONSHIPS

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 and Olson 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 2nd 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

MODEL BUILDING IN MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

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

NONLINEAR MODELS FOR MULTI-FACTOR PLANT NUTRITION EXPERIMENTS

Plant 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

Storage and breakdown of starch aid \u3cem\u3eP. parviflorus\u3c/em\u3e in leaf re-greening after nitrogen deficiency

Plectranthus parviflorus, common Swedish ivy does not lose leaves when it is deprived of nitrogen. Instead this plant retains its yellow leaves and upon reintroduction of nitrogen will re-green and start to grow. In two experiments, rooted cuttings of common Swedish ivy were grown with (150 ppm N) and without nitrogen for 3 weeks. After some plants were sampled the others were either switched or kept at 0 or 150 ppm N and allowed to grow for another 3 weeks. After another sampling, plants were again switched or kept at 0 or 150 ppm N for a final 3 weeks. At each harvest, leaves were tested for starch, sampled for microscopy and then dried and weighed for soluble carbohydrate extraction. Data collected indicates that yellow leaves store and breakdown starch into soluble carbohydrates (specifically reducing sugars) in order to keep leaves from senescing. When nitrogen is re-supplied to these plants, leaves re-green and the plant continues to grow. We propose that common Swedish ivy’s ability to store and breakdown starch aids in the process of leaf re-greening

Strawberry cultivars vary in productivity, sugars and phytonutrient content when grown in a greenhouse during the winter

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 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. For example,” “Albion”, “Chandler”, “Darselect”, “Evie-2” and “Seascape” plants consistently produced fruit October to early January while “AC Wendy”, “Cavendish”, “Honeoye” and “Strawberry Festival” plants mainly produced berries in March/April. Summed over experiment 2, “Albion”, “ Cavendish”, “Chandler”, “ Evie-2”, “Portola” and “Seascape” plants produced the greatest mass of berries. “AC Wendy” and “Darselect” berries contained some of the highest levels of sugars while berries from “Chandler”, “Darselect”, “Evie-2”, “Seascape” and “Strawberry Festival” had some of the highest phytonutrient values. In the third experiment, of the 8 selected cultivars, “Evie-2”, “Evie−2+” and “Portola” plants had the highest total yield and average berry mass/plant. “Seascape” and” Chandler” plants were second in total production. Glucose, fructose and sucrose levels varied across cultivars with “Chandler” and “Seascape” berries possessing the lowest level of total sugars. Phytonutrient values varied among cultivars with some having better flavonoids (“Seascape”), phenols (“Seascape” and “Chandler”) and ant oxidant capacity (“Seascape”, “Evie-2” and “Cavendish”). Measurement of soluble solids concentration varied by week among the cultivars with “Seascape”, “Seascape+”, and “Albion” berries possessing higher levels than other cultivars such as “Cavendish”. Overall, under these winter greenhouse conditions using capillary mat fertigation and an under-bench heat delivery system, strawberries were successfully produced for the off-season market

Comparison of Winter Strawberry Production in a Commercial Heated High Tunnel versus a University Greenhouse

For the past 4 years, the University of Nebraska strawberry team has worked to develop low cost, sustainable methods for farmers and growers to produce strawberries in a double polyethylene greenhouse during the winter. This past year, this growing system was adapted to become a commercial grower’s heated high tunnel for the winter/spring of 2013-14. The idea was to scale up to a farm-size demonstration and compare it to the university greenhouse production system with a goal to expand marketing opportunities for strawberries into the winter season