Teaching reading through imaginative play situations in grades one and two

Thesis (Ed.M.)--Boston Universit

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

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

First experimental constraints on the disformally coupled Galileon model

The Galileon model is a modified gravity model that can explain the late-time accelerated expansion of the Universe. In a previous work, we derived experimental constraints on the Galileon model with no explicit coupling to matter and showed that this model agrees with the most recent cosmological data. In the context of braneworld constructions or massive gravity, the Galileon model exhibits a disformal coupling to matter, which we study in this paper. After comparing our constraints on the uncoupled model with recent studies, we extend the analysis framework to the disformally coupled Galileon model and derive the first experimental constraints on that coupling, using precise measurements of cosmological distances and the growth rate of cosmic structures. In the uncoupled case, with updated data, we still observe a low tension between the constraints set by growth data and those from distances. In the disformally coupled Galileon model, we obtain better agreement with data and favour a non-zero disformal coupling to matter at the $2.5\sigma$ level. This gives an interesting hint of the possible braneworld origin of Galileon theory.Comment: 9 pages, 6 figures, updated versio

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

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

The Proteus Navier-Stokes code

An effort is currently underway at NASA Lewis to develop two- and three-dimensional Navier-Stokes codes, called Proteus, for aerospace propulsion applications. The emphasis in the development of Proteus is not algorithm development or research on numerical methods, but rather the development of the code itself. The objective is to develop codes that are user-oriented, easily-modified, and well-documented. Well-proven, state-of-the-art solution algorithms are being used. Code readability, documentation (both internal and external), and validation are being emphasized. This paper is a status report on the Proteus development effort. The analysis and solution procedure are described briefly, and the various features in the code are summarized. The results from some of the validation cases that have been run are presented for both the two- and three-dimensional codes

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

Evaluating Cropland N2O Emissions and Fertilizer Plant Greenhouse Gas Emissions With Airborne Observations

Agricultural activity is a significant source of greenhouse gas emissions. The fertilizer production process emits N2O, CO2, and CH4, and fertilized croplands emit N2O. We present continuous airborne observations of these trace gases in the Lower Mississippi River Basin to quantify emissions from both fertilizer plants and croplands during the early growing season. Observed hourly emission rates from two fertilizer plants are compared with reported inventory values, showing agreement for N2O and CO2 emissions but large underestimation in reported CH4 emissions by up to a factor of 100. These CH4 emissions are consistent with loss rates of 0.6–1.2%. We quantify regional emission fluxes (100 km) of N2O using the airborne mass balance technique, a first application for N2O, and explore linkages to controlling processes. Finally, we demonstrate the ability to use airborne measurements to distinguish N2O emission differences between neighboring fields, determining we can distinguish different emission behaviors of regions on the order of 2.5 km2 with emissions differences of approximately 0.026 μmol m−2 s−1. This suggests airborne approaches such as outlined here could be used to evaluate the impact of different agricultural practices at critical field‐size spatial scales.Key PointsReported N2O and CO2 emissions from fertilizer plants agree with observations, but CH4 is underestimated by orders of magnitudeWe demonstrate mass balance quantification of N2O emissions from agriculture at 10–100 km scalesAirborne measurements can observe and quantify N2O emission differences between agricultural fields of ∼2.5 km2Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156438/3/jgrd56401.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156438/2/jgrd5640-sup-0001-Figure_SI-S01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156438/1/jgrd56401_am.pd