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

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

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

A multiple imputation strategy for sequential multiple assignment randomized trials

Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient‐specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well‐known SMARTs to date. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108622/1/sim6223-sup-0001-SupInfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108622/2/sim6223.pd

Pulse of the seafloor: Tidal triggering of microearthquakes at 9°50′N East Pacific Rise

Unequivocal evidence of tidal triggering is observed for microearthquakes (−0.4 to 2.0 M_L) recorded between October 2003 to April 2004 near 9°50′N on the East Pacific Rise (EPR). Although semidiurnal tidal stress changes are small (99.9%) nonrandom temporal distribution, with events occurring preferentially near times of peak extension. Due to the proximity of this site to an ocean tidal node, where changes in sea surface height are minimal, periodic stress changes are dominated by the solid Earth tide. In contrast, previous studies on the Juan de Fuca Ridge have shown microearthquake triggering to be a response to seafloor unloading during times of low ocean tide. The modulation of 9°50′N microearthquakes by small-amplitude periodic stresses is consistent with earthquake nucleation within a high stressing rate environment that is maintained near a critical state of failure by on-axis magmatic and hydrothermal processes

Military spending and economic growth in China: a regime-switching analysis

This article has been made available through the Brunel Open Access Publishing Fund.This article investigates the impact of military spending changes on economic growth in China over the period 1953 to 2010. Using two-state Markov-switching specifications, the results suggest that the relationship between military spending changes and economic growth is state dependent. Specifically, the results show that military spending changes affect the economic growth negatively during a slower growth-higher variance state, while positively within a faster growth-lower variance one. It is also demonstrated that military spending changes contain information about the growth transition probabilities. As a policy tool, the results indicate that increases in military spending can be detrimental to growth during slower growth-higher growth volatility periods. © 2014 © 2014 The Author(s). Published by Taylor & Francis