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Interest in cover crops is increasing but information is limited on integrating them into crop rotations especially in the relatively short growing season on the northern Great Plains. A 3-yr research project, initiated in 2009 near Mandan, North Dakota, USA, evaluated (1) what impact cover crops may have on subsequent cash crops yields and (2) whether cover crop mixtures are more productive and provide additional benefits compared to cover crop monocultures. The study evaluated 18 different cover crop monocultures and mixtures that were seeded in August following dry pea (Pisum sativum L.). The following year, spring wheat (Triticum aestivum L.), corn (Zea mays L.), soybean (Glycine max L.) and field pea were seeded into the different cover crop treatments and a non-treated control. A lack of timely precipitation in 2009 resulted in a low cover crop yield of 17 g m2 compared to 100 and 77 g m2 in 2008 and 2010, respectively. Subsequent cash crop yield was not affected by late-seeded cover crops. Cool-season cover crop monocultures were more productive than warm-season monocultures and some mixtures in 2008 and 2010. Relative yield total did not differ from one in any cover crop mixture suggesting that overyielding did not occur. Species selection rather than species diversity was the most important contributor to cover crop yield. Cover crops can be grown following short-season cash crops in the northern Great Plains, but precipitation timing and species selection are critical.
We propose the concept of the “Fish Revolution” to demarcate the dramatic increase in North Atlantic fisheries after AD 1500, which led to a 15-fold increase of cod (Gadus morhua) catch volumes and likely a tripling of fish protein to the European market. We consider three key questions: (1) What were the environmental parameters of the Fish Revolution? (2) What were the globalising effects of the Fish Revolution? (3) What were the consequences of the Fish Revolution for fishing communities? While these questions would have been considered unknowable a decade or two ago, methodological developments in marine environmental history and historical ecology have moved information about both supply and demand into the realm of the discernible. Although much research remains to be done, we conclude that this was a major event in the history of resource extraction from the sea, mediated by forces of climate change and globalisation, and is likely to provide a fruitful agenda for future multidisciplinary research.
Recent commercialization of auxin herbicide–based weed control systems has led to increased off-target exposure of susceptible cotton cultivars to auxin herbicides. Off-target deposition of dilute concentrations of auxin herbicides can occur on cotton at any stage of growth. Field experiments were conducted at two locations in Mississippi from 2014 to 2016 to assess the response of cotton at various growth stages after exposure to a sublethal 2,4-D concentration of 8.3 g ae ha−1. Herbicide applications occurred weekly from 0 to 14 weeks after emergence (WAE). Cotton exposure to 2,4-D at 2 to 9 WAE resulted in up to 64% visible injury, whereas 2,4-D exposure 5 to 6 WAE resulted in machine-harvested yield reductions of 18% to 21%. Cotton maturity was delayed after exposure 2 to 10 WAE, and height was increased from exposure 6 to 9 WAE due to decreased fruit set after exposure. Total hand-harvested yield was reduced from 2,4-D exposure 3, 5 to 8, and 13 WAE. Growth stage at time of exposure influenced the distribution of yield by node and position. Yield on lower and inner fruiting sites generally decreased from exposure, and yield partitioned to vegetative or aborted positions and upper fruiting sites increased. Reductions in gin turnout, micronaire, fiber length, fiber-length uniformity, and fiber elongation were observed after exposure at certain growth stages, but the overall effects on fiber properties were small. These results indicate that cotton is most sensitive to low concentrations of 2,4-D during late vegetative and squaring growth stages.
Item response theory (IRT) represents an alternative measurement approach to Classical test theory (CTT) that has been developed to address some of the key limitations in CTT. IRT utilizes a logistic function to jointly scale both person characteristics (e.g. ability) and task characteristics (e.g. difficulty) along a common metric, and is grounded upon the notion that different item sets should not result in different scaling solutions. This provides IRT with a number of advantages over CTT; namely that the performances of individuals who are administered different sets of tasks may still be justifiably compared. From this basis, the IRT approach has established its utility through its applications in such varied contexts as adaptive testing, cognitive diagnostic modeling, item difficulty modeling, and latent class analyses. The current chapter focuses on applied issues in measurement with IRT, with emphasis on its distinct advantages over traditional approaches.
In this chapter, the authors review concepts related to reliability and validity of measurement in the social sciences. Necessarily eschewing detailed reviews of statistical methods to examine reliability and validity of measurement, the authors focus on terse discussion on key concepts and elementary methods. In addition, given the immense literatures on topics the authors could only discuss in brief, the authors point to sources that provide helpful, focused treatments of undiscussed ideas and techniques. Authors conclude that, given reliability and validity of measurement is crucial to perform scientific research, social scientists ought to prioritize establishing evidence for reliability/validity of measurement for social scientists’ measures and experimental methods.
The chapter begins with a description of the historical roots of comparative research, followed by a description of its spectacular in the last 50 years. The core methodological issues of the field are described: Does an instrument that is administered in different groups or countries constitute and adequate measure of the underlying construct in each application? If so, can we compare scores across groups and countries. A taxonomy of equivalence (similarity of meaning) and bias (presence of nuisance factors) to be considered in comparative studies is described. Special emphasis is given to test adaptations a tool to provide culture-informed measures. The gap between the advanced statistical procedures available to analyze comparative data and the much less advanced level of our theories of cross-cultural similarities and differences is mentioned as a key challenge for the future.
The Internet was created in the mid-20th century as a communication tool for American scientists. Since then, it has grown into a tool that much of the world’s population uses on a daily basis for a wide variety of reasons ranging from social interaction, commerce, and to obtain information. Given its ubiquity, the amount of scholarship on the use of the Internet for conducting research has grown along with it. To date, thousands of books and journal articles include research conducted on the Internet using myriad research methodologies and theoretical perspectives. This chapter reviews the literature on Internet research, exploring questions such as: What is Internet research? What topics do social scientists study on the Internet? What are the different approaches for conducting research on the Internet? Ethical considerations for Internet research, suggestions for best practices in using the Internet for research, and recommendations for future research conclude this chapter.
Bayesian methods are becoming more popular in the social sciences because they offer solutions to problems that arise with classical methods, e.g., convergence issues and the inability to interpret the results probabilistically. However, Bayesian statistics remain controversial because they require specifying prior distributions that reflect the researcher’s state of knowledge before observing the data. Critics of Bayesian statistics note that prior distributions allow researchers to sway the results in the desired direction. This chapter shows how to conduct a Bayesian mediation analysis using real data from a study of delays in PhD completion in the Netherlands. The authors illustrate the challenges in specifying prior distributions and how to examine the influence of a prior distribution in a sensitivity analysis. The chapter also contains detailed examples of how to report the results of a Bayesian mediation analysis and future directions for the field of applied statistics for social sciences.
The current chapter attempts to cover the most important aspects of conducting research in the laboratory while allowing the reader to refer to other chapters in this volume to better understand the most prevalent and important limitations of the lab. We begin by describing what a lab is and by giving examples, from our own research and others, of the many different ways that a normal space can be used for laboratory research. We then explore the many advantages and disadvantages that often result from lab research, followed by the different types of research that are often conducted in the lab. Next, we move on the some of the many issues that one must consider when conducting lab research. We focus this section on issues related to both participants and potential research assistants, including recruitment, training, and minimizing biases. Following this, we present the different types and uses of deception while encouraging the reader to carefully examine each research question and study design before making any research decision. Finally, we discuss the generalizability of lab research to the real world and provide some considerations for increasing the likelihood that your research will generalize.
Interdisciplinary research (IDR) focuses on particular problems or questions that are too comprehensive to be answered satisfactorily by any one discipline. Overall, across disciplines, the practice of IDR is rapidly accelerating because the combination of researchers from different disciplines allows complicated problems to be solved. There is an urgent need for IDR and specific interdisciplinary training to address pressing social, political, economic challenges society faces. Additionally, the necessity to prepare students for an increasingly interdisciplinary, collaborative, and global future also calls for interdisciplinary exposure in post-secondary education. In this article, we aim to provide an explanation of IDR, and to offer a guiding framework towards interdisciplinary research with measurable and positive impact.
Many studies in behavioral science involve physiological measures because they allow the researcher a window into some underlying neural and biological processes. The use of such measures requires a multiple-levels-of-analysis approach to understanding behavior, where the physiological processes are just one level of analysis that should be considered in conjunction with others, such as the social situation or the structure of society. We provide an overview of three basic principles of psychophysiology that are important to consider when planning and interpreting research with these measures. We then give a brief introduction to most commonly used measures, including those that tap into in the autonomic nervous system (e.g., electrodermal activity, heart rate), hormones (e.g., cortisol, testosterone), muscle activity (e.g., facial electromyography), and the brain (e.g., event-related potentials, functional brain imaging). The chapter concludes with tips for reducing participant anxiety during experiments, which can otherwise interfere with physiological recordings.