388 research outputs found
Climate Volatility and Change in Central Asia : Economic Impacts and Adaptation
Central Asia is projected to experience a significant climate change, combined with increased weather volatility. Agriculture is a key economic sector and a major source of livelihoods for Central Asia’s predominantly rural population, especially for the poor. Agricultural production, being sensitive to weather shocks and climate volatility, may suffer from climate change if no adaptive actions are taken. Taking these into account, the present study seeks to estimate the potential economic impacts of climate change on Central Asia’s agriculture and rural livelihoods, as well as to identify factors catalyzing or constraining adaptation to climate change. Weather shocks could potentially affect the supply of agricultural commodities and their prices. In this thesis, the effects of weather shocks on agricultural commodity prices in Central Asia are studied at the provincial scale using monthly data for the period of 2000-2010. The study analyses the idiosyncratic components of the variables using feasible generalized least squares (FGLS) panel regression in the presence of cross-sectional dependence and serial autocorrelation. The analysis indicates that negative shocks, involving lower than usual temperatures and precipitation amounts, could lead to higher wheat prices in the region. Lower availability of irrigation water may encourage irrigation-dependent countries in the region to aggressively raise wheat stocks to face expected supply shortfalls, thus leading to higher regional wheat prices. This effect could be further aggravated by negative impacts of lower irrigation water availability on wheat yields. The estimates of the aggregate impacts of climate change on Central Asian agriculture range between +1.21% to -1.43% of net crop production revenues by 2040. The absolute monetary impact is not negligible, ranging from + 180 mln USD annually in the optimistic scenario, to – 210 mln USD annually in the pessimistic scenario relative to 2010 levels, where optimistic and pessimistic scenarios are defined to correspond to B1 (lowest future emission trajectory) and A1FI (highest future emission trajectory) scenarios by IPCC (2007), respectively. As a key conclusion, agricultural producers operating in inherently stressed environments, such as in Central Asia, may have relatively more experience to dynamically adapt to erratic and changing environments. The analysis of the nationally representative household surveys using quantile regressions with and without instrumentalizing for endogeneity between consumption and production decisions within the framework of agricultural household model confirms that poorer households are more vulnerable to the impacts of weather and climate shocks with every 1% decrease in the level of their farming profits being likely to lead to 0.52% decrease in their food expenses. A similar decrease for the richest 10% of households would translate to only 0.39% decrease in food consumption. The models also show that the profit effect of potato prices seems to be quite important especially for the poorest farmers. Many farmers in Central Asia are already engaged in ex post adaptation to the changing climate; however, further Government support is needed for pro-active ex ante actions. A vital mechanism for achieving this purpose is through increasing farmers’ resilience and adaptive capacities to withstand current and future shocks, both expected and uncertain. The analysis shows that key policy actions to achieve this in the region are through: i) increasing awareness of agricultural producers about climate change impacts and adaptation technologies; and ii) improving rural financial intermediation. The key general message of the adaptation analysis in this study is that most institutional and technological options suggested as measures to adapt to climate change in the region are strongly needed for regional development even with perfect climate change mitigation.Klimaschwankungen und -veränderung in Zentralasien: Wirtschaftliche Auswirkungen und Anpassungsmöglichkeiten Zentralasien wird den Vorhersagen zufolge signifikante Klimaveränderungen gekoppelt mit erhöhten Klimaschwankungen erleben. Die Landwirtschaft ist ein wichtiger Wirtschaftszweig und eine wichtige Lebensgrundlage für die überwiegend ländliche Bevölkerung Zentralasiens, vor allem für die Armen. Die landwirtschaftliche Produktion, die anfällig für Wetterextreme und Klimaschwankungen ist, kann durch den Klimawandel beeinträchtigt werden, z.T. mit gravierenden Folgen für die Lebensgrundlage im ländlichen Raum in vielen Teilen der Region, wenn keine adaptive Maßnahmen ergriffen werden. Dies berücksichtigend versucht die vorliegende Studie, die möglichen wirtschaftlichen Auswirkungen des Klimawandels auf die Landwirtschaft und ländliche Lebensgrundlage Zentralasiens zu bewerten sowie die Faktoren, die die Anpassung an den Klimawandel katalysieren oder einschränken, zu identifizieren. Wetterextreme könnten potenziell die Versorgung mit landwirtschaftlichen Rohstoffen und deren Preise beeinträchtigen. In dieser Arbeit werden die Auswirkungen von Wetterextremen auf landwirtschaftliche Rohstoffpreise in Zentralasien auf Provinzebene mit monatlichen Daten für den Zeitraum von 2000-2010 untersucht. Die Studie verwendet eine innovative Schätzmethode, bei der die idiosynkratischen Komponenten der Variablen mit Verallgemeinerte Kleinste-Quadrate-Modelle (FGLS) Panelregression bei Querschnittsabhängigkeit und serieller Autokorrelation analysiert werden. Die Analyse zeigt, negative Extreme, die niedrigere Temperaturen und Niederschlagsmengen als üblich bedeuten, könnten günstige Bedingungen für höhere Weizenpreise in der Region hervorrufen. Eine geringere Verfügbarkeit von Bewässerungwasser kann die Länder in der Region, die vom Bewässerungswasser abhängig sind, dazu animiere, die Weizenbestände aggressiv zu erhöhen, um die zu erwartenden Engpässen abzupuffern, was zu höheren regionalen Weizenpreisen führen würde. Dieser Effekt könnte zusätzlich verschärft werden durch die negativen Auswirkungen geringerer Wasserverfügbarkeit auf die Weizenerträge. Die Schätzungen der aggregierten Auswirkungen des Klimawandels auf die zentralasiatische Landwirtschaft schwanken von +1,21% bis -1,43% des Nettoumsatzes für Getreideproduktion im Jahr 2040. Die absoluten monetären Auswirkungen sind nicht unerheblich, sie können von +180 Millionen USD jährlich im optimistischen Szenario bis hin zu -210 Millionen USD jährlich im pessimistischen Szenario gegenüber dem Niveau von 2010 variieren, entsprechend den optimistischen und pessimistischen Szenarien B1 (niedrigste zukünftige Emissionskurve) bzw. A1FI (höchste zukünftige Emissionskurve) des IPCC (2007). Als zentrales Ergebnis ist festzustellen, dass die landwirtschaftlichen Produzenten, die in derart inhärent unsicheren Umgebungen operieren, erfahrener sind, sich dynamisch an eine unregelmäßige und sich verändernde Umwelt anzupassen. Die Analyse von national repräsentative Haushaltsbefragungen unter Verwendung von Quantilregressionen mit und ohne Instrumentalisierung für Endogenität zwischen Konsum- und Produktionsentscheidungen im Rahmen des landwirtschaftlichen Haushalts-Modells bestätigt, dass ärmere Haushalte anfälliger sind für die Auswirkungen von Wetter- und Klimaextrema. Ein 1%er Rückgang des Niveaus ihrer landwirtschaftlichen Gewinne führt möglicherweise zu einem Rückgang von 0,52% der Verpflegungskosten, während ein ähnlicher Rückgang für die reichsten 10% der Haushalte nur zu einem Rückgang von 0,39% der Nahrungsaufnahme führen würde. Die Modelle zeigen auch, die Gewinnwirkung der Kartoffelpreise scheint vor allem für die ärmsten Bauern wichtig zu sein. Viele Bauern in Zentralasien sind bereits mit der ex-post-Anpassung an den Klimawandel beschäftigt; weitere Unterstützung seitens der Regierung ist jedoch für pro-aktive ex-ante-Maßnahmen. Ein wichtiger Mechanismus für die Erreichung dieses Ziels ist die Erhöhung der Widerstandsfähigkeit der Landwirte und deren Anpassungsfähigkeit an aktuelle und zukünftige, sowohl vorhersehbare als auch ungewisse Extrema. Die Analyse zeigt die folgenden wichtigsten politischen Maßnahmen auf, um dieses in der Region zu erreichen: i) das Bewusstsein der landwirtschaftlichen Produzenten hinsichtlich der Auswirkungen des Klimawandels und Anpassungtechnologien zu erhöhen; und ii) die Verbesserung der ländlichen Kredit- und Versicherungsvermittlung. Die Schlüsselbotschaft der Anpassungsanalyse dieser Studie ist, dass die meisten institutionellen und technologischen Möglichkeiten, die als Maßnahmen zur Anpassung an den Klimawandel in der Region vorgeschlagen werden, dringend erforderlich sind für regionale Entwicklung auch bei vollständiger Verringerung des Klimawandels
Incorporating the disturbance process of fire into invasive species habitat suitability models
Department Head: Michael J. Manfredo.2008 Fall.Includes bibliographical references (pages 120-131).This study is motivated by the difficulties land managers face while attempting to simultaneously maintain the natural role of fire in ecosystems and prevent the spread and proliferation of invasive plants. I developed habitat suitability models to predict the responses of three invasive species to fire and other environmental variables: one species in each of three National Parks. For each species, model comparisons tested whether the inclusion of nationally-available data on burn severity, time since fire, and fire occurrence could improve habitat suitability models relative to non-burn data alone. Each species demonstrated significant responses to fire, although incorporation of fire information into the models improved model performance for some species more than for others
New approaches in statistical modeling
Diese kumulative Dissertation befasst sich mit der statistischen Modellierung von räumlichen Netzwerkdaten, sowie von Daten zur Pandemie des SARS-CoV-2-Virus. Statistische Modellierung kann im übertragenden Sinne als ein großer "Werkzeugkasten'' verstanden werden, mit dem man Phänomene der realen Welt durch eine geeignete mathematische Formalisierung approximiert. Die in dieser Arbeit verwendeten Modelle beruhen in erster Linie auf Regression, wobei die Schwerpunkte auf der Glättung mit penalisierten Splines unter Einbeziehung von zufälligen Effekten liegen. Im Allgemeinen bestehen die Vorteile von Regressions- und statistischen Modellen darin, dass sie interpretierbare Modellergebnisse liefern und Vorhersagen über unbeobachtete Zustände erlauben. Gleichzeitig ist eine Beurteilung der zugrunde liegenden Unsicherheit der Schätzungen möglich. Diese drei Schlüsselaspekte des statistischen Modellierens spielen eine entscheidende Rolle in den fünf Beiträgen dieser kumulativen Dissertation.
Die ersten drei Artikel befassen sich mit statistischen Modellen und ihrer Anwendung auf Daten, die auf Netzwerken beobachtet werden. Netzwerke sind Strukturen, die aus durch Kanten verbundene Knoten bestehen. Während Netzwerke in natürlicher Weise abstrakte Beziehungen wie soziale Netzwerke oder ein Netzwerk von Geschäftspartnern darstellen können, liegt der Schwerpunkt in dieser Arbeit auf Netzwerken mit einer räumlichen Interpretation. Im ersten Artikel wird ein neues Modell entwickelt, welches erlaubt, statistische Rückschlüsse auf unbeobachtete Fahrten in Bike-Sharing-Netzwerken zu ziehen. Dabei stellen die Fahrradstationen die Eckpunkte des Netzwerks dar, und die Wege zwischen den Fahrradstationen entsprechen den Kanten. Der darauf folgende Artikel behandelt räumliche Netzwerke und die Schätzung der Intensität von stochastischen Prozessen, deren Realisierungen in räumlichen Netzwerken beobachtet werden. Die Methodik erlaubt auch die Einbeziehung von Kovariablen bei der Schätzung der Intensität. Diese Art der Modellierung ist neu und mit den aktuellen, auf Kerndichteschätzung basierenden Methoden, nicht möglich. Um die Methode frei zugänglich zu machen, wurde ein \textbf{R}-Paket implementiert. Der letzte Beitrag im Bereich der Netzwerke befasst sich mit der Vorhersage der Belegung von Parkplätzen, die entlang eines Straßennetzes verteilt sind. In diesem Zusammenhang wird die Netzwerkstruktur genutzt, um räumliche Abhängigkeiten zu modellieren. Darüber hinaus basieren die Vorhersagen auf einem Semi-Markov-Modell, um die nicht-exponentielle Dauer der einzelnen Zustände zu berücksichtigen. Die Übergangsintensitäten werden mit Hilfe von Überlebenszeitmodellen geschätzt.
Der zweite Teil dieser Dissertation befasst sich mit der Pandemie des SARS-CoV-2-Virus, das die Krankheit COVID-19 verursacht. Das deutsche Robert-Koch-Institut (RKI) stellt täglich Daten zu COVID-19-Infektionen und Todesfällen im Zusammenhang mit COVID-19 zur Verfügung, mit zusätzlichen Angaben zu Region, Geschlecht und Alter der Infizierten. Aus verschiedenen Gründen geben die Rohdaten keinen ausreichenden Aufschluss über den Schweregrad der Pandemie, weswegen statistische Modelle auf die Daten angewandt werden. Ein Beitrag befasst sich mit der Vorhersage tödlicher Infektionen auf regionaler Ebene unter Berücksichtigung der lokalen Bevölkerungsstruktur. Damit ist das Modell in der Lage, auch eine regionalspezifische Beurteilung der Schwere der Pandemie vorzunehmen. In einem zweiten Beitrag werden die tödlich endenden Infektionen mit der Anzahl der registrierten Infektionen zueinander in Beziehung gesetzt, um die Veränderung der Fallentdeckungsrate im Laufe der Zeit zu quantifizieren. Darüber hinaus ermöglicht die Methode, den Verlauf der tatsächlichen Zahl der Infektionen zu schätzen, während die gemeldeten Infektionszahlen durch verschiedene Teststrategien beeinflusst sind.This cumulative dissertation is concerned with statistical modeling of data observed on geometric networks and data related to the pandemic of the SARS-CoV-2 virus. Statistical modeling in its broadest sense encompasses a large "toolbox'' to approximate real-world phenomena in a mathematically formalized manner. Models used in this work are primarily regression-based, with an emphasis on penalized spline smoothing and the inclusion of random effects to control for latent heterogeneities. In general, the benefits of regression and statistical models include creating interpretable model results and making predictions about unobserved states while adequately communicating the underlying uncertainty. These three key aspects of statistical modeling play a crucial role in the five contributions of this cumulative dissertation.
The first three articles cover statistical models and their application to data observed on networks, i.e. structures consisting of vertices connected by a set of edges. While networks serve as a natural device to represent abstract relationships such as social networks or a network of commercial partners, the focus here is on spatial networks. The first article develops a new model to draw statistical inference about unobserved trips in bike-sharing networks. Here, bike stations represent the network's vertices, and the paths between the bike stations correspond to the edges. The consecutive article treats spatial networks, focusing on estimating stochastic processes' intensity functions with realizations observed on spatial networks. The methodology also allows fitting the intensity with covariates, which is novel and not feasible with the current state-of-the-art methods based on kernel smoothing. To make the methodology freely available, an \textbf{R} package has been implemented. The last contribution in the field of networks covers the prediction of on-street parking occupancy, where parking lots are distributed along a street network. In this context, the network structure is utilized to model spatial dependencies. Moreover, predictions are based on a semi-Markov model to account for non-exponential duration times in each state and the transition intensities are estimated employing time to event models.
The second part of this dissertation deals with the pandemic of the SARS-CoV-2 virus, which causes the disease COVID-19. The German Robert Koch Institute (RKI) daily provides data concerning COVID-19 infections and deaths related to COVID-19 with information on the infected's region, gender, and age. For several reasons, the raw data do not indicate the seriousness of the pandemic sufficiently well, which is why statistical models are used to get a clearer picture of the pandemic. One contribution is concerned with nowcasting fatal infections on a regional level while accounting for the local population structure. Thus, the model is capable of evaluating the region-specific seriousness of the pandemic. A second paper relates infections ending fatally to registered infections aiming at quantifying the change of the case detection ratio over time. Furthermore, the method allows assessing the relative course of the actual number of infections while testing strategies influence the reported numbers
Leveraging improved seed technology, migration and climate information for building the adaptive capacity and resilience to climate risks in semi-arid regions
Droughts induced by climate change will most likely push dryland ecosystems beyond their biophysical thresholds and lead to long-term decline in agricultural productivity. Subsistence farming in developing countries where agricultural productivity is low will become less viable for many families already ravaged by food insecurity and poverty. This dissertation examines three ways of reducing vulnerability to the adverse effects of climate variability and building resilience in the farming communities residing in semiarid lands. These include the use of adaptive seed technology, migration as a livelihood diversification and adaptive strategy, and the use of climate information in farm decision-making. The second chapter evaluates the impact of improved adaptive seed technology on market participation and food security, using data from a representative sample of 1344 households selected across six agroecological zones in Kenya. The study employed two estimation procedures for impact evaluation: a control function regression using OLS and IV regression estimated by Heckman bivariate sample selection model and 2SLS regression. The study used percentile shares approach to describe distributional inequalities in improved seed adoption across households. Kenya has a well-developed seed system, through which adaptive maize seed has been introduced for various agro-ecological zones. Despite its success with improved maize breeding programs, Kenya is still grappling with food insecurity. The marketed share of household's maize produce, among adopters, was on average 12 percentage points higher than for the control group. This increased with adoption intensity, albeit at a decreasing rate. The top 20% of households accounted for 63% of the quantity and 65% of the area planted with improved maize. The bottom 40% only accounted for 6% of the quantity purchased and 5% of the area planted with improved maize. Adopting households were less vulnerable to food insecurity and stored maize for longer than non-adopters. Larger families participated less in the market and were more food insecure. Wealth and education are other key determinants of food security and market participation. The results of the study indicate a need for a strategic policy on food security in Kenya that considers the concentrated nature of the maize farming sector, to address the problem of food insecurity. Such a policy could aim at food self-sufficiency for small farms and promote commercial production by large-scale producers for national strategic reserves. There is also a need for post-harvest policies that promote safe on-farm grain storage for small and medium scale producers. The third chapter focuses on migration, because of the growing interest among scholars in understanding the relationship between migration and adaptation to climate change. Past studies have looked at climate change as a trigger for migration, but the focus has now shifted to looking at migration as an enabler of climate-change adaptation and a livelihood diversification strategy. However, those most vulnerable to climate variability are the poor who are less able to afford mobility and entry costs. This study adds to the literature by evaluating, in chapter 3, the impact of migration on household consumption expenditure, relative food expenditure share, dietary diversity, spending on agricultural inputs and adaptive capacity. The study used survey data collected from a representative sample of 653 households across three arid regions of Northern Namibia. The study employed a novel identification strategy in migration studies by combining the standard exogenous instruments and Lewbel's constructed instruments using heteroscedastic errors. The study found two-thirds of the sampled households to be migrant-sending households. Poverty and the lack of economic opportunities in the rural villages were the main push factors driving migration to towns and cities. Although tertiary education and technical training of the migrants are key determinants of remittances received by migrant-sending households, over three quarters of the migrants were unskilled and very few having tertiary level training. Migrant-sending households had lower consumption spending and higher food budget share, suggesting relative deprivation. Although consumption spending increased with number of migrants, quality of human capital had greater impact on well-being. Migration had a positive impact on household's adaptive capacity but an inverse relationship between number of migrants and adaptation suggests failure of local adaptive strategies. The study finds households with migrants to have a significantly higher spending on agricultural inputs than those without migrants, with tractor-hire services for land preparation being a major component. The effect of family labour loss is somehow, through remittances, countervailed and compensated by mechanization. In conclusion, migration can potentially play a bigger role as an adaptive and risk-mitigation strategy in the face of climate variability, but poverty, lack of post-school skills training, and low transition to tertiary-level training are key barriers. Developing markets for credit, inputs and farm output, and preparing migrants for participation in labour markets and self-employment through training can further enhance the impact of migration and build resilience to climate shocks. Due to selfreinforcing poverty traps in poor households, the study recommends targeted public programs that support higher education and technical training. Lastly, chapter 4 examined the role of climate information and early warning in decision-making among farming communities in rural Namibia. Improved climate forecasting has been heralded as an important risk management and mitigation tool in climate-sensitive economic sectors such as agriculture. However, Africa has not reaped the benefits of improved climate forecasting and empirical studies about its impact are scanty. Chapter 4 first discusses access to and utilization of climate information in farm decisionmaking, and then evaluates its impact on dietary diversity, food spending and adaptive capacity of the households using propensity score matching, with a sensitivity analysis for hidden bias. Only half of the farmers had access to climate information and most of them relied primarily on traditional knowledge to make decisions on crop and livestock production. Many of the households without access to climate information also had little knowledge of alternative adaptive strategies. The likelihood of receiving climate information increased with the number of migrants per household, household size, social networks, trust and participation in community decision-making processes, but declined with age. Although male heads were more likely to receive climate information, females headed most of the households. The main sources of information for farmers were radios and peer learning. Respondents expressed a low level of trust in information from available channels and most of them rated the information received as insufficient for decision-making. Although 95% of households owned mobile phones, only 5% received information through them, indicating untapped opportunity of using an ICT platform to share information with farmers. Households with climate information had more diversified diets and significantly higher food spending. These households also engaged in more adaptive strategies, but the scale of adoption was small. Community empowerment through enhanced access to extension services, information on alternative adaptive choices, and the development of markets, rural communication and transport infrastructure are prerequisites to access to and effective utilization of improved climate forecast information for successful adaptation
Optimizing peak gust and maximum sustained wind speed estimates from mid-latitude wave cyclones
Wind storms cause significant damage and economic loss and are a major recurring threat in many countries. Maximum sustained and peak gust weather station data from multiple historic wind storms occurring over more than three decades across Europe were analyzed to identify storm tracks, intensities, and areas of frequent high wind speeds. Wind surfaces for maximum sustained and peak gust winds were estimated based on an anisotropic (directionally-dependent) kriging interpolation methodology. Overall, wind speed magnitudes and high intensity locations were identified accurately for each storm. Directional trends and wind swaths were also consistently located in appropriate locations based on known storm tracks. Anisotropic kriging proved to be superior to isotropic (non-directional) kriging when modeling continental-scale wind storms because of the identification of strong directional correlations across space. Results suggest that coastal areas and mountainous areas experience the highest wind intensities during wind storms. These same areas also experience high variability over short distances and thus the highest error measurements associated with concurrent interpolated surfaces. For this reason, various covariates were utilized in conjunction with the cokriging interpolation technique and improved the interpolated wind surfaces for five wind storms that impacted both the mountainous and topographically-varied Alps region and the coastal regions of Europe. Land cover alone reduced station-measured standard error most significantly in a majority of the models, while aspect and elevation (singularly and collectively) also reduced station standard error in most models as compared to the original kriging models. Additional comparisons between different areal scales of kriging/cokriging models revealed that some surface wind variability is muted at the continental scale, but identifiable at the local scale. However, major patterns and trends are more difficult to ascertain for local-scale surfaces when compared to continental-scale surfaces. Large station error can be reduced through local kriging/cokriging, but additional research is needed to merge local-scale semivariograms with continental-scale models. Results showed substantial improvements in wind speed surface estimates over previous estimates and have major implications for catastrophe modeling companies, insurance needs, and construction standards. Implications of this research may be transferrable to other geographies and create an impetus for database and covariate improvement
Agricultural Production in the 21st Century: Land-use, Diversity, Pests and Pesticides
Over the next 50 years, global food demand is forecast to double. Already it is estimated that agriculture covers about 40% of ice-free land, accounts for a third of greenhouse gas emissions, and contributes significantly to global biodiversity declines. One means to reduce the impact of agriculture on humans and natural systems is to ensure the efficient use of pesticides. Pesticides, especially insecticides, have numerous negative externalities for human and environmental health, and their efficient use is an economic, ecological and public health priority. How land use patterns influence insect pests and insecticide demand is of special concern, because productive and efficient land use is key to meeting future food demand. This research investigates the relationships between insecticide use and landscape configuration. It further investigates the importance of weather variability and data quality to understanding agriculture in the 21st Century. Finally, it explores ecological theory to understand how multiple natural enemies may coexist on a single resource species.Specifically, I address the following questions: 1) is landscape simplification a consistent driver of insecticide use across time, 2) is landscape simplification a consistent driver of insecticide use across space and throughout the varied growing regions of the US, and do annual weather patterns influence insecticide use? 3) Is satellite crop data sufficiently accurate to be applied to ecological and economic questions at the sub-county level? 4) Can coexistence be driven by non-consumptive ecological interactions?To address these questions I integrate ecological and economic theory, and apply multivariate statistical techniques to multi-year national or regional databases. I find that, contrary to expectations from ecological theory, landscape simplification does not consistently drive insecticide use over time (Chapter 1) or space (Chapter 2). This spatio-temporal variation helps explain the ambiguous results in the literature and implies that national land use policy will have very different effects on insecticide use if regional differences are ignored. To further understand the underlying mechanisms requires fine-scale spatial information of configuration and crop type. However, leveraging satellite data for sub-county information such as spatial configuration is well suited to simplified growing regions, but highly inaccurate elsewhere (Chapter 3). Lastly, I show natural enemies and other intermediate consumers can coexist with sufficiently strong non-consumptive effects of a top predator on the dominant consumer (Chapter 4).In 2007 US farmers applied ~70 million pounds of insecticide active ingredients. While farmers pay the purchase price, society pays for degradation of natural systems and harm to human health. To minimize the cost of insecticides to both farmers and society, now and under future climate change, we must understand what drives variation in insecticide use and what enables persistence of natural enemy diversity. My dissertation research informs these key gaps in our understanding
Adapting regression equations to minimize the mean squared error of predictions made using covariate data from a GIS
Regression equations between a response variable and candidate explanatory variables are often estimated using a training set of data from closely observed locations but are then applied using covariate data held in a GIS to predict the response variable at locations throughout a region. When the regression assumptions hold and the GIS data are free from error, this procedure gives unbiased estimates of the response variable and minimizes the prediction mean squared error. However, when the explanatory variables in the GIS are recorded with substantially greater errors than were present in the training set, this procedure does not minimize the prediction mean squared error. A theoretical argument leads to the proposal of an adaptation for regression equations to minimize the prediction mean squared error. The effectiveness of this adaptation is demonstrated by a simulation study and by its application to an equation for tree growth rate
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