65 resultados para Agriculture Forecasting
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In today’s atmosphere of constrained defense spending and reduced research budgets, determining how to allocate resources for research and design has become a critical and challenging task. In the area of aircraft design there are many promising technologies to be explored, yet limited funds with which to explore them. In addition, issues concerning uncertainty in technology readiness as well as the quantification of the impact of a technology (or combinations of technologies), are of key importance during the design process. This paper presents a methodology that details a comprehensive and structured process in which to quantitatively explore the effects of technology for a given baseline aircraft. This process, called Technology Impact Forecasting (TIF), involves the creation of a assessment environment for use in conjunction with defined technology scenarios, and will have a significant impact on resource allocation strategies for defense acquisition. The advantages and limitations of the method are discussed. In addition, an example TIF application, that of an Uninhabited Combat Aerial Vehicle, is presented and serves to illustrate the applicability of this methodology to a military system.
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Iron Age societies of the eastern Eurasian steppe are traditionally viewed as nomadic pastoralists. However, recent archaeological and anthropological research in Kazakhstan has reminded us that pastoralist economies can be highly complex and involve agriculture. This paper explores the nature of the pastoralist economies in two Early Iron Age populations from the burial grounds of Ai-Dai and Aymyrlyg in Southern Siberia. These populations represent two cultural groups of the Scythian World - the Tagar Culture of the Minusinsk Basin and the Uyuk Culture of Tuva. Analysis of dental palaeopathology and carbon and nitrogen stable isotopes suggests that domesticated cereals, particularly millet, and fish formed a major component of the diet of both groups. The findings contribute to the emerging picture of the nuances of Early Iron Age subsistence strategies on the eastern steppe.
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Funded by ESRC Knowledge Research Fellowship Programme.
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Reference: ECA: WPW/02/CASE NORTHERN IRELAND W/X0000/e
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High levels of As in groundwater commonly found in Bangladesh and other parts of Asia not only pose a risk via drinking water consumption but also a risk in agricultural sustainability and food safety. This review attempts to provide an overview of current knowledge and gaps related to the assessment and management of these risks, including the behaviour of As in the soil-plant system, uptake, phytotoxicity, As speciation in foods, dietary habits, and human health risks. Special emphasis has been given to the situation in Bangladesh, where groundwater via shallow tube wells is the most important source of irrigation water in the dry season. Within the soil-plant system, there is a distinct difference in behaviour of As under flooded conditions, where arsenite (AsIII) predominates, and under nonflooded conditions, where arsenate (AsV) predominates. The former is regarded as most toxic to humans and plants. Limited data indicate that As-contaminated irrigation water can result in a slow buildup of As in the topsoil. In some cases the buildup is reflected by the As levels in crops, in others not. It is not yet possible to predict As uptake and toxicity in plants based on soil parameters. It is unknown under what conditions and in what time frame As is building up in the soil. Representative phytotoxicity data necessary to evaluate current and future soil concentrations are not yet available. Although there are no indications that crop production is currently inhibited by As, long-term risks are clearly present. Therefore, with concurrent assessments of the risks, management options to further prevent As accumulation in the topsoil should already have been explored. With regard to human health, data on As speciation in foods in combination with food consumption data are needed to assess dietary exposure, and these data should include spatial and seasonal variability. It is important to control confounding factors in assessing the risks. In a country where malnutrition is prevalent, levels of inorganic As in foods should be balanced against the nutritional value of the foods. Regarding agriculture, As is only one of the many factors that may pose a risk to the sustainability of crop production. Other risk factors such as nutrient depletion and loss of organic matter also must be taken into account to set priorities in terms of research, management, and overall strategy.
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The deployment of biofuels is significantly affected by policy in energy and agriculture. In the energy arena, concerns regarding the sustainability of biofuel systems and their impact on food prices led to a set of sustainability criteria in EU Directive 2009/28/EC on Renewable Energy. In addition, the 10% biofuels target by 2020 was replaced with a 10% renewable energy in transport target. This allows the share of renewable electricity used by electric vehicles to contribute to the mix in achieving the 2020 target. Furthermore, only biofuel systems that effect a 60% reduction in greenhouse gas emissions by 2020 compared with the fuel they replace are allowed to contribute to meeting the target. In the agricultural arena, cross-compliance (which is part of EU Common Agricultural Policy) dictates the allowable ratio of grassland to total agricultural land, and has a significant impact on which biofuels may be supported. This paper outlines the impact of these policy areas and their implications for the production and use of biofuels in terms of the 2020 target for 10% renewable transport energy, focusing on Ireland. The policies effectively impose constraints on many conventional energy crop biofuels and reinforce the merits of using biomethane, a gaseous biofuel. The analysis shows that Ireland can potentially satisfy 15% of renewable energy in transport by 2020 (allowing for double credit for biofuels from residues and ligno-cellulosic materials, as per Directive 2009/28/EC) through the use of indigenous biofuels: grass biomethane, waste and residue derived biofuels, electric vehicles and rapeseed biodiesel. © 2010 Elsevier Ltd. All rights reserved.
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Malone, C.A.T. and S.K.F. Stoddart, Current Anthropology, 1992. 33(5): p. 594-5.
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Malone, C.A.T.,, in, C. Malone and S. Stoddart, (eds.). 1994, Cambridge University Press: Cambridge. p. 67-
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Wind energy has been identified as key to the European Union’s 2050 low carbon economy. However, as wind is a variable resource and stochastic by nature, it is difficult to plan and schedule the power system under varying wind power generation. This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact of the magnitude and variance of the offshore wind power forecast error on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price is analysed. The main findings of this research are that the magnitude of the offshore wind power forecast error has the largest impact on system generation costs and dispatch-down of wind, but the variance of the offshore wind power forecast error has the biggest impact on emissions costs and system marginal price. Overall offshore wind power forecast error variance results in a system marginal price increase of 9.6% in 2050.
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Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.