Elements of Moral Functioning in Sport and School

Moral functioning is complex and implicates numerous cognitive and affective processes. Drawing upon Rest’s four-component model of moral functioning and more recent dual-process accounts of cognition, the current study examined a model of moral functioning in both sport and school contexts. Specifically, drawing upon the empirical record, a model of moral functioning was proposed and tested wherein moral identity influenced the adoption of specific contesting orientations, which, in turn, influenced prosocial and antisocial behaviors, both directly and indirectly via moral foundations and moral disengagement. Fit of the model was moderately strong in both contexts, though significant contextual differences emerged, both in terms of interrelationships between moral variables and in intra-individual variability within moral variables. Findings suggested that moral identity, a partnership approach to contesting, and moral foundations that emphasize care and fairness were associated with reduced antisocial behavior across contexts, while a war approach to contesting and moral disengagement were associated with increased antisocial behavior across contexts. Thus, practitioners concerned with athletes’ moral behavior may do well to: 1) promote the importance of moral concerns to the athlete’s self-identity; 2) highlight the cooperative and mutually-beneficial aspects of contests; and, 3) emphasize the importance of the moral values of care and fairness

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Quantitative empirical trends in technical performance

Technological improvement trends such as Moore’s law and experience curves have been widely used to understand how technologies change over time and to forecast the future through extrapolation. Such studies can also potentially provide a deeper understanding of R&D management and strategic issues associated with technical change. However, this requires that methodological approaches for these analyses be addressed and compared to more effectively interpret results. Our analysis of methodological issues recommends less ambiguous approaches to: 1) the unit of analysis; 2) choice of the metrics within a unit of analysis; 3) the relationships among possible independent variables; and 4) qualitative and quantitative data quality considerations. The paper then uses this methodology to analyze performance trends for 28 technological domains with the following findings: 1. Sahal’s relationship is tested for several effort variables (for patents and revenue in addition to cumulative production where it was first developed). 2. The relationship is quite accurate when all three relationships, ( a. an exponential between performance and time, b. an exponential of effort and time and c. a power law between performance and the effort variable) have good data fits (r2 >0.7) . 3. The power law and effort exponents determined are dependent upon the choice of effort variable but the time dependence exponential is not. 4. In domains where the quantity of patents do not increase exponentially with time, Sahal’s relationship gives poor estimates even though Moore’s law is followed even for these domains. 5. Good data quality for any of the relationships depends upon adequate screening involving not only r2 but also the confidence interval based upon two different statistical tests; by these measures, all 28 domains have high quality fits between the log of performance and time whereas less than ½ show this level of quality for power law fits with patents as the effort variable. Overall, the results are interpreted as indicating that Moore’s law is a better description of longer-term technological change when the performance data come from various designs whereas experience curves may be more relevant when a singular design in a given factory is considered

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Mapping Recent Decadal Climate Variations in Precipitation and Temperature across Eastern Africa and the Sahel

This chapter presents a novel interpolation approach that combines long-term mean satellite observations, station data, and topographic fields to produce grids of climate normals and trends. The approach was developed by the Climate Hazard Group (CHG) at the University of California, Santa Barbara (UCSB), to support food security analyses for the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET). The resulting FEWS NET Climatology (FCLIM) combines moving window regressions (MWRs) with geostatistical interpolation (kriging). Satellite and topographic fields often exhibit strong local correlations with in situ measurements of air temperature and rainfall. The FCLIM method uses these relationships to develop accurate and unbiased temperature and rainfall maps. The geostatistical estimation process provides standard error fields that take into account the density and spatial distribution of the point observations. These error fields are especially important when evaluating climate trends. Numerous climate change analyses present trend evaluations without assessing spatial uncertainty. In many of these studies, the number of recent observations can be very low, potentially invalidating the results. This study presents analyses for the Sahelian and eastern African rainfall and air temperatures. The results indicate significant rainfall declines in Sudan, Ethiopia, and Kenya. Every country exhibits significant increases in average air temperatures, with Sudan warming the most. This chapter concludes with a short discussion of how these results are being used to guide climate change adaptation, with a case study focused on Ethiopia

Early Warning of Food Security Crises in Urban Areas: The Case of Harare, Zimbabwe, 2007

In 2007, the citizens of Harare, Zimbabwe began experiencing an intense food security crisis. The crisis, due to a complex mix of poor government policies, high inflation rates and production decline due to drought, resulted in a massive increase in the number of food insecure people in Harare. The international humanitarian aid response to this crisis was largely successful due to the early agreement among donors and humanitarian aid officials as to the size and nature of the problem. Remote sensing enabled an early and decisive movement of resources greatly assisting the delivery of food aid in a timely manner. Remote sensing data gave a clear and compelling assessment of significant crop production shortfalls, and provided donors of humanitarian assistance a single number around which they could come to agreement. This use of remote sensing data typifies how remote sensing may be used in early warning systems in Africa