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

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)

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)

Ranking Functions for Vector Addition Systems

Vector addition systems are an important model in theoretical computer science and have been used for the analysis of systems in a variety of areas. Termination is a crucial property of vector addition systems and has received considerable interest in the literature. In this paper we give a complete method for the construction of ranking functions for vector addition systems with states. The interest in ranking functions is motivated by the fact that ranking functions provide valuable additional information in case of termination: They provide an explanation for the progress of the vector addition system, which can be reported to the user of a verification tool, and can be used as certificates for termination. Moreover, we show how ranking functions can be used for the computational complexity analysis of vector addition systems (here complexity refers to the number of steps the vector addition system under analysis can take in terms of the given initial vector)

The Influence of Maternal Obesity and Breastfeeding on Infant Appetite- and Growth-Related Hormone Concentrations: The SKOT Cohort Studies.

BACKGROUND/AIMS: Exposure to obesity during pregnancy may lead to adverse changes in the offspring's metabolic profile. We compared appetite- and growth-related hormones in a cohort of infants born to obese mothers (SKOT-II) with infants born mainly to nonobese mothers (SKOT-I). METHODS: Infants from SKOT-I (n = 273) and SKOT-II (n = 132) were examined including anthropometric measurements and blood samples analyzed for glucose, insulin, insulin-like growth factor-I (IGF-I), adiponectin, and leptin. Information on breastfeeding and parental characteristics were also collected. RESULTS: At 9 months of age, SKOT-II infants were 3.6% heavier and 1.2% longer than SKOT-I infants even though their mothers were shorter. There was no difference in body mass index (BMI). SKOT-II infants had higher levels of insulin, adiponectin, and leptin but lower levels of IGF-I compared to SKOT-I infants (all p ≤ 0.015). These differences remained, except for leptin, when adjusted for current weight. Breastfeeding versus nonbreastfeeding at 9 months was associated with lower concentrations of all hormones (all p ≤ 0.003). In adjusted models, maternal BMI at 9 months was positively associated with insulin and adiponectin and negatively with IGF-I. CONCLUSIONS: Pre-pregnancy obesity confers symmetrically larger infant body size and higher levels of most growth- and appetite-related hormones but surprisingly lower levels of IGF-I, suggesting other possible infant growth-promoting effects through insulin

Bioimpedance index for measurement of total body water in severely malnourished children: Assessing the effect of nutritional oedema

Restoration of body composition indicates successful management of severe acute malnutrition (SAM). Bioimpedance (BI) index (height(2)/resistance) is used to predict total body water (TBW) but its performance in SAM, especially with oedema, requires further investigation

The role of leptin and other hormones related to bone metabolism and appetite regulation as determinants of gain in body fat and fat-free mass in 8-11-year-old children.

BACKGROUND: Regulation of body composition during childhood is complex. Numerous hormones are potentially involved. Leptin has been proposed to restrain weight gain, but results are inconsistent. OBJECTIVE: We examined whether baseline fasting levels of ghrelin, adiponectin, leptin, insulin, IGF-I, osteocalcin, and intact parathyroid hormone (iPTH) were associated with body composition cross sectionally and longitudinally in 633 8-11-year-olds. DESIGN: Data on hormones and body composition by dual-energy x-ray absorptiometry from the OPUS School Meal Study were used. We looked at baseline hormones as predictors of baseline fat mass index (FMI) or fat-free mass index (FFMI), and also subsequent changes (3 and 6 months) in FMI or FFMI using models with hormones individually or combined. RESULTS: Cross-sectionally, baseline leptin was positively associated with FMI in girls (0.211 kg/m(2) pr. μg/mL; 97.5% confidence interval [CI],0.186-0.236; P < .001) and boys (0.231 kg/m(2) pr. μg/mL; 97.5% CI, 0.200-0.261; P < .001). IGF-I in both sexes and iPTH in boys were positively associated with FMI. An inverse association between adiponectin and FFMI in boys and a positive association between IGF-I and FFMI were found in girls. In longitudinal models, baseline leptin was inversely associated with subsequent changes in FMI (-0.018 kg/m(2) pr. μg/mL; 97.5% CI, -0.034 - -0.002; P = .028) and FFMI (-0.014 kg/m(2) pr. μg/mL; 97.5% CI, -0.024 - -0.003; P = .006) in girls. CONCLUSIONS: Cross-sectional findings support that leptin is produced in proportion to body fat mass, but the longitudinal observations support that leptin inhibits gains in FMI and FFMI in girls, a finding that may reflect preserved leptin sensitivity in this predominantly normal weight population.Address all correspondence and requests for reprints to: Stine-Mathilde Dalskov, Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark. E-mail: [email protected]. This study was registered inClinicalTrials.gov as trial number NCT01457794. The OPUS study was financed by a Grant from the Nordea Foundation (grant number 02-2010-478 0389). A complete list of food suppliers providing full or partial food sponsorships to the study can be found at the website: http://foodoflife.ku.dk/ opus/wp/skolemadsprojektet/leverandorer. Sources of funding and donation had no role in the trial design; collection, analysis, interpretation of data or decision to publish.This is the accepted manuscript for a paper published in Journal of Clinical Endocrinology and Metabolism, March 2015, 100(3):1196 –1205, DOI: 10.1210/jc.2014-370