Vitamin A supplementation on child morbidity

Objective: To determine the impact of vitamin A supplementation on child morbidity and nutritional status. Design: A community based follow-up (interventional) in nature. Setting: Two randomly selected Weredas (districts) of Tigray, North Ethiopia were studied between 1996 and 1997. Subjects: Four thousand seven hundred and seventy children aged between six and 72 months, selected using a multi-stage sampling procedure were enrolled and clinically assessed for xerophthalmia and nutritional status. A sub-sample of these children (n = 281) was further assessed for their serum retinol levels. Main outcome measures: The pre and post intervention data on xerophthalmia, morbidity, nutritional status and serum retinol levels were compared. Results: Vitamin A capsule coverage of 87% in all the villages of the Weredas and a statistically significant (p 0.7 µmole/L) has also improved significantly (from 36.8 to 56.2). Conclusion: In conclusion, the significant improvement in morbidity and nutritional status that followed the intervention programme although encouraging, it still indicates the importance of coupling periodic provision of Vitamin A capsules with nutrition education. (East African Medical Journal: 2003 80(1): 17-21

Vegetation Outlook (VegOut): Predicting Remote Sensing–Based Seasonal Greenness

Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing–based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006), which in turn may help to address agricultural development and food security issues, as well as improve early warning systems. Many studies have demonstrated the value of Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), calculated from satellite observations for assessing vegetation cover and conditions (Tucker et al., 1985; Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005), and such data have become a common source of information for vegetation monitoring. The term vegetation condition in this chapter refers to vegetation greenness or vegetation health, as inferred from canopy reflectance values measured by satellite observations (Mennis, 2001; Anyamba and Tucker, 2005). The vegetation greenness metric is commonly calculated from time-series NDVI (Reed et al., 1994) and represents the seasonal, time-integrated NDVI at a specific date, which has been shown to be representative of indicators of general vegetation health including net primary production (NPP) and green biomass (Tucker et al., 1985; Reed et al., 1996; Yang et al., 1998; Eklundh and Olsson, 2003; Hill and Donald, 2003). As a result, VIs and VI derivatives such as time-integrated VI can be used to characterize the temporal and spatial relationships between climate and vegetation and improve our understanding of the lagged relationship between climate (e.g., precipitation and temperature) and vegetation response (Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005; Camberlin et al., 2007; Groeneveld and Baugh, 2007). Quantitative descriptions of climate-vegetation response lags can then be used to identify and predict vegetation stress during drought

Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter

Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.Comment: 4 pages with 2 figures and 1 table. NeurIPS workshop on Tackling Climate Change with Machine Learning, 202

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regional level vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions

Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc

Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc

Selecting maize for rapid kernel drydown: timing of moisture measurement

Previous studies have shown that maize ear moisture measured using a modified Electrophics Moisture Meter model MT808 was highly correlated to kernel moisture and could be used as a selection tool in breeding maize genotypes with faster rates of kernel drydown. Such a tool would need to be standardized for practical and routine use in a breeding program with large numbers of plants. The objective of this study was to determine the opti¬mum time for measuring ear moisture using this meter. In a split-plot design with three replicates in 2007, 2008 and 2009, ear moisture of six inbred lines and eight F1 hybrids were measured weekly from one to eight weeks post-silking using a modified MT808 moisture meter. To determine if multiple ear moisture readings (EMRs) could be made on the same ear, an additional treatment was added so that all eight readings were made on the same ear. There was a positive correlation between weekly EMRs readings done on separate ears and those done on the same ear, indicating that repeated readings, if desired, could be made on the same ear. Significant genotypic differences in EMRs were found five to eight weeks post-silking. The EMRs at week one, five, and eight could be used to calculate a daily drydown rate (DDR). Maize genotypes (hybrids and inbreds) could be divided into four groups based on their DDRs during development as: high-high, high-low, low-high, and low-low DDRs from weeks one to five and five to eight, respectively. Genotypes with higher DDRs from weeks one to five tended to have overall higher DDRs by eight weeks post-silking. Inbred lines with higher DDRs at either stage expressed this trait in their hybrid crosses. This non-destructive method will improve selection for fast kernel drydown in maize breed¬ing programs, especially in short-season areas

Assessing the Vegetation Condition Impacts of the 2011 Drought across the U.S. Southern Great Plains Using the Vegetation Drought Response Index (VegDRI)

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of recordbreaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing

High-efficiency quantum interrogation measurements via the quantum Zeno effect

The phenomenon of quantum interrogation allows one to optically detect the presence of an absorbing object, without the measuring light interacting with it. In an application of the quantum Zeno effect, the object inhibits the otherwise coherent evolution of the light, such that the probability that an interrogating photon is absorbed can in principle be arbitrarily small. We have implemented this technique, demonstrating efficiencies exceeding the 50% theoretical-maximum of the original ``interaction-free'' measurement proposal. We have also predicted and experimentally verified a previously unsuspected dependence on loss; efficiencies of up to 73% were observed and the feasibility of efficiencies up to 85% was demonstrated.Comment: 4 pages, 3 postscript figures. To appear in Phys. Rev. Lett; submitted June 11, 199