Predicting the Distribution of High Nature Value farmland in Ireland: IDEAL-HNV

Conference presentation outlining the IDEAL-HNV projec

Predicted distribution of High Nature Value farmland in the Republic of Ireland

peer-reviewedHigh Nature Value (HNV) farmland is typically characterised by low-intensity farming associated with high biodiversity and species of conservation concern. Mapping the occurrence and distribution of such farmland are useful for appropriate targeting of conservation measures and supporting associated rural communities. We mapped the likely distribution of HNV farmland in the Republic of Ireland using a linear regression model incorporating established European indicators, adapted for Ireland and weightings based on expert opinion. The indicators used were semi-natural habitat cover, stocking density, hedgerow density, river and stream density and soil diversity, with highest weightings placed on the first two indicators (40% and 30%, respectively). The map provides information on the likely occurrence and distribution of HNV farmland in each electoral division as a reference point for future monitoring of the distribution of HNV farmland in the Republic of Ireland in order to assist with planning and policy development for the rural environment.This study was conducted by Teagasc and IT Sligo as part of the IDEAL-HNV project [Ref. 11/S/108], funded by the Department of Agriculture, Food and the Marine (DAFM) under the National Development Plan 2007–2013

Predicted distribution of High Nature Value farmland in the Republic of Ireland

High Nature Value (HNV) farmland is typically characterised by low-intensity farming associated with high biodiversity and species of conservation concern. Mapping the occurrence and distribution of such farmland are useful for appropriate targeting of conservation measures and supporting associated rural communities. We mapped the likely distribution of HNV farmland in the Republic of Ireland using a linear regression model incorporating established European indicators, adapted for Ireland and weightings based on expert opinion. The indicators used were semi-natural habitat cover, stocking density, hedgerow density, river and stream density and soil diversity, with highest weightings placed on the first two indicators (40% and 30%, respectively). The map provides information on the likely occurrence and distribution of HNV farmland in each electoral division as a reference point for future monitoring of the distribution of HNV farmland in the Republic of Ireland in order to assist with planning and policy development for the rural environment.This study was conducted by Teagasc and IT Sligo as part of the IDEAL-HNV project [Ref. 11/S/108], funded by the Department of Agriculture, Food and the Marine (DAFM) under the National Development Plan 2007–2013

Distribution and extent of High Nature Value farmland in the Republic of Ireland (tetrad scale)

supplementary dataHigh Nature Value (HNV) farmland is extensively managed farmland that has high biodiversity. This farmland is important for the conservation of semi-natural habitats and the plants and animals linked with them. Supporting this type of farmland will ensure high levels of farmland biodiversity, vibrant rural communities, high water, air and soil quality and resistance to flooding among other things. To map the likely distribution of HNV farmland in the Republic of Ireland (ROI) we used five indicators adapted for the Irish context and weighted based on expert knowledge and literature. The indicators used are: semi-natural habitat cover (CORINE land cover), stocking density (Land parcel information system), hedgerow/scrub cover (Teagasc), river and stream density (OSI), and soil diversity (Teagasc). Indicator data sets were included in a weighted sum model that combined raster indicator inputs, representing relative weights and the output HNV farmland had a tetrad-scale (2 km × 2 km) spatial resolution

Preterm birth and neonatal mortality in selected slums in and around Dhaka City of Bangladesh: A cohort study.

BackgroundAlthough under-five mortality has declined appreciably in Bangladesh over the last few decades, neonatal mortality still remains high. The objective of the study is to assess the level and determinants of preterm birth and the contribution of preterm birth to neonatal mortality.MethodsData for this study came from selected slums in and around Dhaka city, where; since 2015, icddr,b has been maintaining the Health and Demographic Surveillance System (HDSS). The HDSS data were collected by female Field Workers by visiting each household every three months; however, during the visit, data on the Last Menstrual Period (LMP) were also collected by asking each eligible woman to ascertain the date of conception. Gestational age was estimated in complete weeks by subtracting LMP from the date of the pregnancy outcome. In this study, 6,989 livebirths were recorded by HDSS during 2016-2018, and these births were followed for neonatal survival; both bivariate and multivariate analyses were performed.ResultsOut of total births, 21.7% were born preterm (before 37 weeks of gestation), and sub-categories were: 2.19% for very preterm (28 to 31 weeks), 3.81% for moderate preterm (32 to 33 weeks), and 15.71% for late preterm (34 to 36 weeks). The study revealed that preterm babies contributed to 39.6% of neonatal deaths; however, the probability of death was very high on the 1st day of birth (0.124 for very preterm, 0.048 for moderate preterm, 0.024 for late preterm, and 0.013 for term birth), and continued until the 3rd day. In the regression analysis, compared to the term neonates, the odds of neonatal mortality were 8.66 (CI: 5.63, 13.32, pConclusionsAlthough urban slums are in proximity to many health facilities, a substantial proportion of preterm births contribute to neonatal deaths. So, pregnant women should be targeted, to ensure timely care during pregnancy, delivery, and post-partum periods to improve the survival of new-borns in general and preterm birth in particular

Daily risk of death during the first month of birth by gestational age category.

Daily risk of death during the first month of birth by gestational age category.</p

Number of births, number of deaths, neonatal death rates, relative risks, and population attributable fractions by gestation age categories.

Number of births, number of deaths, neonatal death rates, relative risks, and population attributable fractions by gestation age categories.</p

Distribution of births, deaths, neonatal mortality rate and relative risk by gestational age.

Distribution of births, deaths, neonatal mortality rate and relative risk by gestational age.</p

Logistic regression estimates of neonatal mortality (N = 6908).

Logistic regression estimates of neonatal mortality (N = 6908).</p