The impact of land cover change on surface energy and water balance in Mato Grosso, Brazil

The sensitivity of surface energy and water fluxes to recent land cover changes is simulated for a small region in northern Mato Grosso, Brazil. The Simple Biosphere Model (SiB2) is used, driven by biophysical parameters derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution, to compare the effects of different land conversion types. The mechanisms through which changes in vegetation alter surface fluxes of energy, momentum, water, and carbon are analyzed for both wet and dry seasons. It is found that morphological changes contribute to warming and drying of the atmosphere while physiological changes, particularly those associated with a plant’s photosynthetic pathway, counterbalance or exacerbate the warming depending on the type of conversion and the season. Furthermore, this study’s results indicate that initial clearing of evergreen and transition forest to bare ground increases canopy temperature by up to 1.7°C. For subsequent land use such as pasture or cropland, the largest effect is seen for the conversion of evergreen forest to C3 cropland during the wet season, with a 21% decrease of the latent heat flux and 0.4°C increase in canopy temperature. The secondary conversion of pasture to cropland resulted in slight warming and drying during the wet season driven mostly by the change in carbon pathway from C4 to C3. For all conversions types, the daily temperature range is amplified, suggesting that plants replacing forest clearing require more temperature tolerance than the trees they replace. The results illustrate that the effect of deforestation on climate depends not only on the overall extent of clearing but also on the subsequent land use type

Differential genetic etiology of reading disability as a function of mathematics performance

In order to assess the etiology of reading disability as a function of mathematics performance, data from 168 monozygotic (MZ) and 127 same-sex dizygotic (DZ) twin pairs in which at least one member of each pair was reading-disabled were subjected to quantitative genetic analyses. MZ and DZ concordance rates for reading disability were computed for different levels of mathematics performance, and reading performance data were fitted to an extension of the basic multiple regression model for the analysis of selected twin data. Results of these analyses suggest that genetic factors may be especially salient as a cause of reading disability in children with borderline deficits in mathematics performance: thus, mathematics performance may be a valid dimension for diagnosing subtypes of reading disability.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43574/1/11145_2004_Article_BF00395110.pd

Generating carbon finance through avoided deforestation and its potential to create climatic, conservation and human development benefits

Recent proposals to compensate developing countries for reducing emissions from deforestation (RED) under forthcoming climate change mitigation regimes are receiving increasing attention. Here we demonstrate that if RED credits were traded on international carbon markets, even moderate decreases in deforestation rates could generate billions of Euros annually for tropical forest conservation. We also discuss the main challenges for a RED mechanism that delivers real climatic benefits. These include providing sufficient incentives while only rewarding deforestation reductions beyond business-as-usual scenarios, addressing risks arising from forest degradation and international leakage, and ensuring permanence of emission reductions. Governance may become a formidable challenge for RED because some countries with the highest RED potentials score poorly on governance indices. In addition to climate mitigation, RED funds could help achieve substantial co-benefits for biodiversity conservation and human development. However, this will probably require targeted additional support because the highest biodiversity threats and human development needs may exist in countries that have limited income potentials from RED. In conclusion, how successfully a market-based RED mechanism can contribute to climate change mitigation, conservation and development will strongly depend on accompanying measures and carefully designed incentive structures involving governments, business, as well as the conservation and development communities

Integrating evidence, politics and society: a methodology for the science–policy interface

There is currently intense debate over expertise, evidence and ‘post-truth’ politics, and how this is influencing policy formulation and implementation. In this article, we put forward a methodology for evidence-based policy making intended as a way of helping navigate this web of complexity. Starting from the premise of why it is so crucial that policies to meet major global challenges use scientific evidence, we discuss the socio-political difficulties and complexities that hinder this process. We discuss the necessity of embracing a broader view of what constitutes evidence—science and the evaluation of scientific evidence cannot be divorced from the political, cultural and social debate that inevitably and justifiably surrounds these major issues. As a pre-requisite for effective policy making, we propose a methodology that fully integrates scientific investigation with political debate and social discourse. We describe a rigorous process of mapping, analysis, visualisation and sharing of evidence, constructed from integrating science and social science data. This would then be followed by transparent evidence evaluation, combining independent assessment to test the validity and completeness of the evidence with deliberation to discover how the evidence is perceived, misunderstood or ignored. We outline the opportunities and the problems derived from the use of digital communications, including social media, in this methodology, and emphasise the power of creative and innovative evidence visualisation and sharing in shaping policy

Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India

Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012–2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%–78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1–3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires

Comorbidity of Reading and Mathematics Disabilities

Although children with learning disabilities frequently manifest comorbid reading and mathematics deficits, the cause of this comorbidity is unknown. To assess the extent to which comorbidity between reading and mathematics deficits is due to genetic and environmental influences, we conducted a twin study of reading and mathematics performance. Data from 148 identical and 111 fraternal twin pairs in which at least one member of the pair had a reading disability were subjected to a cross-concordance analysis and also fitted to a bivariate extension of the basic multiple regression model for the analysis of selected twin data. Results of these analyses suggest that genetic and shared-environmental influences both contribute to the observed covariance between reading and mathematics deficits.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68572/2/10.1177_002221949502800204.pd

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes

Healthy, affordable and climate-friendly diets in India

India has among the highest lost years of life from micronutrient deficiencies. We investigate what dietary shifts would eliminate protein, iron, zinc and Vitamin A deficiencies within households’ food budgets and whether these shifts would be compatible with mitigating climate change. This analysis uses the National Sample Survey (2011–12) of consumption expenditure to calculate calorie, protein and the above micronutrient intake deficiencies and relate them to diets, income and location. We show that more than two-thirds of Indians consume insufficient micronutrients, particularly iron and Vitamin A, and to a lesser extent zinc. A greater proportion of urban households than rural households are deficient at all income levels and for all nutrients, with few exceptions. Deficiencies reduce with increasing income. Using constrained optimization, we find that households could overcome these nutrient deficiencies within their food budgets by diversifying their diets, particularly towards coarse cereals, pulses, and leafy vegetables, and away from rice. These dietary changes could reduce India’s agricultural greenhouse gas (GHG) emissions by up to 25%. Current agricultural and food pricing policies may disincentivize these dietary shifts, particularly among the poor

Predicting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. Here we investigated the relationship between year-to-year changes in satellite-derived estimates of fire activity in South America and sea surface temperature (SST) anomalies. We found that the Oceanic Ni o Index (ONI) was correlated with interannual fire activity in the eastern Amazon whereas the Atlantic Multidecadal Oscillation (AMO) index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model that predicted regional annual fire season severity (FSS) with 3-5 month lead times. Our approach provides the foundation for an early warning system for forecasting the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for mitigation of greenhouse gas and air pollutant emissions