Temporal Variance Research Articles

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.

Identifying subsoil sediment sources with carbon and nitrogen stable isotope ratios

AbstractIncreased sediment loads from accelerated catchment erosion significantly degrade waterways worldwide. In the South East Queensland region of Australia, sediment loads are degrading Moreton Bay, a Ramsar listed wetland of international significance. In this region, like most parts of coastal Australia, sediment is predominantly derived from gully and channel bank erosion processes. A novel approach is presented that uses carbon and nitrogen stable isotope ratios and elemental composition to discriminate between these often indistinguishable subsoil sediment sources. The conservativeness of these sediment properties is first tested by examining the effect of particle size separation (testing for consistency during transport) and the effect of sampling at different times (testing for temporal source consistency). The discrimination potential of these sediment properties is then assessed with the conservative properties, based on the particle size and temporal analyses, modelled to determine sediment provenance in three catchments. Nitrogen sediment properties were found to have significant particle size enrichment and high temporal variance indicative of non‐conservative behaviour. Conversely, carbon stable isotopes had very limited particle size and temporal variability highlighting their suitability for sediment tracing. Channel erosion was modelled to be a significant source of sediment (μ 51%, σ 9%) contrasting desktop modelling research that estimated gully erosion is the predominant sediment source. To limit the supply of sediment to Moreton Bay, channel bank and gully erosion must both be targeted by sediment management programs. By distinguishing between subsoil sediment sources, this approach has the potential to enhance the management of sediment loads degrading waterways worldwide. Copyright © 2014 John Wiley & Sons, Ltd.

The orbital period of the V Sge star candidate QU Carinae★

Close Binary Supersoft X-ray Sources (CBSS) are considered strong candidates to SN Ia progenitors, but very few CBSS are known in our Galaxy. The galactic counterparts of the CBSS may be the V Sge stars, not detected in X-rays due to the strong absorption by the interstellar gas. Nevertheless, the number of members in the V Sge class is still small. In order to find new members we selected QU Car for photometric and spectroscopic observations. The orbital period of this system was published in the literature as 10.9 h, determined from radial velocity data taken in 1979-1980, but posterior analysis of data taken in 2006-2007 did not confirm this period. We analysed the high variability of its emission line profiles with the Temporal Variance Spectrum (TVS) technique. Besides, we recovered the 10.9 h orbital period from the radial velocities of the He II 4686 A emission line and, for the first time, detected what may be the orbital modulation in the photometric data. This photometric modulation is present only in the lower brightness state data, when the flickering is attenuated. The inclusion of QU Car in the V Sge class is supported by many features like high/low states, strong winds, nebular lines and He II 4686 A/Hbeta line ratios. However, the non-detection of the characteristic O VI 3811-34 A lines in its spectrum claims against this classification. These lines, though, may be highly variable so additional spectra analysed with the TVS technique can, possibly, solve this question.

Relationships between biodiversity and the stability of marine ecosystems: Comparisons at a European scale using meta-analysis

The relationship between biodiversity and stability of marine benthic assemblages was investigated through meta-analyses using existing data sets (n=28) covering various spatial (m–km) and temporal (1973–2006; ranging from 5 to >250months) scales in different benthic habitats (emergent rock, rock pools and sedimentary habitats) over different European marine systems (North Atlantic and western Mediterranean). Stability was measured by a lower variability in time, and variability was estimated as temporal variance of species richness, total abundance (density or % cover) and community structure (using Bray–Curtis dissimilarities on species composition and abundance). Stability generally decreased with species richness. Temporal variability in species richness increased with the number of species at both quadrat (<1m2) and site (~100m2) scales, while no relationship was observed by multivariate analyses. Positive relationships were also observed at the scale of site between temporal variability in species richness and variability in community structure with evenness estimates. This implies that the relationship between species richness or evenness and species richness variability is slightly positive and depends on the scale of observation. Thus, species richness does not stabilize temporal fluctuations in species number, rather species rich assemblages are those most likely to undergo the largest fluctuations in species numbers and abundance from time to time. Changes within community assemblages in terms of structure are, however, generally independent of biodiversity. Except for sedimentary and rock pool habitats, no relationship was observed between temporal variation of total abundances and diversity at either scale. Overall, our results emphasize that the relation between species richness and species-level measures of temporal variability depends on scale of measurements, type of habitats and the marine system (North Atlantic and Mediterranean) considered.

Patterns of phenotypic correlations among morphological traits across plants and animals.

Despite the long-standing interest of biologists in patterns of correlation and phenotypic integration, little attention has been paid to patterns of correlation across a broad phylogenetic spectrum. We report analyses of mean phenotypic correlations among a variety of linear measurements from a wide diversity of plants and animals, addressing questions about function, development, integration and modularity. These analyses suggest that vertebrates, hemimetabolous insects and vegetative traits in plants have similar mean correlations, around 0.5. Traits of holometabolous insects are much more highly correlated, with a mean correlation of 0.84; this may be due to developmental homeostasis caused by lower spatial and temporal environmental variance during complete metamorphosis. The lowest mean correlations were those between floral and vegetative traits, consistent with Berg's ideas about functional independence between these modules. Within trait groups, the lowest mean correlations were among vertebrate head traits and floral traits (0.38-0.39). The former may be due to independence between skull modules. While there is little evidence for floral integration overall, certain sets of functionally related floral traits are highly integrated. A case study of the latter is described from wild radish flowers.

Spatial and Temporal Variance Analysis of Water Quality Purification in Linluo Artificial Wetland

This paper investigated whether differences exist in the surface water quality when the wetland was under the management of different organizations through factor analysis. Factor scores suggest that overall there is a regular reduction of nutrient sources BOD, COD, TKN, TN, NH3-N, NO2-N and fecal pollution in this wetland. The nutrient content is the highest at the water inlet and gradually decreases over densely planted zone I, the open water zone, densely planted zone II and the discharge orifice, reaching the minimum level at the outlet. The influence factors of suspended solids TB and SS exhibit irregular variation in the different water bodies. Cluster analysis is conducted based on water quality using data collected between 2008 and 2012. Results reveal that regardless of wet or dry seasons, similar water quality is observed in the open water zone and densely planted zone II. Water quality at the inlet resembles that in densely planted zone I.

Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990–2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation.

Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia.

Recent studies at the cellular and regional levels have pointed out the multifaceted importance of neural synchronization and temporal variance of neural activity. For example, neural synchronization and temporal variance has been shown by us to be altered in patients in the vegetative state (VS). This finding nonetheless leaves open the question of whether these abnormalities are specific to VS or rather more generally related to the absence of consciousness. The aim of our study was to investigate the changes of inter- and intra-regional neural synchronization and temporal variance of resting state activity in anesthetic-induced unconsciousness state. Applying an intra-subject design, we compared resting state activity in functional magnetic resonance imaging (fMRI) between awake versus anesthetized states in the same subjects. Replicating previous studies, we observed reduced functional connectivity within the default mode network (DMN) and thalamocortical network in the anesthetized state. Importantly, intra-regional synchronization as measured by regional homogeneity (ReHo) and temporal variance as measured by standard deviation (SD) of the BOLD signal were significantly reduced in especially the cortical midline regions, while increased in the lateral cortical areas in the anesthetized state. We further found significant frequency-dependent effects of SD in the thalamus, which showed abnormally high SD in Slow-5 (0.01-0.027 Hz) in the anesthetized state. Our results show for the first time of altered temporal variance of resting state activity in anesthesia. Combined with our findings in the vegetative state, these findings suggest a close relationship between temporal variance, neural synchronization and consciousness.

Spatially distributed characterization of hyporheic solute transport during baseflow recession in a headwater mountain stream using electrical geophysical imaging

Summary The transport of solutes along hyporheic flowpaths is recognized as central to numerous biogeochemical cycles, yet our understanding of how this transport changes with baseflow recession, particularly in a spatially distributed manner, is limited. We conducted four steady-state solute tracer injections and collected electrical resistivity data to characterize hyporheic transport during seasonal baseflow recession in the H.J. Andrews Experimental Forest (Oregon, USA). We used temporal moment analysis of pixels generated from inversion of electrical resistivity data to compress time-lapse data into descriptive statistics (mean arrival time, temporal variance, and temporal skewness) for each pixel. A spatial visualization of these temporal moments in the subsurface at each of five 2-D transects perpendicular to the stream was interpreted to inform transport processes. As baseflow recession progressed we found increasing first arrival times, persistence, mean arrival time, temporal variance, and coefficient of variation, and decreasing skewness. These trends suggest that changes in hydrologic forcing alter the relative influence of transport phenomena (e.g., advection vs. other transport processes such as dispersion) along flowpaths. Spatial coverage obtained from electrical resistivity images allowed for qualitative comparison of spatial patterns in temporal moments both at an individual cross-section as well as between cross sections. We found that geomorphologic controls (e.g., bedrock confinement vs. gravel wedge deposits) resulted in different distributions and metrics of hyporheic transport. Results of this study provide further evidence that hyporheic transport is highly variable both in space and through the baseflow recession period. Geophysical images differentiate advection-dominated flowpaths from those that are more affected by other transport processes (e.g., dispersion, mobile-immobile exchange).

Adaptive weighted fusion with new spatial and temporal fingerprints for improved video copy detection

In this paper, we propose a new and novel modality fusion method designed for combining spatial and temporal fingerprint information to improve video copy detection performance. Most of the previously developed methods have been limited to use only pre-specified weights to combine spatial and temporal modality information. Hence, previous approaches may not adaptively adjust the significance of the temporal fingerprints that depends on the difference between the temporal variances of compared videos, leading to performance degradation in video copy detection. To overcome the aforementioned limitation, the proposed method has been devised to extract two types of fingerprint information: (1) spatial fingerprint that consists of the signs of DCT coefficients in local areas in a keyframe and (2) temporal fingerprint that computes the temporal variances in local areas in consecutive keyframes. In addition, the so-called temporal strength measurement technique is developed to quantitatively represent the amount of the temporal variances; it can be adaptively used to consider the significance of compared temporal fingerprints. The experimental results show that the proposed modality fusion method outperforms other state-of-the-arts fusion methods and popular spatio-temporal fingerprints in terms of video copy detection. Furthermore, the proposed method can save 39.0%, 25.1%, and 46.1% time complexities needed to perform video fingerprint matching without a significant loss of detection accuracy for our synthetic dataset, TRECVID 2009 CCD Task, and MUSCLE-VCD 2007, respectively. This result indicates that our proposed method can be readily incorporated into the real-life video copy detection systems.

Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness

The richness of conscious experience is thought to scale with the size of the repertoire of causal brain states, and it may be diminished in anesthesia. We estimated the state repertoire from dynamic analysis of intrinsic functional brain networks in conscious sedated and unconscious anesthetized rats. Functional resonance images were obtained from 30-min whole-brain resting-state blood oxygen level-dependent (BOLD) signals at propofol infusion rates of 20 and 40 mg/kg/h, intravenously. Dynamic brain networks were defined at the voxel level by sliding window analysis of regional homogeneity (ReHo) or coincident threshold crossings (CTC) of the BOLD signal acquired in nine sagittal slices. The state repertoire was characterized by the temporal variance of the number of voxels with significant ReHo or positive CTC. From low to high propofol dose, the temporal variances of ReHo and CTC were reduced by 78% ± 20% and 76%± 20%, respectively. Both baseline and propofol-induced reduction of CTC temporal variance increased from lateral to medial position. Group analysis showed a 20% reduction in the number of unique states at the higher propofol dose. Analysis of temporal variance in 12 anatomically defined regions of interest predicted that the largest changes occurred in visual cortex, parietal cortex, and caudate-putamen. The results suggest that the repertoire of large-scale brain states derived from the spatiotemporal dynamics of intrinsic networks is substantially reduced at an anesthetic dose associated with loss of consciousness.

Temporal variability of forest communities: empirical estimates of population change in 4000 tree species.

Long-term surveys of entire communities of species are needed to measure fluctuations in natural populations and elucidate the mechanisms driving population dynamics and community assembly. We analysed changes in abundance of over 4000 tree species in 12 forests across the world over periods of 6-28 years. Abundance fluctuations in all forests are large and consistent with population dynamics models in which temporal environmental variance plays a central role. At some sites we identify clear environmental drivers, such as fire and drought, that could underlie these patterns, but at other sites there is a need for further research to identify drivers. In addition, cross-site comparisons showed that abundance fluctuations were smaller at species-rich sites, consistent with the idea that stable environmental conditions promote higher diversity. Much community ecology theory emphasises demographic variance and niche stabilisation; we encourage the development of theory in which temporal environmental variance plays a central role.

Slow Feature Analysis for Change Detection in Multispectral Imagery

Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods.

Valued learning experiences of early career and experienced high-performance coaches

Background and purpose: This paper attempts to move the discussion of high-performance coach development from an examination of coaches' volume of experiences towards a consideration of the contribution of the learning experiences that coaches have reported throughout their careers. Furthermore, a discussion of proximal and distal guidance in the development of coaches was investigated. We examined the kinds of learning experiences within the framework of workplace learning and specifically the situated nature of learning and the view that learning occurs through social participation.Method: Nineteen high-performance coaches participated in this study, including 10 scholarship and 9 mentor coaches (MCs). Participants rated a list of 14 developmental activities derived from empirical research on a seven-point Likert scale (0 = not used, 1 = of little value, to 7 = extremely valuable). Each participant coach rated the 14 (guided, unguided) activities in the first two years of their coaching career, middle two years, and final two years. To analyse the data and identify the key trends for both the scholarship and MCs we examined statistical differences between scores for each of the sources the non-parametric Friedman test was used (p < .01). Significant χ2 results were followed up with the Wilcoxon (two-tailed) T-test (p < .05) to identify statistically significant differences between scores at different time intervals.Results: Three key findings emerged from these data: (i) reported increased valuing of a range of developmental experiences over time; (ii) temporal variance in the value of different learning sources at different stages of their careers; and (iii) an acknowledgement of the shift away from an emphasis on proximal learning sources for the MC and the shift towards proximal sources for the scholarship coach.

Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.

Text-Based Twitter User Geolocation Prediction

Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author’s location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain “location indicative words”. We then evaluate the impact of non-geotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability. We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems.

Density-Dependent Regulation of Brook Trout Population Dynamics along a Core-Periphery Distribution Gradient in a Central Appalachian Watershed

Spatial population models predict strong density-dependence and relatively stable population dynamics near the core of a species' distribution with increasing variance and importance of density-independent processes operating towards the population periphery. Using a 10-year data set and an information-theoretic approach, we tested a series of candidate models considering density-dependent and density-independent controls on brook trout population dynamics across a core-periphery distribution gradient within a central Appalachian watershed. We sampled seven sub-populations with study sites ranging in drainage area from 1.3–60 km2 and long-term average densities ranging from 0.335–0.006 trout/m. Modeled response variables included per capita population growth rate of young-of-the-year, adult, and total brook trout. We also quantified a stock-recruitment relationship for the headwater population and coefficients of variability in mean trout density for all sub-populations over time. Density-dependent regulation was prevalent throughout the study area regardless of stream size. However, density-independent temperature models carried substantial weight and likely reflect the effect of year-to-year variability in water temperature on trout dispersal between cold tributaries and warm main stems. Estimated adult carrying capacities decreased exponentially with increasing stream size from 0.24 trout/m in headwaters to 0.005 trout/m in the main stem. Finally, temporal variance in brook trout population size was lowest in the high-density headwater population, tended to peak in mid-sized streams and declined slightly in the largest streams with the lowest densities. Our results provide support for the hypothesis that local density-dependent processes have a strong control on brook trout dynamics across the entire distribution gradient. However, the mechanisms of regulation likely shift from competition for limited food and space in headwater streams to competition for thermal refugia in larger main stems. It also is likely that source-sink dynamics and dispersal from small headwater habitats may partially influence brook trout population dynamics in the main stem.

Automatic Task Assistance for People with Cognitive Disabilities in Brushing Teeth - A User Study with the TEBRA System

People with cognitive disabilities such as dementia and intellectual disabilities tend to have problems in coordinating steps in the execution of Activities of Daily Living (ADLs) due to limited capabilities in cognitive functioning. To successfully perform ADLs, these people are reliant on the assistance of human caregivers. This leads to a decrease of independence for care recipients and imposes a high burden on caregivers. Assistive Technology for Cognition (ATC) aims to compensate for decreased cognitive functions. ATC systems provide automatic assistance in task execution by delivering appropriate prompts which enable the user to perform ADLs without any assistance of a human caregiver. This leads to an increase of the user’s independence and to a relief of caregiver’s burden. In this article, we describe the design, development and evaluation of a novel ATC system. The TEBRA (TEeth BRushing Assistance) system supports people with moderate cognitive disabilities in the execution of brushing teeth. A main requirement for the acceptance of ATC systems is context awareness: explicit feedback from the user is not necessary to provide appropriate assistance. Furthermore, an ATC system needs to handle spatial and temporal variance in the execution of behaviors such as different movement characteristics and different velocities. The TEBRA system handles spatial variance in a behavior recognition component based on a Bayesian network classifier. A dynamic timing model deals with temporal variance by adapting to different velocities of users during a trial. We evaluate a fully functioning prototype of the TEBRA system in a study with people with cognitive disabilities. The main aim of the study is to analyze the technical performance of the system and the user’s behavior in the interaction with the system with regard to the main hypothesis: is the TEBRA system able to increase the user’s independence in the execution of brushing teeth?

Coping With Climate Change: a Food Policy Approach1

The early drafts of Food Policy Analysis were stimulated by the attention to high food prices following the world food crisis in 1973–74, and the fears of a repeat in 1979–80. But by the fourth full draft, in 1982, it became apparent that surpluses were returning to world food markets. A volume predicated on a world running out of food would have been out of date before the ink was dry, and a full-scale revamping of the analytical messages was needed. After a nearly complete re-write, the new theme, which has stood the test of 30 years of market fluctuations, was the need for flexibility to cope with market instability. That message is even more relevant now, as we learn to cope with a new source of instability—climate change. Such flexibility is not a natural feature of domestic policymaking, in the food sector or elsewhere, and providing the analytical tools for understanding how to create flexible responses turned out to be a real challenge. The task in this paper is to ask specifically how climate change would alter the basic message of Food Policy Analysis. Virtually all of the analysis was focused on national policies and domestic markets, an approach that seems problematical for preventing or mitigating climate change, but entirely appropriate for designing adaptation strategies. Climate change is imposing itself as a reality via the increased probability of extreme weather events in general, and also on both global and localized food security outcomes in particular. The ecosystem services provided by the climate are essential for all agricultural production. The most important effects of climate change on agriculture are likely to include a net global loss of agricultural land, changing crop suitability, an increase in the frequency of natural disasters, and greater temporal and geographic variance in production. It will also have negative effects on other areas of agriculture broadly interpreted—reducing the carrying capacity of many rangelands and posing threats to fisheries and aquaculture production systems. Climate change is expected to have highly variable effects on different regions; tropical and equatorial regions will bear the heaviest burdens, with some gains in yields and land availability in temperate regions. Since rural poverty is concentrated in tropical and, in South Asia, coastal areas, climate change is expected to have a disproportionate effect on the already vulnerable. The challenge is to design, analyze, and implement in-country “climate-smart agriculture” adaptation projects and programs, which are now part of the food policy agenda, as well as to improve the openness to trade in agricultural commodities to even out geographical instability. Designing appropriate policies for bio-fuels also needs to be on the analytical agenda.

When is the Right Time? A Little Later! Delayed Responses Show Better Temporal Control

When is the Right Time? A Little Later! Delayed Responses Show Better Temporal Control