Quantifying rainfall-induced climate risk in rainfed agriculture: A volatility-based time series study from semi-arid India

Small and marginal farmers in semi-arid India face vulnerabilities stemming from low agricultural productivity, degraded ecosystems, and market and climate uncertainties. Farmer producer organizations (FPOs) have emerged as collectives to address these challenges and enhance climate resilience. Despite their potential in promoting empowerment and inclusive decision-making, FPOs in India struggle to develop successful business plans and cope with extreme weather events. This study aimed to identify climate resilient development pathways (CRDPs) for FPOs in semi-arid India that can simultaneously address development and climate change concerns. We collaborated with four FPOs in Maharashtra and eight experts to co-create CRDPs for these FPOs. The CRDPs challenged existing visions; when confronted with data on increasing climate risks based on critical climate-stress moments, the FPOs included targeted climate resilience–building measures. The application of critical climate-stress moments for CRDP development is a methodological advancement, particularly in cases of contested goals, ambiguous vision, and multi-stakeholder involvement. Further, depending on their agricultural products and stage of development of climate resilience, FPOs initially prefer incremental adaptation measures over larger transformative changes. We recommend that current policies for promoting FPOs in India carefully consider the differences in types of FPOs and stage of development in climate resilience.

What shapes vulnerability and risk management in semi-arid India? Moving towards an agenda of sustainable adaptation

Climate risk, vulnerability and resilience: Supporting livelihood of smallholders in semiarid India

Community-based groundwater and ecosystem restoration in semi-arid north Rajasthan (3): Evidence from remote sensing

The recharge flow paths in a typical weathered hard-rock aquifer in a semi-arid area of southern India were investigated in relation to structures associated with a managed aquifer recharge (MAR) scheme. Despite the large number of MAR structures, the mechanisms of recharge in their vicinity are still unclear. The study uses a percolation tank as a tool to identify the input signal of the recharge and uses multiple measurements (piezometric time series, electrical conductivity profiles in boreholes) compared against heat-pulse flowmeter measurements and geochemical data (major ions and stable isotopes) to examine recharge flow paths. The recharge process is a combination of diffuse piston flow and preferential flow paths. Direct vertical percolation appears to be very limited, in contradiction to the conceptual model generally admitted where vertical flow through saprolite is considered as the main recharge process. The horizontal component of the flow leads to a strong geochemical stratification of the water column. The complex recharge pattern, presented in a conceptual model, leads to varied impacts on groundwater quality and availability in both time and space, inducing strong implications for water management, water quality evolution, MAR monitoring and longer-term socio-economic costs.

BackgroundInformation on life expectancy change is of great concern for policy makers, as evidenced by the discussions of the so-called "harvesting" issue (i.e. the question being, how large a loss each death corresponds to in the mortality results of time series studies).MethodsWhereas most epidemiological studies of air pollution mortality have been formulated in terms of mortality risk, this paper shows that a formulation in terms of life expectancy change is mathematically equivalent, but offers several advantages: it automatically takes into account the constraint that everybody dies exactly once, regardless of pollution; it provides a unified framework for time series, intervention studies and cohort studies; and in time series and intervention studies, it yields the life expectancy change directly as a time integral of the observed mortality rate.ResultsResults are presented for life expectancy change in time series studies. Determination of the corresponding total number of attributable deaths (as opposed to the number of observed deaths) is shown to be problematic. The time variation of mortality after a change in exposure is shown to depend on the processes by which the body can repair air pollution damage, in particular on their time constants. Hypothetical results are presented for repair models that are plausible in view of the available intervention studies of air pollution and of smoking cessation. If these repair models can also be assumed for acute effects, the results of cohort studies are compatible with those of time series.ConclusionThe proposed life expectancy framework provides information on the life expectancy change in time series studies, and it clarifies the relation between the results of time series, intervention, and cohort studies.

The purpose of this study is to decompose the observed changes in quantity demanded of cereals into its components-due to changes in income, price consumer taste and preferences and residual including other omitted variables. This will enable us to understand the relative importance of these components and evolve policies to modify the direction of changes in the demand far cereals. The main ingredients for this purpose are demand elasticities for cereals with respect to income, price and taste. These demand elasticities are estimated for each often Semi-Arid Tropical (SAT) states and all India using pooled time series of cross-sections data published by the National Sample Survey organization for the period 1972-1994. The empirical results show wide variation in demand elasticities across states, income groups and rural/urban sectors. The consumer taste and preferences captured through time trend variable is significant and negative for most of the individual states, as well as, all India. The negative effect of taste change has more than offset the positive effects of income and other variables in the model. This explains partly the decline in cereal demand over time despite rise in per capita income and fall in relative prices of cereals. The magnitudes of the effects are smaller in urban areas than rural with taste change becoming positive ill some cases. Coarse cereals exhibit negative demand responses with respect to income, own-price and taste variables, interestingly, in few cases - Andhra Pradesh. Karnataka, Madhya Pradesh and Maharashtra - consumer tastes seem to have changed in favour of -coarse cereal consumption. However the positive taste effect is nullified by negative income effect with the net result of decline in cereal demand The tests of nested hypotheses also confirm the need for inclusion of taste, income group dummies and their interaction variables in the model.

Problem statement: As a complement of the periodogram study the asymptotic properties of the spectral density using data window for stationary stochastic process are investigated. Some statistical properties of covariance estimation function with missing observations are studied. Approach: The asymptotic normality was discussed. A numerical example was discussed by using computer programming. Results: The study of time series with missed observations and with the modified periodogram had the same results of the study of the classic time series. Conclusion: Modified periodogram with expanded finite Fourier transformation for time series with missed observation has improved the results of the classic time series.

BackgroundRegression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision.MethodsWe derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario.ResultsIn single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data.ConclusionsPredicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low.

BackgroundInformation on life expectancy (LE) change is of great concern for policy makers, as evidenced by discussions of the "harvesting" (or "mortality displacement") issue, i.e. how large an LE loss corresponds to the mortality results of time series (TS) studies. Whereas loss of LE attributable to chronic air pollution exposure can be determined from cohort studies, using life table methods, conventional TS studies have identified only deaths due to acute exposure, during the immediate past (typically the preceding one to five days), and they provide no information about the LE loss per death.MethodsWe show how to obtain information on population-average LE loss by extending the observation window (largest "lag") of TS to include a sufficient number of "impact coefficients" for past exposures ("lags"). We test several methods for determining these coefficients. Once all of the coefficients have been determined, the LE change is calculated as time integral of the relative risk change after a permanent step change in exposure.ResultsThe method is illustrated with results for daily data of non-accidental mortality from Hong Kong for 1985 - 2005, regressed against PM10 and SO2 with observation windows up to 5 years. The majority of the coefficients is statistically significant. The magnitude of the SO2 coefficients is comparable to those for PM10. But a window of 5 years is not sufficient and the results for LE change are only a lower bound; it is consistent with what is implied by other studies of long term impacts.ConclusionsA TS analysis can determine the LE loss, but if the observation window is shorter than the relevant exposures one obtains only a lower bound.

When looking for a pattern of phytoplankton behaviour across trophic gradients, we need to cross the boundaries between different disciplinary areas, from autoecology to systems ecology, because eutrophication is a complex process which involves different time scales and different levels of community structure. Thus, we submit our observations to the muddled conceptual world of assemblage ecology. These inaccuracies arise, for example, from both species and community arguments; eutrophication as a fertilization or a metabolic phenomenon; and the notions frequently interwoven of pattern, process and rules. We suggest that it is advantageous to tackle this issue from the perspective of general ecology, rather than from a specifically planktonic orientation. In this way, useful general ecological tools, for example, time series and assembly-rule studies, can be used. Time-series study allows the dynamics of any variable to be described or to show that long term variable fluctuations may sometimes be unregulated, in response to some exogenous factor. Rules of assembly help us to resolve which traits are selectively involved during the eutrophication process. In this context, we advocate (1) the use of traits instead of morphospecies in phytoplankton studies, (2) looking for the dynamic patterns of phytoplankton with eutrophication, (3) the use of time series techniques to study phytoplankton trajectories, (4) the use of assembly rules to discern patterns in the formation of multispecies assemblages, (5) the consideration of the pelagic food-web in studies of phytoplankton dynamics and, as an overall suggestion, to borrow knowledge and inspiration from general ecology.

To the Editor: Recent papers in EPIDEMIOLOGY have reported on time-series studies com paring daily pollution levels and daily health events among a city or group of cities.2 However, such designs have a potential for uncontrolled/residual con founding that may not have been ade quately considered. Time-series studies are in nature. As Morgenstern3 suggests, ecological may be severe in prac tice and adjustment for extraneous risk factors may not reduce the ecologic bias and may even increase it. The weather variables included in time-series models are themselves crude surrogates for heat stress, and provide no information on important individual-level factors (eg, access to air conditioning). Such heat-related variables have a strong and nonlinear effect on health,4 which may not be adequately accounted for in linear time-series models. Perhaps most importantly, it is not clear that time-series studies frilly control for traditional confounding variables, such as smoking and stress. It is true that chronic risk factors such as smoking status and high blood pressure are not associated with daily pollution levels, and are there fore not important confounders in time series studies. However, these chronic variables are also not pertinent to the daily risks (eg, death or hospitalization) of in terest in time-series studies. That is to say, smoking status may predict someone's

Micro-phytoplankton at the equatorial Pacific (140°W) during the JGOFS EqPac Time Series studies: March to April and October 1992

The emigration of skilled professionals from low- and middle-income countries (LMICs) to high-income countries (HICs) is a general phenomenon but poses particular challenges in health care, where it contributes to human resource shortages in the health systems of poorer countries. However, little is known about the effects of strategies to help regulate this movement. To assess the effects of policy interventions to regulate emigration of health professionals from LMICs. We searched the Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register (searched 15 March 2011), the Cochrane Register of Controlled Trials (CENTRAL) (searched 2 March 2011), MEDLINE (searched 5 March 2011), EMBASE (searched 2 March 2011), CINAHL (searched 5 March 2011), LILACS (searched 7 March 2011), WHOLIS (searched 20 March 2011), SocINDEX (searched 11 March 2011), EconLit (searched 8 March 2011), Science and Social Science Citation Index (searched 8 March 2011), NLM Gateway (searched 31 March 2011) and ERIC (searched March 3 2011). We reviewed reference lists of included studies and selected reviews on the topic, contacted authors of included studies and experts on the field, and reviewed relevant websites. Randomised controlled trials (RCT), non-randomised controlled trials (NRCT), controlled before-and-after studies (CBA) and interrupted time series (ITS) studies assessing any intervention inthe source, the recipient or both countries that could have an impact on the number of professionals that emigrate from a LMIC. Health professionals, such as physicians, dentists, nurses or midwives, should be nationals of a LMIC whose graduate training was in a LMIC. One review author extracted data onto a standard form and a second review author checked data. Two review authors assessed risk of bias. Only one study was included. This time series study assessed the migration of Philippine nurses to the United States of America (USA) from 1954 to 1990. We re-analysed it as an interrupted time series study. The intervention was a modification of migratory law in the US, called the 'Act of October 1965', which decreased the restrictions on Eastern hemisphere immigrants to the USA. The analysis showed a significant immediate increase of 807.6 (95% confidence interval (CI) 480.9 to 1134.3) in the number of nurses migrating to the USA annually after the intervention. This represents a relative increase of5000% over the underlying pre-intervention trend. There were no significant differences in the slopes of the underlying trends for the number of nurses migrating between the pre- and postintervention periods. There is an important gap in knowledge about the effectiveness of policy interventions in either HICs or LMICs that could regulate positively the movement of health professionals from LMICs. The only evidence found was from an intervention in a HIC that increased the movement of health professionals from a LMIC.New initiatives to improve records on the migration of health professionals from LMICs should be implemented, as a prerequisite to conducting more rigorous research in the field. This research should focus on whether the range of interventions outlined in the literature could be effective in retaining health professionals in LMICs. Such interventions include financial rewards, career development and continuing education, improving hospital infrastructure, resource availability, better hospital management and improved recognition of health professionals.

Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. Three search filters were created in MEDLINE: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions. Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.