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Nitrous oxide (N 2 O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N 2 O emissions. Most national GHG reporting to the United Nations Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N 2 O pulses in the northcentral region of the United States during early spring and this increases annual N 2 O emissions from croplands and grasslands reported in the national GHG inventory by 6 to 16%. Considering this, emission accounting in cold climate regions is very likely underestimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N 2 O increases once CO 2 and CH 4 are phased out. Hence, the N 2 O emission underestimate introduces additional risks into meeting long-term climate goals.

Despite their importance for regional water resource planning and as indicators of climate change, records of in situ glacier mass balance remain short and spatially sparse in the Himalaya. Here, we present an updated series of in situ mass-balance measurements from Rikha Samba Glacier, Nepal, between 2011 and 2021. The updated in situ mass balance is −0.39 ± 0.32 m w.e. for this period. We use an energy-mass balance model to extend the annual mass-balance series back to 1974. The model is forced using daily meteorological variables from ERA5-Land reanalysis data that is linearly bias-corrected using observations from an automatic weather station situated near the glacier terminus. The modeled mass balance is consistent with the in situ mass-balance series measured 2011–2021 and with previous glaciological and geodetic estimates. The model results indicate a mass balance of −0.56 ± 0.27 m w.e. a−1 over the reconstruction period of 1974–2021, which is comparable to the mass losses experienced by other Himalayan glaciers during this time. An assessment of the sensitivity of the glacier mass balance to meteorological forcing suggests that a change in temperature of ±1 K has a stronger effect on the calculated mass balance compared to a ±20% change in either precipitation, or relative humidity, or solar radiation.

Nitrogen (N) loss through ammonia $$({\mathrm{NH}}_{3})$$ volatilization in agricultural soils is a significant source of atmospheric $${\mathrm{NH}}_{3}$$ , contributing to low N use efficiency in crops, risk to human health, environmental pollution, and is an indirect source of nitrous oxide $$({\mathrm{N}}_{2}\mathrm{O})$$ emissions. Our objective was to develop an ammonia volatilization method within the DayCent ecosystem model that incorporates key 4R N management practices (right type, right rate, right placement, and right timing) that influence $${\mathrm{NH}}_{3}$$ volatilization associated with application of urea-based nitrogen fertilizers to agricultural soils. The $${\mathrm{NH}}_{3}$$ volatilization method was developed with Bayesian calibration using sampling importance resampling methods and Bayes factors to select the level of complexity in the model that best represents $${\mathrm{NH}}_{3}$$ volatilization given the observed data. The final model included urea hydrolysis and the influence of urease inhibitors; short-term soil pH changes following fertilization; fertilizer incorporation into the soil (mechanically and through irrigation/precipitation); and specification of the fertilizer placement method (i.e. broadcast vs. banding and surface vs incorporated). DayCent predicts $${\mathrm{NH}}_{3}$$ volatilization with a root-mean-squared error of 158 (95% interval ranging from 133 to 192), bias of 7 (95% interval ranging from − 106 to 102) g NH3-N ha−1 day−1, and with a Bayesian R2 value of 0.39 (95% interval ranging from 0.17 to 0.62). Furthermore, the model incorporates key management options influencing $${\mathrm{NH}}_{3}$$ volatilization related to placement method and fertilizer type with and without urease inhibitors that can be used to evaluate management and policy options for reducing losses of NH3 from urea fertilization.

Agriculture soils are responsible for a large proportion of global nitrous oxide (N2O) emissions—a potent greenhouse gas and ozone depleting substance. Enhanced-efficiency nitrogen (N) fertilizers (EENFs) can reduce N2O emission from N-fertilized soils, but their effect varies considerably due to a combination of factors, including climatic conditions, edaphic characteristics and management practices. In this study, we further developed the DayCent ecosystem model to simulate two EENFs: controlled-release N fertilizers (CRNFs) and nitrification inhibitors (NIs) and evaluated their N2O mitigation potentials. We implemented a Bayesian calibration method using the sampling importance resampling (SIR) algorithm to derive a joint posterior distribution of model parameters that was informed by N2O flux measurements from corn production systems a network of experimental sites within the GRACEnet program. The joint posterior distribution can be applied to estimate predictions of N2O reduction factors when EENFs are adopted in place of conventional urea-based N fertilizer. The resulting median reduction factors were − 11.9% for CRNFs (ranging from −51.7% and 0.58%) and − 26.7% for NIs (ranging from −61.8% to 3.1%), which is comparable to the measured reduction factors in the dataset. By incorporating EENFs, the DayCent ecosystem model is able to simulate a broader suite of options to identify best management practices for reducing N2O emissions.

The direct or glaciological method is an integral part of international glacier monitoring strategies, and the mass balance is an essential variable to describe the climate system and model runoff. In 2011, we established two glacier mass balance programmes on Yala and Rikha Samba Glacier in the Nepal Himalaya. Here we present the methods and data that we ingested into the database of the World Glacier Monitoring Service. We present glacier length changes and the annual mass balances for the first six mass balance years for both glaciers. For Yala Glacier we additionally present the mass balance of seasonal in situ measurements and the mass balance from 2000 to 2012 analysed with the geodetic method. The annual mass balance rates of Yala Glacier from 2000 to 2012 and from 2011 to 2017 are −0.74 ± 0.53 m and −0.74 ± 0.28 m w.e. a−1, and for Rikha Samba Glacier from 2011 to 2017 −0.39 ± 0.32 m w.e. a−1. The mass loss for the period 2011 to 2017 for Yala and Rikha Samba Glacier −4.44 ± 0.69 m w.e. and −2.34 ± 0.79 m w.e., respectively. The winter balance of Yala Glacier is positive in every investigated year, but the negative summer balance determines the annual balance. The mass balance of Yala Glacier is more negative than on other glaciers in the region, mostly because of the small and low lying accumulation area. The mass balance of Rikha Samba Glacier is more positive than the other glaciers in the region, likely because of the large and high lying accumulation area. Due to the topography, the retreat rates of Rikha Samba Glacier are much higher than for Yala Glacier. From 1989 to 2013, Rikha Samba retreated 431 m (−18.0 m a−1), and from 1974 to 2016 Yala Glacier retreated 346 m (−8.2 m a−1). During the study period, a change of Yala Glacier's surface topography has been observed with glacier thinning and down wasting, which indicates the likely disappearance of Yala Glacier within this century. The datasets are freely accessible from WGMS (2020a): Fluctuations of Glaciers Database. World Glacier Monitoring Service, Zurich, Switzerland. http://dx.doi.org/10.5904/wgms-fog-2020-08.

Accurate estimation of crop net primary production (NPP) and yields is fundamental for regional analyses of agroecosystem dynamics using process‐based models. In this study, we simulated croplands in the contiguous U.S. using the DayCent ecosystem model with new production algorithms. Crops were divided into crop variety groups based on regional varieties of three major crops (corn, soybeans, and winter wheat) and generic parameter values that were generated for each group. These varieties have been developed through crop breeding programs and enhance production of major crop types in different temperature and precipitation regimes. NPP and yields for the three major crops were evaluated at the county level with reported yields from the National Agricultural Statistics Service (NASS). The predictions of the multiyear average yields in all counties were more accurate than most other published results using process‐based models. DayCent predictions of yields produced an overall R2 of 0.54, 0.54, and 0.38 for corn, soybean, and winter wheat, respectively, with predictions for most counties within ±20% of the NASS reported yields. Our estimations of the total annual NPP for the three crops in the contiguous U.S. are 0.24, 0.09, and 0.06 Pg C yr(−1) for corn, soybean, and winter wheat, respectively. Together, they contribute 7.3% to 14.8% of the total NPP for all vegetation in the contiguous U.S. We conclude that crop variety groups capture heterogeneity in NPP for major crop types and can improve biogeochemical model predictions of NPP for croplands.

Background: In 2014, approximately 462 million adults worldwide were underweight; while 1.9 billion were either overweight or obese. Hence, a study was carried out first time using the NFHS Standard of Living Index to assess the association between socioeconomic status and underweight & overweight among likely to conceive women.Material & Methods: In Determinants of Birth Weight (DBW): a community based prospective cohort study; household characteristic information was collected from 1293 likely to conceive women. Nutritional status of women was graded based on WHO international BMI categories. Chi-square test and multinomial logistic regression were applied to assess the association between standard of living and underweight & overweight with the 95% confidence level and p values less than 0.05.Results: The prevalence of underweight, normal weight, overweight and obesity was found 35.1%, 52.4%, 9.7% and 2.7%, respectively, with mean±SD BMI 20.4 kg/m2±3.876 kg/m2. The low standard of living was significantly associated with underweight whereas high standard of living was associated with overweight among likely to conceive women in a rural North Karnataka with a p value less than 0.05.Conclusion: Among likely to conceive women, there is an inverse association between standard of living and underweight whereas positive association with overweight in a rural North Karnataka.

Biofuels vary greatly in their carbon intensity, depending on the specifics of how they are produced. Policy frameworks are needed to ensure that biofuels actually achieve intended reductions in greenhouse gas emissions. Current approaches do not account for important variables during cultivation that influence emissions. Estimating emissions based on biogeochemical models would allow accounting of farm-specific conditions, which in turn provides an incentive for producers to adopt low emissions practices. However, there are substantial uncertainties in the application of biogeochemical models. This paper proposes a policy framework that manages this uncertainty while retaining the ability of the models to account for (and hence incentivize) low emissions practices. The proposed framework is demonstrated on nitrous oxide (N2O) emissions from the cultivation of winter barley. The framework aggregates uncertainties over time, which (1) avoids penalizing producers for uncertainty in weather, (2) allows for a high degree of confidence in the emissions reductions achieved, and (3) attenuates the uncertainty penalties borne by producers within a timescale of several years. Results indicate that with effective management, N2O emissions from feedstock cultivation may be 20% of the carbon intensity of gasoline. If these emissions reductions are monetized, the framework can provide up to $0.002 per liter credits (0.8 cents per gallon) to fuel producers, which could incentivize emissions mitigation practices by biofuel feedstock suppliers, such as avoiding fall N application on silty clay loam soils. The conservatism in the current approach fails to incentivize the adoption of biofuels, while the lack of specificity fails to incentivize site-level mitigation practices. Improved uncertainty accounting and consideration of farm-level practices will incentivize mitigation efforts at landscape to global scales.

PURPOSE: The purpose of the study was to compare the efficacy of arthrocentesis alone and arthrocentesis with duloxetine in patients with TMJ pain. MATERIALS AND METHODS: This study comprised of 20 patients. Patients with TMJ pain as confirmed by RDC/TMD and with pain duration of at least 3 months were included in the study. Patients were randomly divided into two groups, group A in which patients were treated with arthrocentesis alone and group B in which patients were treated with arthrocentesis followed by duloxetine 30 mg twice daily for 12-week therapy. Patients were followed up at regular interval at day 1, day 5, day 7, week 4, week 6 and week 12. Outcome assessment of pain, swelling, maximal mouth opening, painful/pain-free lateral or protrusive jaw movement was done. Hospital anxiety and depression (HAD) scale was used to assess pre- and post-treatment anxiety and depression. Radiologic assessment was done based on OPG and CBCT. Biochemical assessment of IL-6 in lavage fluid between groups A and B was done pre-operatively and postoperatively. RESULTS: There was significant reduction of pain in both groups, but reduction of pain was more in group B than in group A at week 4, week 6 and week 12. Mouth opening was significantly higher in group B than in group A patients. Reduced painful lateral and protrusive movements were seen in group B than in group A patients. There was no significant difference in hospital anxiety and depression scale among the groups at pre-operative and at 4th week post-operative. Though a significant reduction of IL-6 levels was seen post-operatively, there was no significant difference between the two groups. CONCLUSION: Arthrocentesis is an effective procedure for relieving symptoms in patients with temporomandibular disorders. In this study, combination of duloxetine with arthrocentesis gave much better outcome than arthrocentesis alone. Further studies with large sample size required to evaluate the effect of arthrocentesis plain or in combination with duloxetine in providing permanent relief to the patient.

Aim: The extent of methicillin-resistant Staphylococcus aureus (MRSA) infection in Nepalese children is largely unknown. Materials & methods: Six hundred and seventy-two clinical samples collected from 232 patients between June and November 2016 were processed in a microbiology laboratory. Results: Out of 300 culture-positive samples, 52 (17.3%) were S. aureus isolates. Among those 52, 39 (75.0%) were found to be MRSA. The infection rate of S. aureus was shown to be higher in inpatients (55.7%) compared with outpatients (44.3%) at p = 0.637, 95% CI. Thirteen types of antibiotics were used in the antibiotic susceptibility test. MRSA isolates showed 100 and 0% resistance to penicillin and vancomycin, respectively. The D-test showed inducible clindamycin-resistant phenotype in 15.4% of MRSA isolates. Conclusion: This demonstrates the utmost need for routine testing for MRSA in Nepalese hospitals., Lay abstract The problem of antibiotic resistance is rising worldwide. Methicillin antibiotic-resistant Staphylococcus aureus (i.e., MRSA) infection is one of the bacterial infections that contribute to increased health and economic burdens. An accurate understanding of the MRSA infection status in Nepalese children is not available to date. Thus, the current research was performed in a pediatric hospital. We also discovered that MRSA is not tested routinely and thus we recommend it should be monitored regularly, as with other bacterial infections.

Water efficiency measures and alternative supply sources alleviate peak water demand on urban water supply networks. Consequently, they also provide benefits to water service providers, in terms of augmentation deferrals and reduced sized infrastructure. However, while these benefits are acknowledged in the literature, they have not been thoroughly investigated and quantified. This paper empirically demonstrates how the installation of different potable water saving measures would affect the design of urban water supply networks. Peak day water demand profiles were developed for the baseline scenario, which represented the typical building code mandated for new dwellings constructed in Queensland, Australia, and for households fitted with water saving measures. The core novel feature of this study relates to the use of an innovative bottom-up approach to the development of demand profiles based on smart meters enabling comprehensive water end use datasets (i.e. demand in shower, tap, etc.) to be obtained. Hydraulic model runs were conducted for various water savings scenarios across different planning horizons to determine the scheduling of augmentations in a water supply study area. The results of the model runs showed deferred and eliminated augmentations, as well as reductions in infrastructure sizing for the water savings scenarios compared to the baseline scenario. Financial analysis (i.e. NPV) on trunk main augmentation requirements over 50 year asset life cycles indicated that savings of between $1,574,289 (11.4%) and $7,030,796 (51%) could be achieved by incorporating water efficiency and potable source substitution measures in new infill developments in the study region.

Essential service providers face unique challenges that are specific delivering a secure and safe water and energy supply to remote communities such as islands and isolated mainland townships. Many remote communities rely on energy intensive water supply systems which are inherently costly to operate. Water demand management programmes such as retrofitting households with water-efficient devices and appliances are one way of reducing the water-energy costs in these communities. This paper presents modelling results from a comparison between business as usual and a scenario where water-efficient strategies were retrofitted in households in three remote communities in Northern Australia. The modelling demonstrated considerable savings to both the water and energy average daily consumption and associated economic costs though reduced reliance on desalination plants and bore pumping. The retrofit scenario was shown to reduce water demand by between 14 and 39 ML/y and total energy demand in off-grid communities by between 83 and 208 MWh/y. Cost reductions for delivering treated water to households ranged between around AUD$11,000 and AUD$70,000 per year, depending on uptake rates of the retrofit programme by each community. This paper forms part of Stage 1 of the Remote and Isolated Communities Essential Services (RICES) project. Stage 2 will confirm many of the assumptions underlying the modelling and build on the smart metering datasets and community engagement process currently underway in Stage 1. The overall outcome of the RICES project is to ensure that such strategies are practical to implement on a broader scale to ultimately achieve more sustainable water and energy efficient off-grid communities.

Diversified water supply schemes can reduce both peak demand and overall demand in the urban water supply network. Consequently, they provide benefits to both the water utility and their customers including deferring network augmentations and reducing household water bills, respectively. However, the installation of different water saving scenarios also incurs additional costs which present a financial burden to the householder. This paper investigates the financial viability of installing alternative water supplies and water efficient appliances in a large scale area, taking into account both their benefits and incurred costs. Water demand profiles were developed for the baseline and various water saving scenarios for new dwellings in Queensland, Australia. Hydraulic model runs were conducted to determine system augmentation and pump power requirements for various water saving scenarios across different planning horizons in a water supply study area. The results of the modeling showed deferred augmentations and reduced pumping requirements for the water savings scenarios, compared to the baseline scenario; contributing to monetary savings to the utility. Cost benefits to the householders are from reduced mains water and energy consumption, with incurred costs from installing the water saving systems. A total net cost balance appraisal demonstrated monetary savings for the water efficient and rainwater tank scenarios while the greywater scenario produced negative net costs. The results are discussed along with incentives and potential savings to promote sustainable alternative water use in an urban area.

Increasingly, the water sector is exploring the value of applying demand management strategies to reduce peak water use through behavioural and technical solutions. Literature suggests that using behavioural interventions may be a useful approach in changing the daily peak demand patterns to reduce the pressure on network pumping energy costs during peak use times. There is a lack of studies, however, that have investigated the role of social based marketing or behavioural intervention studies on specifically reducing and shifting residential peak diurnal daily water end-use demand. This concept is modelled in this current study through the application of longitudinal experimental end-use data to predict how reduced demand through behaviour change can impact on overall peak residential demand. Notwithstanding the acknowledged limitations of the study, results illustrate a range of potential peak hour flow savings that can be realised from reducing total demand, or shifting the peak demand in households. The study provides preliminary evidence that water businesses can use demand-side strategies to also achieve efficiencies in the distribution of urban water (e.g. reduced energy for pumping in pressurised water system, pipe augmentation deferrals, peak energy demands).

Benefits of carbon sequestration in agricultural soils are well recognized, and process-based models have been developed to better understand sequestration potential. However, most studies ignore the uncertainty arising during model prediction—a critical requirement for scientific understanding, policy implementation and carbon emission trading. Furthermore, the dependencies created in process-based models due to many parameters and a relatively small set of empirical data hinder parameterization. We have implemented a Bayesian approach using the sampling importance resampling (SIR) method to calibrate the DayCent ecosystem model for estimating soil organic carbon (SOC) stocks, and to quantify uncertainty in model predictions. A SOC dataset compiled from 19 long-term field experiments, representing 117 combinations of management treatments, with 491 measurements of SOC, was split into independent datasets for model calibration and evaluation. The most important DayCent model parameters were identified through a global sensitivity analysis (GSA) for parameterization and SIR was used to calibrate the model and produce posterior distributions for the most sensitive parameters. On average, the Bayesian calibration reduced the model uncertainty by a factor of 6.6 relative to the uncertainty associated with the prior. The Bayesian model analysis framework will allow for ongoing updates to the model as new datasets and model structural improvements are made in future research, and overall provide a stronger basis for models to support policy and management decisions associated with GHG mitigation through C sequestration in agricultural soils.

Integrating alternative water supplies and water efficient appliances are proven strategies for reducing customers' average and peak demand on the cities mains water distribution infrastructure network. However, water utilities are yet to harness the potential of smart meters to build tailored daily diurnal demand patterns that reflect the consumption characteristics of individual developments that are increasingly applying alternative water sources and ultra-efficient appliances. This paper demonstrates how smart metering and water end-use data facilitates enhanced infrastructure planning of contemporary water supply schemes located in Queensland, Australia. To achieve research aims, extensive data received from high resolution smart meters fitted to a sample of 130 households was disaggregated into end use events (e.g. shower, dishwasher, etc.) and categorised into 48 thirty-minute time steps (i.e. daily diurnal demand) over the average day (AD) and peak day (PD). Moreover, these demand curves were also clustered according to water appliance stock efficiency ratings. Novel bottom-up end-use level models of demand were formulated representing scenarios of both single and multi-residential dwellings installed with combinations of water efficient appliances and rainwater tanks or greywater reuse. AD and PD diurnal demand patterns for these contemporary water scenarios were compared against the baseline scenario which represents the typical building code mandated dwelling constructed in the region of Queensland, Australia. Modelled demand curves for scenarios applying more efficient appliances in both dwelling types had lower peak demand than the baseline scenario by 13% and 6% for the AD and PD respectively, while scenarios also including rainwater tanks and greywater reuse (i.e. source substitution) resulted in peak reductions of 28% on the AD and 51% on the PD. The paper concludes with some implications for better handling the demand complexities of contemporary water supply schemes being developed by using smart meter data for the optimal planning of water infrastructure networks.

To design water distribution network infrastructure, water utilities formulate daily demand profiles and peaking factors. However, traditional methods of developing such profiles and peaking factors, necessary to carry out water distribution network modelling, are often founded on a number of assumptions on how top-down bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters allows for a new novel methodology for using the continuous ‘big data’ generated by these meter fleets to create evidence-based water demand curves suitable for use in network models. To demonstrate the application of the developed method, high resolution water consumption data from households fitted with smart water meters were collected from the South East Queensland and Hervey Bay regions in Australia. Average day (AD), peak day (PD) and mean day maximum month (MDMM) demand curves, often used in water supply network modelling, were developed from the herein created methodology using both individual end-use level and hourly demand patterns from the smart meters. The resulting modelled water demand patterns for AD, PD and MDMM had morning and evening peaks occurring earlier and lower main peaks (AD: 12%; PD: 20%; MDMM: 33%) than the currently used demand profiles of the regions’ water utility. The paper concludes with a discussion on the implications of widespread smart water metering systems for enhanced water distribution infrastructure planning and management as well as the benefits to customers.

Commercial buildings are central to cities and contribute significantly to the urban demand for natural resources, including freshwater. Green building benchmarking tools include more efficient water use as key indicator of sustainability. This paper explores options for substituting mains drinking water with an alternative, non-potable water source on a fit for purpose basis. The research findings are based on a monitoring study of a commercial building in Brisbane, Australia that is harvesting rainwater for meeting non-potable water demand. The results demonstrated that the system is only achieving moderate reliability in meeting demand due to operational problems. The case study analysis has highlighted the need to include validation and monitoring to ensure the system is operating as per design intent. The paper also investigates the potential of other local, non-potable water sources for high-rise commercial buildings, in particular air conditioning condensate and groundwater inflow to a basement wet well. The paper concludes by comparing the advantages and disadvantages of different local water sources which highlights the need to undertake a site specific investigation to identify a suitable alternative water source, which considers O&M complexity and the capacity of facilities management.

Communal rainwater tank systems provide an alternative urban water supply solution for reducing dependence on centralised water supply networks. Rainwater from household roofs is transported through a gravity collection system and stored in a centralised communal tank before being treated and supplied back to homes through a reticulated pumping system. Literature on the design, life cycle costing and economies of scale of communal rainwater tank systems is currently limited. This study intends to develop a methodology for the system design, assess the economies of scale of communal systems and identify the main cost contributors for the total capital and life cycle costs. A methodology developed for this analysis is presented for the benefit of water professionals across the globe to support similar studies in their local regions. Housing layouts were developed, designed and costed for a flat topography and a centralised storage and treatment scenario, ranging from 4 to 576 homes. An economic assessment was then carried out using the net present value method (NPV). The results show that costs of storage and treatment units are more influential for a group of households at lower scale, whilst the diseconomy of scale of pipes is a major cost factor for higher scale of household groups. An optimal scale was observed between 192 and 288 households and sensitivity analysis on the discount rate showed no changes within this range. A basic analysis showed that topography of the land does influence overall NPV. However, the influence factor depends on the nature of the slope, with costs varying for differing scenarios and further work required to have a thorough understanding of its influence in final NPV.

The community seed bank (CSB) is emerged as an effective rural institution at the community level to strengthen farmers’ access to diversity of crop genetic resources as well as seeds that contribute to local food security for poor farmers and also to ensure the conservation of agro-biodiversity on-farm. This paper presents some evidences from the community of western Nepal to demonstrate that CSB is one of the options to conserve and use agro-biodiversity on farm. Biodiversity Conservation and Development Committees (BCDCs) and farmers have established six CSBs in western terai with the support of Agrobiodiversity component of Western Terai Landscape Complex Project (WTLCP-ABD) in Bardia (1 CSB), Kanchanpur (2 CSBs) and Kailali (3 CSBs) districts. These CSBs are functional with prearranged community developed mechanisms for collection, conservation, replication and distribution of seeds. Communities have collected and conserved 88 (Belwa), 77 (Beldandi), 99 (Gadariya), 78 (Masuriya), 76 (Patharaiya) and 82 (Shankarpur) number of accessions of different crops in CSBs. CSB enhanced farmer’s seed system and strengthened social networks for exchange of seeds to cope with impacts of climatic adversity by providing immediate access to locally adapted germplasm as community based adaptation strategy. CSB is becoming a reliable local adaptation practice that minimizes the potential negative impacts of climate change in addition to accessibility, conservation and use of genetic resources in western terai of Nepal. The Journal of Agriculture and Environment Vol:12, Jun.2011, Page 132-139 DOI: http://dx.doi.org/10.3126/aej.v12i0.7573

Background: The duel burden of malnutrition is characterized by the coexistence of under-nutrition along with overweight, obesity or diet-related NCDs, within individuals, households and populations, and across the life-course. Hence a study carried out to identify the prevalence of underweight and overweight among likely to conceive women and its determinants. Method: Determinants of Birth Weight (DBW): a community based prospective cohort study at rural North Karnataka collected information from 1293 likely to conceive women on pretested interview schedule. Nutrition status of women was graded based on WHO international BMI categories. Chi Square test and multinomial logistic regression were applied to identify the determinants of underweight and overweight with the 95% confidence interval with P values less than 0.05. Result: The prevalence of underweight, normal weight, overweight and obesity was found 35.1%, 52.4%, 9.7% and 2.7% respectively with mean±SD BMI 20.4 kg/m2±3.876 kg/m2. The determinants of duel burden of underweight and overweight found in the study were current age of women, age at menarche, family type and education level of women and p value less than 0.05. Conclusion: Younger age group women, women from joint family and women with lower education level are more likely to suffer from both underweight and overweight in North Karnataka.

Vegetation indices derived from remote sensing data provide information about the variability in stature, growth and vigor of the vegetation across a region, and have been used to model plant processes. For example, the Enhanced Vegetation Index (EVI) provides a measure of greenness of the vegetation that can be used to predict net primary production. However, ecosystem models relying on remote sensing data for EVI or other vegetation indices are limited by the time series of the satellite data record. Our objective was to develop a statistical model to predict EVI in order to extend the time series for modeling applications. To explain the functional behavior of the seasonal EVI curves, a two-stage multiple regression fitting procedure within a semi-parametric mixed effect (SPME) model framework was used. First, a linear mixed effect (LME) model was fitted to the EVI with climate indexes, crop and irrigation information as predictor variables. Second, Penalized B-splines were used to explain the behavior of the smooth residuals, which result from a smooth model fit to the smooth EVI data curve, in order to describe the uncertainty of the EVI curve. Individual models were fit within individual Major Land Resources Areas (MLRAs). Predicted seasonal EVI, derived from our regression equations, showed a strong agreement with the observed EVI and was able to capture the site by site and year by year variation in the EVI curve. Out-of-sample prediction produced excellent results for a majority of the sites, except for sites without clear seasonal patterns, which may have resulted from cloud contamination and/or snow cover. Therefore, given the appropriate climate, crop, and irrigation information, the proposed approach can be used to predict seasonal EVI curves for extending the time series into the past and future.

Improved agricultural practices have a significant potential to mitigate greenhouse gas (GHG) emissions. A key issue for implementing mitigation options is quantifying emissions practically and cost effectively. Web-based systems using process-based models provide a promising approach.