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This paper provides an overview of how food production influences climate change and also illustrates the impact of climate change on food production. To perform such an overview, the (inter)link between different parts of the food supply chain continuum (agriculture production, livestock farming, food processing, food transport and storing, retail food, and disposal of food waste) and climate change has been investigated through a bibliometric analysis. Besides UN Sustainable Development Goal (SDG) 13, associated with climate change, other SDGs that are associated with this overview are goals #1, #2, #3, #6, #7, #12, and #15. Based on the evidence gathered, the paper provides some recommendations that may assist in efforts to reduce the climate-related impacts of food production.

Vegetated coastal ecosystems (VCE) display a promising potential to act as natural carbon sinks in climate change mitigation. Although growing interest in wetland carbon has intensified the global level carbon stock estimation studies, large knowledge gaps and uncertainties remain, particularly in tropical salt marshes in the South and Southeast Asian regions. Therefore, the current study aims to quantify the organic carbon stocks in the salt marsh habitats on the Northwest coast of Sri Lanka and to showcase the relevance of salt marsh carbon in local and regional contexts. Vegetation and soil up to a depth of 50 cm were sampled from four sites representing the Wedithalathive Nature Reserve (WNR). Species-specific allometric relationships developed for the major succulent halophytic species indicated a significant positive correlation between dry biomass and plant height. The loss-on-ignition (LOI) technique was applied in combination with a carbon conversion factor to calculate the soil organic carbon (SOC) content across 4 depth intervals. The study provided an average total organic carbon (TOC) storage of 73 ± 14.47 Mg C ha

Background: Climate change is a problem which is global in nature, and whose effects go across a wide range of disciplines. It is therefore important that this theme is taken into account as part of universities´ teaching and research programs. Methods: A three-tiered approach was used, consisting of a bibliometric analysis, an online survey and a set of case studies, which allow a profile to be built, as to how a sample of universities from 45 countries handle climate change as part of their teaching programs. Results: This paper reports on a study which aimed at identifying the extent to which matters related to climate change are addressed within the teaching and research practices at universities, with a focus on the training needs of teaching staff. It consists of a bibliometric analysis, combined with an online worldwide survey aimed at ascertaining the degree of involvement from universities in reducing their own carbon footprint, and the ways they offer training provisions on the topic. This is complemented by a set of 12 case studies from universities round the world, illustrating current trends on how universities handle climate change. Apart from reporting on the outcomes of the study, the paper highlights what some universities are doing to handle climate issues, and discusses the implications of the research. Conclusions: The paper lists some items via which universities may better educate and train their students on how to handle the many challenges posed by climate change., peer-reviewed

Soil salinity is known to be a significant threat to food security for the increasing population, which is further aggravated under the climate change scenario. Indo-Gangetic plain (IGP) is one of the most productive in the world and is most affected by salinity. To understand the modifications in soil characteristics under different management practices followed to reclaim salinity affected land, the present study was conducted at variously reclaimed saline areas of three districts of Uttar Pradesh situated in IGP. Soil from six sites (electrical conductivity (EC) ranging from 0.89 to 10.28 mS) following different management practices, RJT (Rajatalab, rice-wheat +organic), BBN (Beerbhanpur, rice-wheat +inorganic), MZM (Mirzamurad, rice-mustard +organic), BRP (Baraipur, rice-wheat +organic), DHR (Dharahara, rice-fallow +organic) and SLM (Salempur, rice-wheat +inorganic) were assessed for physical, chemical and biological properties during the vegetative stage and after harvest of crops. Soil quality index (SQI) based on representative parameters obtained by principal component analysis and yield of crops were also calculated at saline and non-saline sites. The SLM site showed highest salinity followed by BRP, DHR, MZM, while BBN and RJT were non-saline. Total organic carbon, total nitrogen, microbial activity, and microbial biomass were low at saline compared to non-saline sites but were higher under organic matter amendment compared to inorganic. Activities of soil enzymes were negatively influenced while β-glucosidase and alkaline phosphatase activities were enhanced under higher salinity. Organic amendments were more efficient in improving the soil properties along with SQI at saline soil resulting into a better yield in all crop combinations compared to inorganic amendments.

Grasslands grow in a sequence of seasonal growth stages that respond to both climate and weather, and these relationships can be used to establish a strategy for predicting plant phenology. Current plant states (phenophase) can be represented as one of established growth stages that dictate carbon allocation and leaf photosynthetic capacity. Calculating daily phenophases from climate and environmental relationships allows for sequential growth stages (i.e., well‐defined seasonal cycles with a single growth period) or dynamic growth stages (i.e., multiple growth periods during a growing season). Senescence results from biomass mortality in response to environmental conditions. This approach uses a single mechanistic framework to represent grassland ecology, removing the dependence on satellite‐based vegetation indices and individual site tuning of parameters. Rather than being specified, a variety of properties emerge, from allometric relationships such as root‐shoot ratios, to behavior across moisture gradients, to interannual variability in growing season lengths, carbon stores, and land surface fluxes. Using dynamic phenology stages to link biophysical and biogeochemical processes provides a mechanism to predict self‐consistent land‐atmosphere exchanges of carbon, water, energy, radiation, and momentum, as well as carbon storage in cascading pools of biomass; and describing these processes in a mathematically determinate model makes them clear, testable, and usable for predictions. This paper describes this new phenology method as it is implemented in the Simple Biosphere Model Version 4 (SiB4), and a companion paper evaluates this method at grassland sites worldwide.

The population sizes of tiger beetles are known to display fluctuations in response to several microclimatic and soil parameters. In this study, analytical models were developed using negative binomial regression to describe the effects of weather and soil parameters on the population count of selected tiger beetle species. The tiger beetle species Hypaetha biramosa was selected to represent the coastal habitats and Ifasina labioaenea represented crop cultivated habitats. Population sizes for each tiger beetle species were estimated by taking counts maximum of two times a month using visual encounter surveys. Three sites were selected for each species. At each site, weather and soil parameters were measured. Based on the statistical models, an increase in wind speed was associated significantly with the decline in the counts of H. biramosa in the study sites. Solar radiation and soil electric conductivity were associated with an increase in the count of H. biramosa. An increase in the soil salinity and air temperature was associated positively with the increase of the counts of I. labioaenea in the study sites. The understanding of the effects of these parameters can be used to design better monitoring and conservation plans for Sri Lankan tiger beetles.

Wetlands hold significant potential for climate change mitigation due to their high capacity to sequester atmospheric carbon dioxide (CO2). Colombo, Sri Lanka was recently declared as one of the eighteen global Ramsar wetland cities. The current study represents the first attempt to quantify soil organic carbon (SOC) stocks held by the urban freshwater wetlands in Colombo. The study focused on the extensive urban wetland ecosystems of Kolonnawa wetland and Thalawathugoda wetland park. SOC stocks were determined using three parameters: depth of soil, bulk density, and SOC concentration. Loss on ignition method was used in quantifying SOC concentrations. Average SOC stocks, up to a depth of 60 cm at Kolonnawa wetland and Thalawathugoda wetland park were estimated at 504 ± 14 t C/ha and 550 ± 23 t C/ha, respectively. Furthermore, the total SOC stock at Kolonnawa wetland and Thalawathugoda wetland park were estimated at 198,408 ± 5564 t CO2eq and 66,313 ± 2764 t CO2eq, respectively. When considering global estimates, it was found that freshwater wetlands in Colombo hold a higher SOC stock than tropical wet forests and tropical dry forests. The current study highlights the importance of urban ecosystems in mitigating the ever increasing concentrations of atmospheric CO2 .

The availability of low cost and long-lasting water pumping technology is a dream of every farmer. In Sri Lanka, fuel or electricity-powered water pumps are used to irrigate thousands of hectares of field crops, Vegetables, and fruits. Based on the International Water Management Institute (IWMI) survey conducted in the year 2000, there are about 50,000 agro-wells in the dry zone of Sri Lanka. More than 110,000 pumps are used to pump water from those wells. The fuel cost is the biggest burden for the farmers, which results in a high cost of production. The use of solar energy for water pumping is a promising alternative to conventional electricity and fuel-based water pumping systems. Solar-powered water pumping is based on photovoltaic (PV) technology that converts solar energy into electrical energy to run a DC or AC type water pump. This paper presents a comparative analysis of economic and environmental benefits associated with solar water pumping systems against fuel-based water pumping systems. The analyses were based on practical experience over 50 acres of land cultivated for export-oriented Green Cucumber by 100 members of Tempitiya Farmer Organization in Ampara District of Sri Lanka. It concluded that the solar water pumping system is advantageous compared to a fuel-based pump in terms of economic and environmental aspects.

Anthropogenic climate change has caused increased soil salinity in South Asia due to saltwater intrusion caused by sea level rise, input of fertilizers with high salt index, and irrigation malpractices, etc. Salinity has a multitude of impacts on plant and soil processes, leading to alterations in gas fluxes and rice productivity. The remedial measures adopted on salt-affected soils to reduce the salinity effect could enhance future climate change if they cause an increase in greenhouse gas (GHG) emissions. This study was conducted to find the best agricultural management practices (BMPs) for salt-affected soils in rice cropping systems (i.e. the major cropping system in Asia) in four South Asian countries (Sri Lanka, India, Bangladesh and Pakistan) considering net GHG emissions and other socioeconomic benefits associated with the adopted measures. The salinity-affected sites were selected based on available information (e.g. agricultural statistics and maps). Site-level measurements on soil parameters and GHG emissions were made in control- and managed plots and farmer surveys were conducted. Although organic amendments ameliorated salinity, it could cause a net increase in carbon dioxide or methane emissions depending on the soil conditions, particularly during the initial stages. This impact could be ameliorated by combining organic amendments with other management practices. In the Indo-Gangetic region, poor soil drainage causing anaerobic conditions favoured nitrous oxide emission under low to medium salinity. Yield losses and emissions in high salinity sites were controlled through organic amendment, irrigation and rice-fallow cropping sequence. The combination of transplanting of rice seedlings, the addition of organic matter, and intermittent irrigated water levels was identified as the BMP for Sri Lankan farmers. The outcome of this project will be used to raise awareness among farmers and policymakers.

The textile industry plays a critical role in the Sri Lankan economy. It is also a major consumer of energy and water and it is the fifth largest contributor to CO2 emissions in the world. With the increasing popularity of the concept of sustainability and the effect of the external and internal drivers, the textile industry of Sri Lanka has adopted more environmentally friendly production processes and uses sustainability as a marketing tool. This study was undertaken to evaluate the environmental and economic performances of three selected textile processing factories in the Biyagama Export Processing Zone, Sri Lanka. Eco-efficiency analyses were performed for each factory using the formula suggested by the World Business Council for Sustainable Development (WBCSD). Total production for the factories for five- year period (2011–2015) was considered as an indicator of product value. Indicators of environmental influence were selected by considering inputs (energy, material and water) and outputs (wastewater and greenhouse gas emission) associated with the production process. Sustainability measures undertaken by the factories within the study period were evaluated using the eco-efficiency change over time. The sustainability measures taken by each factory had a positive impact on their eco-efficiency. A correlation analysis considering eco-efficiency of different environmental factors and the revenue of the factories was performed. The study revealed that eco-efficiency influenced the production cost. The study also revealed a positive significant correlation between eco-efficiency and the factory level revenue.

The objective of this study was to analyze the greenhouse gas (GHG) emissions associated with energy usage in tea and rubber industries in Sri Lanka. The scope of the study covered GHG inventory analysis of carbon dioxide, methane and nitrous oxide emissions associated with tea and rubber products, considering a life cycle approach starting from the plantations to the processed products. The functional units of tea and rubber products were ‘one metric ton of tea’ and ‘one metric ton of dry rubber content (DRC)’ respectively. Total GHG emission ranged from 514.27 ± 68.66 (high-grown) to 603.10 ± 191.58 (Uva) kg CO2e ton−1 of made tea and the emissions were not statistically significant (p > 0.05; ANOVA and Tukey's multiple comparison tests) among the four tea growing regions, namely, high-grown, mid-grown, low-grown and Uva regions. The electricity consumption contributed 63% of the GHG emissions in tea industry and the rest of the emissions were from fossil fuel (23%) and biomass (14%) usage. GHG emissions associated with rubber plantations were 155.6 ± 47.4 kg CO2e per ton of DRC. GHG emissions in raw rubber processing factories of crepe and ribbed smoked sheet (RSS), centrifuged latex and technically specified rubber (TSR) were 168.6 ± 44.1, 125.1 ± 24.8 and 375.60 kg CO2e per ton of DRC respectively. GHG emissions associated with latex based and dry rubber based industries were 1472.1 ± 1011.8 and 1801.23 ± 360.26 kg CO2e per ton of DRC respectively. The main source of GHG emissions in rubber industry was the use of grid electricity. GHG emissions associated with electricity usage in rubber plantations, crepe and RSS, centrifuged latex, TSR, latex based and dry rubber based industries were 15%, 80%, 42%, 53%, 75% and 52% respectively and the remaining emissions were from diesel, biomass, and fuel oil usage. Fuel switching in the manufacture of dry rubber based products has contributed to mitigation of GHG emissions by about 80%.

© 2016, Springer International Publishing Switzerland. Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2seasonal uptake over agricultural regions.

The objective of this research was to identify the effects of antecedent rainfall on the probability of landslide occurrence and to analyze the knowledge of the community on Emergency Preparedness. In this study, recent major landslides events were studied. The rainfall relevant to landslide locations was extracted using a GIS-based interpolation. From the daily rainfall of two days prior to landslide event (i.e. Day 3) till the daily rainfall of landslide day (Day 1), there was an identifiable pattern. There is more than 90% chance for a landslide to occur, when the rainfall of Day 1, or the day of the landslide event, is close to 300 mm or above with a rainfall of ~80 mm or above on the previous day (Day 2); i.e. There is more than 94% chance for a landslide to occur when the total rainfall received on Day 1 and Day 2 exceeds 375 mm. The majority of the events (>94%) had a daily rainfall of ~80 mm or above on Day 3. The cumulative rainfall of the landslide day up to three days prior to the landslide event shows a significant pattern (Day 1-Day 4). Overall, when there is a rainfall event of ~80 mm or over, people need to be warned/cautioned about a potential landslide within the next three days, if the rainfall continues to be high. Social survey shows that during 2017, people were more prepared compared to 2003, but still there are many steps to be taken in terms of preparedness.

Corrigendum to “Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands” published in Biogeosciences, 6, 969–986, 2009 E. Lokupitiya1, S. Denning1, K. Paustian2,3, I. Baker1, K. Schaefer4, S. Verma5, T. Meyers6, C. J. Bernacchi7, A. Suyker5, and M. Fischer8 1Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA 2Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA 3Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA 4National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309, USA 5School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0978, USA 6NOAA/ARL/ATDD, Oak Ridge, TN 37830-2456, USA 7Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA 8Lawrence Berkeley National Laboratory, Environmental Energy Technologies Division, Atmospheric Science Department, Berkeley, CA 94720, USA

Carbon (C) added to soil as organic matter in crop residues and carbon emitted to the atmosphere as CO2 in soil respiration are key determinants of the C balance in cropland ecosystems. We used complete and comprehensive county-level yields and area data to estimate and analyze the spatial and temporal variability of regional and national scale residue C inputs, net primary productivity (NPP), and C stocks in US croplands from 1982 to 1997. Annual residue C inputs were highest in the North Central and Central and Northern Plains regions that comprise ~70% of US cropland. Average residue C inputs ranged from 1.8 (Delta States) to 3.0 (North Central region) Mg C ha−1 year−1, and average NPP ranged from 3.1 (Delta States) to 5.4 (Far West region) Mg C ha−1 year−1. Residue C inputs tended to be inversely proportional to the mean growing season temperature. A quadratic relationship incorporating the growing season mean temperature and total precipitation closely predicted the variation in residue C inputs in the North Central region and Central and Northern Plains. We analyzed the soil C balance using the crop residue database and the Introductory Carbon Balance regional Model (ICBMr). Soil C stocks (0–20 cm) on permanent cropland ranged between 3.07 and 3.1 Pg during the study period, with an average increase of ~4 Tg C year−1, during the 1990s. Interannual variability in soil C stocks ranged from 0 to 20 Tg C (across a mean C stock of 3.08 ± 0.01 Pg) during the study period; interannual variability in residue C inputs varied between 1 and 43 Tg C (across a mean input of 220 ± 19 Tg). Such interannual variation has implications for national estimates of CO2 emissions from cropland soils needed for implementation of greenhouse gas (GHG) mitigation strategies involving agriculture.

The North China Plain is one of the most important crop production regions in China. However, water resources in the area are limited. Accurate modeling of water consumption and crop production in response to the changing environment is important. To better describe the two-way interactions among climate, irrigation, and crop growth, the crop phenology and physiology scheme of the SiBcrop model was coupled with the Simple Biosphere model version 2 (SiB2) for simulating crop phenology, as well as the crop production and evapotranspiration of winter wheat and summer maize, two of the main crops in the region. In the coupled model, the Leaf Area Index (LAI) produced by the crop phenology and physiology scheme was used in estimating the sub-hourly energy and carbon fluxes. Observations obtained from two typical eddy covariance sites located in this region were used to validate the model. The coupled model was able to simulate carbon and energy fluxes, soil water content, biomass carbon, and crop yield with high accuracy, especially for the latent heat flux and carbon flux. The LAI was also well-simulated by the model. Therefore, the coupled model is capable of assessing the responses of water resources and crop production to the changes of future climate and irrigation schedules.

Crop biomass and residue production are major components of cropland carbon dynamics that can be estimated using yield data from ground-based surveys. In the USA, surveyed yield data are available at county level and have been widely used for various research, economic and policy purposes, in addition to biomass estimation. However, survey data may be unavailable for certain times and/or locations and thus biomass estimates using remotely sensed data might be used to fill in any missing biomass data for estimating residue production and carbon dynamics in croplands. Compared to ground-based surveys, remotely sensed data are collected on a regular schedule and may also provide more spatially resolved data. We analysed composite biweekly Normalized Difference Vegetation Index (NDVI) data obtained using the Advanced Very High Resolution Radiometer (AVHRR) sensor and crop aboveground biomass (AGBM) estimated from available county-level yield data reported by the National Agricultural Statistics Service (NASS) for three crops (corn, soybean and oats) during 1992, 1997 and 2002. The aim of the study was to explore the relationships between NDVI and crop biomass to complete the missing biomass data in counties where no NASS-reported yields are available for biomass estimation. AGBM was estimated from Pathfinder biweekly NDVI, using canonical correlation analysis (CCA) and best subset multiple regressions incorporating canonical variates from NDVI time series. Cross-validation of model estimates was performed by randomly splitting the dataset into training and application subsets, simulating a 10-40% range of missing values. NDVI and crop biomass in Iowa during a given year were well correlated, with coefficient of determination (R2) values > 0.8 in most cases. Using the available (training) data from a single year or a combination of years to derive models for filling the missing (validation) data within the same time period yielded a mean estimated biomass with < 1% relative error and bias. However, models applied to out-of-sample years had lower (< 0.4) R2 values for the relationships between biomass and NDVI, although the mean residuals were low.

Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phenology and physiology is important in land-atmosphere carbon models being used to predict these exchanges. To better estimate time-varying exchanges of carbon, water, and energy of croplands using the Simple Biosphere (SiB) model, we developed crop-specific phenology models and coupled them to SiB. The coupled SiB-phenology model (SiBcrop) replaces remotely-sensed NDVI information, on which SiB originally relied for deriving Leaf Area Index (LAI) and the fraction of Photosynthetically Active Radiation (fPAR) for estimating carbon dynamics. The use of the new phenology scheme within SiB substantially improved the prediction of LAI and carbon fluxes for maize, soybean, and wheat crops, as compared with the observed data at several AmeriFlux eddy covariance flux tower sites in the US mid continent region. SiBcrop better predicted the onset and end of the growing season, harvest, interannual variability associated with crop rotation, day time carbon uptake (especially for maize) and day to day variability in carbon exchange. Biomass predicted by SiBcrop had good agreement with the observed biomass at field sites. In the future, we will predict fine resolution regional scale carbon and other exchanges by coupling SiBcrop with RAMS (the Regional Atmospheric Modeling System).

Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses. We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.

Tiger Beetles are one of the most studied caraboid groups in the world which includes highly predacious active adults and sedentary larvae. Over 2300 species of tiger beetles have been described and are found worldwide except in Antarctica, the Arctic and very remote isolated oceanic islands. The Indian subcontinent is reported to have a rich diversity of tiger beetles with over 150 described species belonging to a single genus Cicindela (sensu lat.) . Sri Lanka is considered as a global hot spot of tiger beetle diversity with 32 species out of the 55 recorded species being endemic to the country despite the smaller land extent compared to other countries of the Indian subcontinent. The recorded species belong to thirteen genera of which sub genus Ifasina consists of eight species of which the majority are endemic to Sri Lanka . Cylindera ( Ifasina ) waterhousei Horn 1900 is an endemic tiger beetle species which belongs to sub-genus Ifasina Jeannel, 1946. Examination of past distribution records available from 1984 to 2011 indicates the occurrence of C. waterhousei from six locations in the western Province and sabaragamuwa Province-kitulgala, Kegalle district, sabaragamuwa Province (1979); Karawanella, Kegalle district, sabaragamuwa Province (1979); Awissawella, Colombo district, western Province (1979); Labugama, Colombo district, western Province (1979); Ratnapura district, sabaragamuwa Province (1979), Bopath Ella, Ratnapura District, Sabaragamuwa Province (2003). In the present study conducted in August 2014, fifteen specimens of this species were collected from sandy banks of a tributary of Kalu Ganga in Ingiriya and Domabagaskanda Forest Reserve in Kalutara district where it has not been recorded before despite the extensive surveys conducted in the past. Further, specimens were also collected from Bopath Ella, Ratnapura district. This is the first time that C. waterhousei has been recorded from Kalutara district even though the species has been recorded in other locations of the western Province. Sampling was conducted by visual encounter surveys (VES) along 100 m transect. During 2003 to 2010 period this endemic species was reported only from the sandy banks of Bopath Ella in Ratnapura District. These new records indicate a possible range expansion for the species and contribute largely to the knowledge on distribution of C. waterhousei in Sri Lanka. Keywords: Tiger Beetles, Endemic, Cylindera ( Ifasina ) waterhousei, Kalutara district

Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.

Methane emission from enteric fermentation is a subcategory considered under the Agriculture sector greenhouse gas emissions under the United Nations Framework Convention on Climate Change (UNFCCC). So far, most non-annex 1 member countries have used the default IPCC 1996 revised guidelines in estimating such emissions. The latest IPCC 2006 revised guidelines for national greenhouse gas inventories provide better guidelines for developing country-specific emission factors. Countryspecific methane emission factors were developed for estimating methane emission from enteric fermentation in livestock in Sri Lanka, using a Tier 2 approach with energy calculations (IPCC, 2006) based on country-specific activity data. Each animal category was further subdivided for the analyses, and emission estimates incorporated population characteristics in the different agro-climatic zones of the country. Separate emission factors were calculated for sub categories within the local (indigenous) and improved (Indian and European) breeds. The population-weighted methane emission factor (50 kg head-1 yr-1) estimated for dairy cows (i.e. lactating females) was lower than the IPCC default value. The methane emission factors calculated for other cattle categories ranged between 11 – 58 kg head-1 yr-1. However the population-weighted emission factor (32 kg head-1 yr-1) was slightly higher than the IPCC default value (27 kg head-1 yr-1), as the proportion of the calves in the other cattle population was relatively lower (~47 percent) than what the IPCC default emission factor has considered. Overall, the population-weighted emission factors for buffaloes (49 kg head-1 yr-1) and sheep (3 kg head-1 yr-1) were lower than the IPCC default values.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2005 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-_ve di_erent associated projects were spawned across _ve U.S. agencies over the course of nearly a decade involving hundreds of researchers. The primary objective of the project was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 exchange over the major croplands of the U.S. Statistics from densely monitored crop production, consisting primarily corn and soybeans, provided the backbone of a well-studied\bottom up"flux estimate that was used to evaluate the atmospheric inversion results. Three different inversion systems, representing spatial scales varying from high resolution mesoscale, to continental, to global, coupled to different transport models and optimization techniques were compared to the bottom up" inventory estimates. The mean annual CO2-C sink for 2007 from the inversion systems ranged from 120 TgC to 170 TgC, when viewed across a wide variety of inversion setups, with the best" point estimates ranging from 145 TgC to 155 TgC. Inversion-based mean C sink estimates were generally slightly stronger, but statistically indistinguishable,from the inventorymore » estimate whose mean C sink was 135 TgC. The inversion results showed temporal correlations at seasonal lengths while week to week correlations remained low. Comparisons were made between atmospheric transport yields of the two regional inversion systems, which despite having different influence footprints in space and time due to differences in underlying transport models and external forcings, showed similarity when aggregated in space and time.« less

International audience; Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

International audience; [1] Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Human conversion of natural ecosystems to croplands modifies not only the exchange of water and energy between the surface and the atmosphere, but also carbon fluxes. To investigate the impacts of crops on carbon fluxes and resulting atmospheric CO 2 concentrations in the mid-continent region of the United States, we coupled a crop-specific phenology and physiology scheme for corn, soybean and wheat to the coupled ecosystem–atmosphere model SiB3–RAMS. Using SiBcrop–RAMS improved carbon fluxes at the local scale and had regional impacts, decreasing the spring uptake and increasing the summer uptake over the mid-continent. The altered fluxes changed the mid-continent atmospheric CO 2 concentration field at 120 m compared to simulations without crops: concentrations increased in May and decreased >20 ppm during July and August, summer diurnal cycle amplitudes increased, synoptic variability correlations improved and the gradient across the mid-continent region increased. These effects combined to reduce the squared differences between the model and high-precision tower CO 2 concentrations by 20%. Synoptic transport of the large-scale N–S gradient caused significant day-to-day variability in concentration differences measured between the towers. This simulation study shows that carbon exchange between crops and the atmosphere significantly impacts regional CO 2 fluxes and concentrations. DOI: 10.1111/j.1600-0889.2010.00485.x

Summary Because of anthropogenic activities, atmospheric deposition of nitrogen has increased on some high elevation ecosystems in North America. On Elk Mountain in SE Wyoming, USA, we found that ice, formed by the impaction of supercooled cloud droplets, contains nitrogen levels (NH 4 + = 58 ± 47 μmole/1, NO 3 − = 52 ± 40 μmole/1) significantly higher than snow (NH 4 + = 7 ±5 μmole/1, NO 3 − = 14 ± 7 μmole/1). At this site the impaction process (riming) occurs on krummholz and is an important mechanism of water and nutrient deposition. We sampled nematodes in alpine soil for two seasons under this rime ice deposition and in adjacent meadow and krummholz soil with only snow deposition. No significant difference was found in nematode density and trophic composition between snow and rime ice deposition zones in krummholz; and nematode densities were significantly higher in meadow soil than in the krummholz rhizosphere. Densities of active nematodes were highest immediately after snow melt and a positive correlation was found between nematode density and percent soil moisture. With subsequent soil drying, nematodes gradually entered anhydrobiosis and this process began earlier in the exposed meadow than under the krummholz canopy. In a subsequent microcosm experiment we exposed nematodes in two alpine soils to four different nitrogen treatments [ammonium nitrate (NH 4 NO 3 ) at 0, 20, 40 and 80 kg N/ha/yr] and two temperatures (5 and 25°C). None of the three variables had a significant effect on nematode density. But at the first sampling (1 month) the interaction between soil type and nitrogen level was significant, and at the second and final sampling (6 months) the interaction between soil type and temperature was significant. Nematode response to increasing nitrogen deposition rates varied in a complex way with soil type and temperature. Under the microcosm conditions, nematode community composition shifted to opportunistic rhabditid species. Our work indicates that higher levels of nitrogen deposition (>80 kg N/ha/yr) were necessary to produce detectable effects on nematode density in this alpine region.