Assessing California's bar-built estuaries using the California Rapid Assessment Method

Bar-built estuaries are generally found at the mouths of smaller watersheds with seasonal precipitation, episodic streamflow and seasonal swell dynamics. Low streamflows and constructive wave forces form a sand bar at the mouth isolating the stream from the ocean, creating a ponded lagoon, and inundating the surrounding marsh plain. Bar-built estuaries are wide spread in California comprising over 50 percent of California's more than 500 estuaries. By connecting terrestrial, freshwater, and marine realms bar-built estuaries are complex and dynamic providing great habitat and ecosystem services. California has suffered some of the highest losses of wetland habitats, and the wetland habitats of bar-built estuaries continue to be threatened by further development, pollution, and climate related changes including diminished streamflows and sea level rise. Given this loss and threat we developed a California Rapid Assessment Method (CRAM) to assess the condition of California's bar-built estuaries. CRAM uses visual indicators to accurately reflect current wetland condition with regards to buffer habitat, hydrology, physical complexity, plant diversity and structure, and landscape influences. Here we validate this method by comparing results of CRAM for bar-built estuaries to other accepted measures of wetland condition that we simultaneously collected with CRAM including vegetative surveys, water nutrient levels, and GIS landscape scale measures of stress for 32 sites throughout California. CRAM correlated well with each of these three independent methods of assessing condition. Notably, the Environmental Monitoring and Assessment Program (EMAP) number of natives metric significantly correlated with CRAM Index and all Attribute scores. The strong correlations of CRAM to nutrient levels is particularly important considering the documentation of the negative impact of nutrients on fish populations, the use of bar-built estuaries by juveniles of commercially important species, and the nursery role of bar-built estuaries for maintaining imperiled populations of species such as steelhead. GIS measured percent impervious, percent agriculture, and percent dams all correlated well with expected CRAM Attribute scores at appropriate watershed scales relative to the area of inference for each CRAM metric. Further, CRAM for bar-built estuaries works well throughout California's diversity of environmental conditions and regardless of geography, timing, or whether the bar was open or closed during the survey. We hope that the availability of CRAM combined with available data repositories will enable local, state, and federal decision makers to better manage, restore, and conserve valuable bar-built estuaries in the face of continual threats like development, drought, and sea level rise.     

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data

The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R² = 0.63 for the handheld GPS devices and R² = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development.     

N fate and transport under variable cropping history and fertilizer rate on loamy sand and clay loam soils: I. Calibration of the LEACHMN model

The need for efficient use of agricultural chemicals and their potential adverse impact on critical water resources have increased the use of simulation models of the soil and plant system. Nevertheless, there is currently little or no agreement concerning model validity and applicability in varied soils and environments. The research version of LEACHMN (the N subroutine of LEACHM) was calibrated using field data including soil physical, hydraulic, and chemical properties, and maize (Zea mays L.) N uptake collected from a 3-yr nitrate leaching experiment. The field site consisted of plot-size lysimeters on clay loam and loamy sand soils with N fertilizer rates of 22, 100 and 134 kg N ha^–1. The calibration involved adjusting nitrification, denitrification, and volatilization rate constants to optimize the fit between predicted and measured data. When calibrated for each treatment-year combination and soil type, the model simulations of soil profile NO₃–N distribution were generally successful. The N transformation rate constants yielded by the calibration efforts were similar or close to those used in other model simulation studies. At both sites, the calibrated rate constants for the first year (following sod plowdown) were different from those for the subsequent two years. Denitrification rate constants were consistently higher for the clay site than for the sand site, while the nitrification rate constants were lower. N rate of application appeared not to affect the rate constants within each year-site combination, suggesting that cropping history and soil type had the greatest effect on N transformation rates.     

Assessing the impact of RDP agri-environment measures on the use of nitrogen-based mineral fertilizers through spatial econometrics: The case study of Emilia-Romagna (Italy)

Agriculture is the main source of nitrogen loading (EEA, 2012) and is the sector with the largest residual emission reduction potential (Sutton et al., 2011). Moreover, surpluses of nitrogen are forecast to grow in the next decade (FAO, 2012). The objective of this study is to evaluate the determinants of the use of N-based mineral fertilizers in agriculture and the effectiveness of Agri-Environmental Schemes (AES) implemented in Emilia-Romagna, Italy, in preventing nitrate pollution.The indicator N mineral fertilizer application rate in agriculture was first estimated at the municipality level for the years 2000 and 2010. Thereafter, we performed a Moran's statistics and a LISA (Local Indicators of Spatial Association; Anselin, 1995) analysis to test the data for local spatial autocorrelations. Finally, in order to provide a quantitative evaluation of the impact of the agri-environmental measures on the application rate of N fertilizers, we constructed an aspatial model (Ordinary Least Square model) and two spatial models (spatial lag and error models). All of the models are able to explain more than 70% of the change in the N mineral fertilizer application rate between the years 2000 and 2010 (the dependent variable).The results indicate that the observed decrease in the application rate of N mineral fertilizers in the considered decade was positively influenced by both the uptake of specific AES and the location in Nitrate Vulnerable Zones (NVZ). Among the policy variables, the participation in AES is less important than the location in a NVZ for explaining the reduction in the N mineral fertilizer application rate in the municipalities of Emilia-Romagna. Other significant variables are farm size, population density and share of certified organic surface in the utilized agricultural area (UAA). The availability of finer scale data for the estimation of changes in nitrogen inputs would improve the robustness of the models.     

An Internet-based simulation model for nitrogen management in agricultural settings

Complex chemical, physical, and biological processes mediate nitrogen (N) transformations and movement during agricultural production, making the optimization of fertilizer use and environmental protection exceedingly difficult. Various computer models have been developed to simulate the site-specific fate and transport of N resulting from different crop production scenarios, but these models are very complex and difficult to use for most farmers, consultants, and conservationists. In an effort to facilitate access and simplify the use of sophisticated models, the U.S. Department of Agriculture (USDA) has developed an Internet-based nitrogen analysis tool. Based on the Nitrate Leaching and Economic Analysis Package (NLEAP), the Web site allows a user to conduct multiyear N simulation modeling specific to a crop field. Servers handle much of the required data assembly and formatting, thus sparing the user"s resources. Model runs are executed on the servers and the results are transmitted to the user. This new tool is presented along with early implementation results.     

Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

Quantitative genetic analyses have been increasingly used to estimate the genetic basis of life‐history traits in natural populations. Imperfect detection of individuals is inherent to studies that monitor populations in the wild, yet it is seldom accounted for by quantitative genetic studies, perhaps leading to flawed inference. To facilitate the inclusion of imperfect detection of individuals in such studies, we develop a method to estimate additive genetic variance and assess heritability for binary traits such as survival, using capture–recapture (CR) data. Our approach combines mixed‐effects CR models with a threshold model to incorporate discrete data in a standard ‘animal model’ approach. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data from a wild population of blue tits (Cyanistes caeruleus) and present the first estimate of heritability of adult survival in the wild. In agreement with the prediction that selection should deplete additive genetic variance in fitness, we found that survival had low heritability. Because the detection process is incorporated, capture–recapture animal models (CRAM) provide unbiased quantitative genetics analyses of longitudinal data collected in the wild.     

Critical role of collapsin response mediator protein-associated molecule CRAM for filopodia and growth cone development in neurons

Collapsin response mediator proteins (CRMPs) have been implicated in signaling of axonal guidance, including semaphorins. We have previously identified a unique member of this gene family, CRMP-associated molecule CRAM (CRMP-5), which is phylogenetically divergent from the other four CRMPs. In this study, we have examined the distribution and function of CRAM in developing neurons. Immunohistochemical analysis showed accumulation of CRAM in the filopodia of growth cones. Experiments using cytochalasin D indicated that filopodial localization of CRAM was independent of filamentous actin. Overexpression of CRAM in neuronal cells significantly promoted filopodial growth and led to the formation of supernumerary growth cones, which acquired resistance to semaphorin-3A stimulation. Finally, knockdown of CRAM by using RNA interference blocked filopodial formation and revealed an aberrant morphology of growth cones. We propose that CRAM regulates filopodial dynamics and growth cone development, thereby restricting the response of growth cone to repulsive guidance cues.     

Grasshoppers (Orthoptera: Acrididae) select vegetation patches in local‐scale responses to foliar nitrogen but not phosphorus in native grassland

Abstract Key elements such as nitrogen (N) and phosphorus (P) are often limiting relative to the nutritional needs of herbivores that feed on them. While N often limits insect herbivores in natural terrestrial ecosystems, the effect of P is poorly studied in the field, even though compelling hypotheses from the ecological stoichiometry literature predict its importance. We evaluated small‐scale spatial distributions of, and herbivory by, grasshoppers among neighboring plots that vary in foliar‐N and ‐P in tallgrass prairie. Grasshopper densities were 67% greater in N‐fertilized plots but detected no effect to grasshopper densities from P‐fertilizer. Leaf damage to the dominant grass Andropogon gerardii was 32% greater in N‐fertilized plots, but no response to foliar‐P was detected. Herbivore damage to a common forb, goldenrod (Solidago missouriensis), was not strongly linked by fertilizer treatments, although there was increased leaf damage in N‐fertilizer treatments when no P was applied (a significant N × P interaction). Under field conditions at local scales, we conclude that spatially heterogeneous distributions of grasshoppers are primarily affected by foliar‐N in host plants with little evidence that P‐levels contribute to the spatial patterns.     

Sequence requirements for trafficking of the CRAM transmembrane protein to the flagellar pocket of African trypanosomes

CRAM is a cysteine-rich acidic transmembrane protein, highly expressed in the procyclic form of Trypanosoma brucei. Cell surface expression of CRAM is restricted to the flagellar pocket of trypanosomes, the only place where receptor mediated endocytosis takes place in the parasite. CRAM can function as a receptor and was hypothesized to be a lipoprotein receptor of trypanosomes. We study mechanisms involved in the presentation and routing of CRAM to the flagellar pocket of insect- and bloodstream-form trypanosomes. By deletional mutagenesis, we found that deleting up to four amino acids from the C terminus of CRAM did not affect the localization of CRAM at the flagellar pocket. Shortening the CRAM protein by 8 and 19 amino acids from the C terminus resulted in the distribution of the CRAM protein in the endoplasmic reticulum (ER) (the CRAM protein is no longer uniquely sequestered at the flagellar pocket). This result indicates that the truncation of the CRAM C terminus affected the transport efficiency of CRAM from the ER to the flagellar pocket. However, when CRAM was truncated between 29 and 40 amino acids from the C terminus, CRAM was not only distributed in the ER but also located to the flagellar pocket and spread to the cell surface and the flagellum. Replacing the CRAM transmembrane domain with the invariant surface glycoprotein 65-derived transmembrane region did not affect the flagellar pocket location of CRAM. These results indicate that the CRAM cytoplasmic extension may exhibit two functional domains: one domain near the C terminus is important for efficient export of CRAM from the ER, while the second domain is of importance for confining CRAM to the flagellar pocket membrane.     

A computer model to simulate family history of breast/ovarian cancer in BRCA1 mutation carriers

The BRCA1 gene and its relationship to family history of breast/ovarian cancer are difficult to study in a population because of practical and ethical issues. The paucity of information on BRCA1 in the general population was a major theme in a recent review of genetic testing in Canada. We develop a simulation model to mimic genetic inheritance and cancer incidence in the family of someone with a germline BRCA1 mutation. Given someone's age and family structure, our model simulates his or her family history in three steps: (1) determine which family members have the mutation, (2) determine the ages of family members and (3) determine which family members have breast/ovarian cancer. Each step involves random variation. Some parameters in our model are estimated using local (British Columbia, Canada) population data. The breast/ovarian cancer risk associated with BRCA1 mutations is estimated using values published in the literature. An example is provided to illustrate the model's application. The model incorporates results from genetics, demography and epidemiology, but requires several additional assumptions. Research to address these assumptions is recommended.     

Using ecosystem CO2 measurements to estimate the timing and magnitude of greenhouse gas mitigation potential of forest bioenergy

Forest bioenergy opportunities may be hindered by a long greenhouse gas (GHG) payback time. Estimating this payback time requires the quantification of forest‐atmosphere carbon exchanges, usually through process‐based simulation models. Such models are prone to large uncertainties, especially over long‐term carbon fluxes from dead organic matter pools. We propose the use of whole ecosystem field‐measured CO₂ exchanges obtained from eddy covariance flux towers to assess the GHG mitigation potential of forest biomass projects as a way to implicitly integrate all field‐level CO₂ fluxes and the inter‐annual variability in these fluxes. As an example, we perform the evaluation of a theoretical bioenergy project that uses tree stems as bioenergy feedstock and include multi‐year measurements of net ecosystem exchange (NEE) from forest harvest chronosequences in the boreal forest of Canada to estimate the time dynamics of ecosystem CO₂ exchanges following harvesting. Results from this approach are consistent with previous results using process‐based models and suggest a multi‐decadal payback time for our project. The time for atmospheric carbon debt repayment of bioenergy projects is highly dependent on ecosystem‐level CO₂ exchanges. The use of empirical NEE measurements may provide a direct evaluation of, or at least constraints on, the GHG mitigation potential of forest bioenergy projects.     

Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N₂O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N₂O emissions at the country scale by aggregating all crops, under the assumption that N₂O emissions are linearly related to N application. However, field studies and meta‐analyses indicate a nonlinear relationship, in which N₂O emissions are relatively greater at higher N application rates. Here, we apply a super‐linear emissions response model to crop‐specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N₂O emissions from croplands. We estimate 0.66 Tg of N₂O‐N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N₂O emissions range from 20% to 40% lower throughout sub‐Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N₂O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N₂O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high‐resolution N application data are critical to support accurate N₂O emissions estimates.     

冬小麦高产优质高效栽培决策模拟研究

在产量和品质形成规律及投入产出效益规律基础上,应用模糊集方法,构建了一个综合的冬小麦高产优质高效栽培优化决策模拟模型.并以Window XP为平台,采用VB(Visual Basic)语言编程,建立了相应的可视化决策支持系统.实现了在不同时空、自然、社会、经济、技术条件下,进行多目标、可综合、可选择、可调控的冬小麦田间水肥管理决策的目标.通过检验证明模拟模型是可行的.     

Soil nitrous oxide emissions from a typical semiarid temperate steppe in inner Mongolia: effects of mineral nitrogen fertilizer levels and forms

Nitrous oxide (N₂O) emissions can be significantly affected by the amounts and forms of nitrogen (N) available in soils, but the effect is highly dependent on local climate and soil conditions in specific ecosystem. To improve our understanding of the response of N₂O emissions to different N sources of fertilizer in a typical semiarid temperate steppe in Inner Mongolia, a 2-year field experiment was conducted to investigate the effects of high, medium and low N fertilizer levels (HN: 200 kg N?ha^-1y^-1, MN: 100 kg N ha^-1y^-1, and LN: 50 kg N ha^-1y^-1) respectively and N fertilizer forms (CAN: calcium ammonium nitrate, AS: ammonium sulphate and NS: sodium nitrate) on N₂O emissions using static closed chamber method. Our data showed that peak N₂O fluxes induced by N treatments were concentrated in short periods (2 to 3 weeks) after fertilization in summer and in soil thawing periods in early spring; there were similarly low N₂O fluxes from all treatments in the remaining seasons of the year. The three N levels increased annual N₂O emissions significantly (P?<?0.05) in the order of MN > HN > LN compared with the CK (control) treatment in year 1; in year 2, the elevation of annual N₂O emissions was significant (P?<?0.05) by HN and MN treatments but was insignificant by LN treatments (P?>?0.05). The three N forms also had strong effects on N₂O emissions. Significantly (P?<?0.05) higher annual N₂O emissions were observed in the soils of CAN and AS fertilizer treatments than in the soils of NS fertilizer treatments in both measured years, but the difference between CAN and AS was not significant (P?>?0.05). Annual N₂O emission factors (EF) ranged from 0.060 to 0.298% for different N fertilizer treatments in the two observed years, with an overall EF value of 0.125%. The EF values were by far less than the mean default EF proposed by the Intergovernmental Panel on Climate Change (IPCC).     

The role of N2O derived from crop-based biofuels, and from agriculture in general, in Earth's climate

In earlier work, we compared the amount of newly fixed nitrogen (N, as synthetic fertilizer and biologically fixed N) entering agricultural systems globally to the total emission of nitrous oxide (N(2)O). We obtained an N(2)O emission factor (EF) of 3-5%, and applied it to biofuel production. For 'first-generation' biofuels, e.g. biodiesel from rapeseed and bioethanol from corn (maize), that require N fertilizer, N(2)O from biofuel production could cause (depending on N uptake efficiency) as much or more global warming as that avoided by replacement of fossil fuel by the biofuel. Our subsequent calculations in a follow-up paper, using published life cycle analysis (LCA) models, led to broadly similar conclusions. The N(2)O EF applies to agricultural crops in general, not just to biofuel crops, and has made possible a top-down estimate of global emissions from agriculture. Independent modelling by another group using bottom-up IPCC inventory methodology has shown good agreement at the global scale with our top-down estimate. Work by Davidson showed that the rate of accumulation of N(2)O in the atmosphere in the late nineteenth and twentieth centuries was greater than that predicted from agricultural inputs limited to fertilizer N and biologically fixed N (Davidson, E. A. 2009 Nat. Geosci. 2, 659-662.). However, by also including soil organic N mineralized following land-use change and NO(x) deposited from the atmosphere in our estimates of the reactive N entering the agricultural cycle, we have now obtained a good fit between the observed atmospheric N(2)O concentrations from 1860 to 2000 and those calculated on the basis of a 4 per cent EF for the reactive N.     

Prediction of nitrogen responses of corn by soil nitrogen mineralization indicators

Soil nitrogen mineralization potential (N min) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. The study objectives were to assess the spatial variability in soil N min potential based on clay and organic matter (OM) contents and the impact of grouping soils using these criteria on corn grain (Zea mays L.) yield, N uptake response curves to N fertilizer, and soil residual N. Four indicators were used: OM content and three equations involving OM and clay content. The study was conducted on a 15-ha field near Montreal, Quebec, Canada. In the spring 2000, soil samples (n = 150) were collected on a 30- x 30-m grid and six rates of N fertilizer (0 to 250 kg N ha(-1)) were applied. Kriged maps of particle size showed areas of clay, clay loam, and fine sandy loam soils. The N min indicators were spatially structured but soil nitrate (NO3-) was not. The N fertilizer rate to reach maximum grain yield (N max), as estimated by a quadratic model, varied among textural classes and Nmin indicators, and ranged from 159 to 250 kg N ha(-1). The proportion of variability (R2) and the standard error of the estimate (SE) varied among textural groups and N min indicators. The R2 ranged from 0.53 to 0.91 and the SE from 0.13 to 1.62. Corn grain N uptake was significantly affected by N fertilizer and the pattern of response differed with soil texture. For the 50 kg N ha(-1) rate, the apparent N min potential (ANM) was significantly larger in the clay loam (122 kg ha(-1)) than in the fine sandy loam (80 kg ha(-1)) or clay (64 kg ha(-1)) soils. The fall soil residual N was not affected by N fertlizer inputs. Textural classes can be used to predict N max. The N min indicators may also assist the variable rate N fertilizer inputs for corn production.     

Identification of CRAM, a novel unc-33 gene family protein that associates with CRMP3 and protein-tyrosine kinase(s) in the developing rat brain

Four members of collapsin response mediator proteins (CRMPs) are thought to be involved in the semaphorin-induced growth cone collapse during neural development. Here we report the identification of a novel CRMP3-associated protein, designated CRAM for CRMP3-associated molecule, that belongs to the unc-33 gene family. The deduced amino acid sequence reveals that the CRAM gene encodes a protein of 563 amino acids, shows 57% identity with dihydropyrimidinase, and shows 50-51% identity with CRMPs. CRAM appears to form a large complex composed of CRMP3 and other unidentified proteins in vivo. Indeed, CRAM physically associates with CRMP3 when co-expressed in COS-7 cells. The expression of CRAM is brain-specific, is high in fetal and neonatal rat brain, and decreases to very low levels in adult brain. Moreover, CRAM expression is up-regulated during neuronal differentiation of embryonal carcinoma P19 and PC12 cells. Finally, immunoprecipitation analysis of rat brain extracts shows that CRAM is co-immunoprecipitated with proteins that contain protein-tyrosine kinase activity. Taken together, our results suggest that CRAM, which interacts with CRMP3 and protein-tyrosine kinase(s), is a new member of an emerging family of molecules that potentially mediate signals involved in the guidance and outgrowth of axons.     

Rethinking sources of nitrogen to cereal crops

Understanding how to manage N inputs to identify the practices that maximize N recovery has been an organizing principle of agronomic research. Because growth in N fertilizer inputs is expected to continue in an ongoing effort to boost crop production over coming decades, understanding how to efficiently manage recovery of fertilizer N will be important going forward. Yet synthesis of published data that has traced the fate of ¹⁵N‐labeled fertilizer shows that less than half of the N taken up by crops is derived from current‐year N fertilizer. The source of the majority of N in crops is something other than current‐year fertilizer and the sources are not really known. This is true for maize (only 41% of N in crops was from current‐year N fertilizer), rice (32%), and small grains (37%). Recovery of organic fertilizer N (manure, green manure, compost, etc.) in crops is low (27%), though N recovery in subsequent years (10%) was greater than that for mineral fertilizers. Thus, while research on efficiency of N fertilizer use through improved rate, type, location, and timing is important, this research fails to directly address management of the majority of the N supplied to crops. It seems likely that the majority of non‐fertilizer N found in crops comes from turnover of soil and crop residue N. We encourage the research community to revisit the mental model that fertilizer is a replacement for N supply from turnover of soil organic N (SON) and consider a model in which N fertilizer augments ongoing SON turnover and makes an important longer term contribution to SON maintenance and turnover. Research focused on the efficient recovery of N current‐year fertilizer inputs neglects this potential role for building soil N and managing soil N turnover, which seems likely to be the most important source of crop N.     

Zero‐inflated spatio‐temporal models for disease mapping

In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio‐temporal count data have excess zeros. To that end, we consider random effects in zero‐inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio‐temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B‐spline smoothing over the temporal dimension are proposed. The analysis of these complex models is computationally difficult from the frequentist perspective. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning method provides a frequentist approach to mixed models that is also computationally convenient. We propose to use data cloning, which yields to maximum likelihood estimation, to conduct frequentist analysis of zero‐inflated spatio‐temporal modeling of disease incidence. One of the advantages of the data cloning approach is that the prediction and corresponding standard errors (or prediction intervals) of smoothing disease incidence over space and time is easily obtained. We illustrate our approach using a real dataset of monthly children asthma visits to hospital in the province of Manitoba, Canada, during the period April 2006 to March 2010. Performance of our approach is also evaluated through a simulation study.