Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

Integrated Use of GLEAMS and GIS to Prevent Groundwater Pollution Caused by Agricultural Disposal of Animal Waste

/ In modern intensive animal farming the disposal of a large amount of waste is of great concern, as, if not properly performed, it can cause the pollution of water, mainly because of the high content of nitrate and phosphate. This paper presents the results of a study intended to assess the environmental sustainability of animal waste disposal on agricultural soils in the alluvial plain of the River Chiana (Tuscany, Italy), a particularly sensitive area because of the high vulnerability of the shallow aquifer and of the intensive agricultural and breeding activities. With this aim, a strategy has been employed, that consists of the integrated use of a management model and GISs. The consequences on groundwater of applying animal waste to different kind of soils and crop arrangements have been simulated by means of the management model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, ver 2.01). As the huge amount of data required by such a sophisticated model does not allow applications at a scale larger than the field size, IDRISI and GRASS GIS packages have been used to divide the study area into land units, with homogeneous environmental characteristics, and then to generalize on these units the outputs of the model. The main conclusions can be synthesized as follows: The amount of animal waste produced in some of the investigated areas (i.e., municipal territory) is greater than that disposable on their own agricultural soil with no risks to the groundwater; consequently a cooperative approach among municipalities is necessary in order to plan waste disposal in a comprehensive and centralized way.KEY WORDS: Land use; Animal waste disposal; Groundwater protection; GIS, Management models     

A SIMULATION OF RIVER-BASIN RESPONSE TO MESOSCALE METEOROLOGICAL FORCING: THE SUSQUEHANNA RIVER BASIN EXPERIMENT (SRBEX)1

ABSTRACT: A mesoscale meteorological model, a surface hydrology model, and a ground-water hydrology model are linked to simulate the hydrographic response of a large river basin to a single storm. Synoptic climatology is employed to choose a representative hydro-climatic event. The mesoscale meteorological model uses three nested domains to simulate relatively high-resolution precipitation over a sub-basin of the Susquehanna River Basin. The hydrology models simulate surface runoff and ground-water baseflow using both analyzed and simulated precipitation. The hydrologic abstractions are handled using both Curve Number and Green-Ampt routines. To support the linkage of the numerical models, special attention is given to data resampling and reprojection. The mesoscale meteorological model simulation captures the spatial and temporal structure of the storm event, while the hydrology models represent the timing of the event well. The Curve Number method generates a realistic hydrograph with both analyzed and simulated precipitation. In contrast, the hydrographic response generated by the Green-Ampt routine is inferior. Several interrelated factors contribute to these results, including: the nature of the precipitation event chosen for the experiment; the tendency of the mesoscale meteorological model to underpredict low intensity, widespread precipitation in this case; and the influence of the surface soil-texture characteristics on infiltration rates.     

区域生态经济规划编制导则

区域生态经济规划的编制及编制方法的研究，是当前国内外生态建设中的热点之一，不仅得到学术界的高度重视，也引起各国政府部门的关注。本文就如何编制区域生态经济规划进行了介绍，内容包括编制规划的指导思想、基本原则、规划目标的确定、规划应包括的内容以及编制规划的方法、技术路线和程序。文中还对规划编制过程中应注意的问题做了说明。     

Increasing the Accuracy of Productivity and Survival Estimates in Assessing Landbird Population Status

Abstract:  The conservation of species with declining populations requires information on population demography and identification of factors that limit population growth. For landbird species, an understanding of large-scale population declines often requires assessment of local population processes, including the production of offspring, the survival of those offspring, and adult survival. Population growth has been modeled for several species of landbirds to date, and these studies have provided important information on relationships between population status and population-limiting factors. Several recent studies have illuminated field methods and analytical techniques that can aid in increasing the accuracy of productivity and survival estimates for population models. We reviewed these methods and recommend their implementation, including quantification of the season-long productivity of individuals, collection of empirical data on juvenile survival during the postfledging and overwintering periods, and incorporation of adult breeding dispersal into annual adult survival estimates. Such methods will allow for more accurate assessment of population status and provide a better understanding of the factors on which to focus our conservation efforts.     

treedyn3 forest simulation model

The treedyn3 forest simulation model is a process model of tree growth, carbon and nitrogen dynamics in a single-species, even-aged forest stand. It is based on the treedyn model. Major changes include the computation of sun angle and radiation as a function of latitude and day of the year, the closed-form integration of canopy production as a function of day and hour, the introduction of tree number, height, and diameter as separate state variables, and different growth strategies, mortalities, and resulting self-thinning as function of crowding competition.The tree/soil system is described by a set of nonlinear ordinary differential equations for the state variables: tree number, base diameter, tree height, wood biomass, nitrogen in wood, leaf mass, fine root mass, fruit biomass, assimilate, carbon and nitrogen in litter, carbon and nitrogen in soil organic matter, and plant-available nitrogen. The model includes explicit formulations of all relevant ecophysiological processes such as: computation of radiation as a function of seasonal time, daytime and cloudiness, light attenuation in the canopy, and canopy photosynthesis as function of latitude, seasonal time, and daytime, respiration of all parts, assimilate allocation, increment formation, nitrogen fixation, mineralization, humification and leaching, forest management (thinning, felling, litter removal, fertilization etc.), temperature effects on respiration and decomposition, and environmental effects (pollution damage to photosynthesis, leaves, and fine roots). Only ecophysiological parameters which can be either directly measured or estimated with reasonable certainty are used. treedyn3 is a generic process model which requires species- and site-specific parametrization. It can be applied to deciduous and coniferous forests under tropical, as well as temperate or boreal conditions.The paper presents a full documentation of the mathematical model as well as representative simulation results for spruce and acacia.     

Extending the Dewanji and Kalbfleisch model to incorporate marker data

The model of Dewanji and Kalbfleisch for the estimation of time to tumour onset from a serial-sacrifice experiment is extended to include a marker state prior to the onset of the tumour. There are two versions of the model, one where a tumour is allowed to develop without the onset of marker, the other where a tumour develops after the marker but in which the marker later becomes unobservable.     

A general model for tagging on multiple component fisheries: an integration of age-dependent reporting rates and mortality estimation

A major objective of analyzing multiple year tag return data in fisheries is to estimate fishing and natural mortality rates which may vary by age class and calendar year. To do this one needs to be able to estimate the reporting rates for the tags recovered. Some fisheries such as that for Southern Bluefin Tuna (Thunnus maccoyii) have multiple components with potentially different reporting rates for the tag returns. In this paper we develop a general model for multi-cohort, multi-year tag return analyses where there are multiple components to the fishery with potentially different reporting rates. We require the assumption that one component has a reporting rate of 100% (i.e., this could be the component of a boat based fishery where scientific observers are present). We show further how it is possible to partition the overall likelihood developed into two conditionally independent components. The first component of the likelihood is the standard multinomial likelihood that allows estimation of fishing and natural mortality rates. It uses the tag return matrix summed over all the components of the fishery. It requires an average reporting rate for the tag returns (where the average reporting rate is a weighted average of the individual reporting rates of the different components). The second component is also multinomial for the individual component tag returns and allows us to estimate individual component reporting rates. However, this requires that we augment our second component tag return likelihood with a catch data likelihood for the corresponding components. The methodology is illustrated on some Southern Bluefin Tuna tagging and catch data. We also discuss important model assumptions and give suggestions for future research including the integration of tag-return and catch at age data analyses.     

Direct calculation of likelihood-based benchmark dose levels for quantitative responses

A benchmark dose (BMD) for quantitative responses is a lower confidence limit (LCL) on the effective dose corresponding to a specified risk level r. A commonly adopted method for calculating the BMD is to obtain a pointwise upper confidence curve U(d) on the risk function and then invert this relationship by solving the equation U(d)=r. The solution d is taken to be the BMD. Sciullo et al. (2000) have shown that the coverage achieved by this inversion method is at least as great as the coverage achieved by U (·) but that there is otherwise no general relationship between the two coverage probabilities. The present paper develops a method for direct calculation of the BMD based on the asymptotic distribution of the likelihood ratio statistic. It is further shown that the direct method and the inversion method are equivalent when U (·) is also based on the likelihood ratio. Since the direct method is known to be asymptotically correct, it follows that the LR-based inversion method is also asymptotically correct. However, the direct method is computationally faster and easier to program. Finally, some simulation studies are conducted to assess the small sample coverage probabilities of the direct method when responses follow either a normal or a gamma distribution.