Arthur F. Naylor, et als., Defendants-Respondents : civil action on appeal from final judgment of the Superior Court of New Jersey, Chancery Division - Burlington County. Sat below: Goldmann, J. S. C. Appendix to respondents' brief.

At head of title: Superior Court of New Jersey Appellate Division - Docket no. A-215-53.

Soil survey of Los Alamos County, New Mexico /

Issued June 1978.

Mayflower Pilgrim descendants in Cape May County, New Jersey; memorial of the three hundredth anniversary of the landing of the Pilgrims at Plymouth, 1620-1920; a record of the Pilgrim descendants who early in its history settled in Cape May County, and some of their children throughout the several states of the union at the present time,

Lettered on cover: Mayflower descendants in Cape May County, 1620-1920.

The Daily union history of Atlantic City and County, New Jersey. Containing sketches of the past and present of Atlantic City and County ...

"Biographies": p. 443-517.

Soil survey of the Millville area, New Jersey

Mode of access: Internet.

Geologic and water-supply reports and maps : Pennsylvania-New Jersey.

Mode of access: Internet.

Cape May County Board of Taxation, Cape May Court House, New Jersey.

"September 1995"--P. iii.

Extent and frequency of inundation of Millstone River flood plain in Somerset County, New Jersey,

Open-file report [no. 4]

Impact of Rapid Transit on the Residential Market in Hudson County, New Jersey

The New York Metropolitan region is one of the most populous urban agglomerations in the world, and the single largest in North America.[1] It is also one of the most prominent economic centers, with New York City at the epicenter of its growth. With the entire region growing rapidly over the last decade, it is essential to analyze the socio-economic changes in order to understand the impact it has on commercial real estate. With its focus on housing rentals, this study aims to highlight housing costs as a function of rapid transit over time.

Area two, New Jersey /

Covers South Jersey.

Watershed status map, New Jersey : August 1986 /

Includes indexed "Status of watersheds."

Map index, upper section, flood hazard analyses, Assunpink Creek watershed, Mercer and Monmouth Counties, New Jersey /

Shows "Stream reaches analyzed" and "Map location & plate number."

Direct, positive feedbacks produce instability in models of interrelationships among soil structure, plants and arbuscular mycorrhizal fungi

Current conceptual models of reciprocal interactions linking soil structure, plants and arbuscular mycorrhizal fungi emphasise positive feedbacks among the components of the system. However, dynamical systems with high dimensionality and several positive feedbacks (i.e. mutualism) are prone to instability. Further, organisms such as arbuscular mycorrhizal fungi (AMF) are obligate biotrophs of plants and are considered major biological agents in soil aggregate stabilization. With these considerations in mind, we developed dynamical models of soil ecosystems that reflect the main features of current conceptual models and empirical data, especially positive feedbacks and linear interactions among plants, AMF and the component of soil structure dependent on aggregates. We found that systems become increasingly unstable the more positive effects with Type I functional response (i.e., the growth rate of a mutualist is modified by the density of its partner through linear proportionality) are added to the model, to the point that increasing the realism of models by adding linear effects produces the most unstable systems. The present theoretical analysis thus offers a framework for modelling and suggests new directions for experimental studies on the interrelationship between soil structure, plants and AMF. Non-linearity in functional responses, spatial and temporal heterogeneity, and indirect effects can be invoked on a theoretical basis and experimentally tested in laboratory and field experiments in order to account for and buffer the local instability of the simplest of current scenarios. This first model presented here may generate interest in more explicitly representing the role of biota in soil physical structure, a phenomenon that is typically viewed in a more process- and management-focused context. (C) 2011 Elsevier Ltd. All rights reserved.

The analysis of ranked observations of soil structure using indicator geostatistics

Structure is an important physical feature of the soil that is associated with water movement, the soil atmosphere, microorganism activity and nutrient uptake. A soil without any obvious organisation of its components is known as apedal and this state can have marked effects on several soil processes. Accurate maps of topsoil and subsoil structure are desirable for a wide range of models that aim to predict erosion, solute transport, or flow of water through the soil. Also such maps would be useful to precision farmers when deciding how to apply nutrients and pesticides in a site-specific way, and to target subsoiling and soil structure stabilization procedures. Typically, soil structure is inferred from bulk density or penetrometer resistance measurements and more recently from soil resistivity and conductivity surveys. To measure the former is both time-consuming and costly, whereas observations made by the latter methods can be made automatically and swiftly using a vehicle-mounted penetrometer or resistivity and conductivity sensors. The results of each of these methods, however, are affected by other soil properties, in particular moisture content at the time of sampling, texture, and the presence of stones. Traditional methods of observing soil structure identify the type of ped and its degree of development. Methods of ranking such observations from good to poor for different soil textures have been developed. Indicator variograms can be computed for each category or rank of structure and these can be summed to give the sum of indicator variograms (SIV). Observations of the topsoil and subsoil structure were made at four field sites where the soil had developed on different parent materials. The observations were ranked by four methods and indicator and the sum of indicator variograms were computed and modelled for each method of ranking. The individual indicators were then kriged with the parameters of the appropriate indicator variogram model to map the probability of encountering soil with the structure represented by that indicator. The model parameters of the SIVs for each ranking system were used with the data to krige the soil structure classes, and the results are compared with those for the individual indicators. The relations between maps of soil structure and selected wavebands from aerial photographs are examined as basis for planning surveys of soil structure. (C) 2007 Elsevier B.V. All rights reserved.