Strategies for determining soil-loss tolerance

Excessive soil losses due to erosion or lateral displacement by machinery impair productivity. Some soil loss is tolerable, but not so much that plant productivity diminishes. Thus productivity is the dominant concern in determining soil-loss tolerance. The effects of soil loss on productivity, however, are difficult to determine. Therefore, two alternatives are discussed for determining the limits of soil loss, or soil-loss tolerance. These alternatives are the maintenance of soil organic matter and, for shallow and moderately deep soils, the maintenance of soil depth. They are not new strategies, but our rapidly increasing knowledge of the dynamics of soil organic matter and the rates of soil formation from bedrock or consolidated sediments warrants the reconsideration of these alternatives. Reductions in either soil organic matter or the depth of shallow or moderately deep soils will lead to declining productivity. Soil organic matter, considered to be a surrogate for productivity, is much easier to monitor than is productivity. Also, there are many computer models for predicting the effects of management on soil organic matter. Recently compiled data on rates of soil formation suggest that soil losses of 1 t/a (2.24 Mg/ha yr) are greater than the rate of replenishment by the weathering of lithic or paralithic material in all but very wet climates.     

The fine structure of the osphradial leaflets in Conus flavidus

The osphradium in the genus Conus is a prominent bi-lamellate structure lying in the mantle roof near to the base of the siphon. Fine structural examination shows that the component cells are organised into a peripheral and a central region. The peripheral region consists of pigmented epidermal cells forming prominent ridges between which are grooves, at the sides of which lie the sensory cells, protected in clefts. The central region is clearly differentiated from the peripheral by a prominent basement membrane. This region contains smooth muscle, paired nerve trunks and a discrete area of cells containing numerous, dense, cored vesicles.     

First Jurassic Choristodera from Asia

Although choristoderes have a good Lower Cretaceous record in Asia, they have never previously been recorded from Jurassic deposits. Here we describe fragmentary vertebral material referable to Choristodera indet. from the Middle Jurassic Balabansai Svita of the Fergana Valley, Kyrgyzstan. This provides a significant range extension for the group in Asia and shows that choristoderes already had a Pan-Laurasian distribution in the Jurassic.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.