40Ar/39Ar single-crystal dating of detrital muscovite and K-feldspar of ODP Holes 116-717A and 116-718C

Detrital K-feldspars and muscovites from Ocean Drilling Program Leg 116 cores that have depositional ages from 0 to 18 Ma have been dated by the 40Ar/39Ar technique. Four to thirteen individual K-feldspars have been dated from seven stratigraphic levels, each of which have a very large range, up to 1660 Ma. At each level investigated, at least one K-feldspar yielded an age minimum which is, within uncertainty, identical to the age of deposition. One to twelve single muscovite crystals from each of six levels have also been studied. The range of muscovite ages is less than that of the K-feldspars and, with one exception, reveal only a 20-Ma spread in ages. As with the K-feldspars, each level investigated contains muscovites with mineral ages essentially identical to depositional ages. These results indicate that a significant portion of the material in the Bengal Fan is first-cycle detritus derived from the Himalayas. Therefore, the significant proportion of sediment deposited in the distal fan in the early to mid Miocene can be ascribed to a significant pulse of uplift and erosion in the collision zone. Moreover, these data indicate that during the entire Neogene, some portion of the Himalayan orogen was experiencing rapid erosion (<= uplift). The lack of granulite facies rocks in the eastern Himalayas and Tibetan Plateau suggests that very rapid uplift must have been distributed in brief pulses in different places in the mountain belt. We suggest that the great majority of the crystals with young apparent ages have been derived from the southern slope of the Himalayas, predominantly from near the main central thrust zone. These data provide further evidence against tectonic models in which the Himalayas and Tibetan plateaus are uplifted either uniformly during the past 40 m.y. or mostly within the last 2 to 5 m.y.

(Table 1) Consolidation test for sediments of ODP Leg 130 sites

Consolidation tests were performed on 19 samples of calcareous ooze from the Ontong Java Plateau, obtained during Ocean Drilling Program Leg 130. Rebound curves from consolidation tests on Ontong Java Plateau samples yield porosity rebounds of 1%-4% for these sediments at equivalent depths up to 1200 mbsf. The exception is a radiolarian-rich sample that has 6% rebound. A rebound correction derived from the porosity rebound vs. depth data has been combined with a correction for pore-water expansion to correct the shipboard laboratory porosity data to in-situ values. Comparison of the laboratory porosity data corrected in this manner with the downhole log data shows good agreement.

Distribution of Recent benthic foraminifera in Arctic galciomarine sediments

Foraminifera were examined in recent (<100 years) fine-grained glaciomarine muds from surface sediments and cores from Nordensheld Bay, Novaja Zemlja, and Hornsund and Bellsund, Spitsbergen. This study presents the first data on modern foraminifera distribution for fjord environments in Novaja Zemlja, Russia. The data are interpreted with reference to the distribution of foraminiferal near Svalbard and the Barents Sea. In Nordensheld Bay, live and dead Nonionellina labradorica and Islandiella norcrossi are most abundant in the outer fjord. Cassidulina reniforme and Allogromiina spp. dominate in the middle and inner fjord. The dominant species are dissimilar to species occurring in other areas of the Barents Sea region, with the exception of Svalbard fjords. The number of live foraminifera (24 to 122 tests/10 cm1) in outer and middle Nordensheld Bay corresponds with values known from the open Barents Sea. However, the biomass (0.03 mg/10 cm**3) is two orders of magnitude less due to smaller foraminiferal test size, which in glaciomarine sediments reflects the absence of larger species, paucity of large specimens, and high occurrence of juvenile foraminifera. The smaller size indicates an opportunistic response to environmental stress due to glacier proximity. The presence of Quinqueloculina stalkeri is diagnostic of glaciomarine environments in fjords of Novaja Zemlja and Svalbard.

Statistics of the National soil test database of France

In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2009 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 2,000,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 4 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009). Two kinds of agronomic soil properties are available: the firs one correspond to the quantitative variables like the organic carbon content and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics stets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.