Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments

Snow properties have been retrieved from satellite data for many decades. While snow extent is generally felt to be obtained reliably from visible-band data, there is less confidence in the measurements of snow mass or water equivalent derived from passive microwave instruments. This paper briefly reviews historical passive microwave instruments and products, and compares the large-scale patterns from these sources to those of general circulation models and leading reanalysis products. Differences are seen to be large between the datasets, particularly over Siberia. A better understanding of the errors in both the model-based and measurement-based datasets is required to exploit both fully. Techniques to apply to the satellite measurements for improved large-scale snow data are suggested.

Northern hemisphere snow: measurement, modelling and predictability

Northern hemisphere snow water equivalent (SWE) distribution from remote sensing (SSM/I), the ERA40 reanalysis product and the HadCM3 general circulation model are compared. Large differences are seen in the February climatologies, particularly over Siberia. The SSM/I retrieval algorithm may be overestimating SWE in this region, while comparison with independent runoff estimates suggest that HadCM3 is underestimating SWE. Treatment of snow grain size and vegetation parameterizations are concerns with the remotely sensed data. For this reason, ERA40 is used as `truth' for the following experiments. Despite the climatology differences, HadCM3 is able to reproduce the distribution of ERA40 SWE anomalies when assimilating ERA40 anomaly fields of temperature, sea level pressure, atmospheric winds and ocean temperature and salinity. However when forecasts are released from these assimilated initial states, the SWE anomaly distribution diverges rapidly from that of ERA40. No predictability is seen from one season to another. Strong links between European SWE distribution and the North Atlantic Oscillation (NAO) are seen, but forecasts of this index by the assimilation scheme are poor. Longer term relationships between SWE and the NAO, and SWE and the El Ni\~no-Southern Oscillation (ENSO) are also investigated in a multi-century run of HadCM3. SWE is impacted by ENSO in the Himalayas and North America, while the NAO affects SWE in North America and Europe. While significant connections with the NAO index were only present in DJF (and to an extent SON), the link between ENSO and February SWE distribution was seen to exist from the previous JJA ENSO index onwards. This represents a long lead time for SWE prediction for hydrological applications such as flood and wildfire forecasting. Further work is required to develop reliable large scale observation-based SWE datasets with which to test these model-derived connections.

Dichotomous-choice contingent valuation with 'don't know' responses and misreporting

A new approach is presented that simultaneously deals with Misreporting and Don't Know (DK) responses within a dichotomous-choice contingent valuation framework. Utilising a modification of the standard Bayesian Probit framework, a Gibbs with Metropolis-Hastings algorithm is used to estimate the posterior densities for the parameters of interest. Several model specifications are applied to two contingent valuation datasets: one on wolf management plans, and one on the US Fee Demonstration Program. We find that DKs are more likely to be from people who would be predicted to have positive utility for the bid. Therefore, a DK is more likely to be a YES than a NO. We also find evidence of misreporting, primarily in favour of the NO option. The inclusion of DK responses has an unpredictable impact on willingness-to-pay estimates, since it impacts differently on the results for the two datasets we examine. Copyright (C) 2009 John Wiley & Sons, Ltd.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Evaluation of NRC, UC Davis and ADAS approaches to estimate the metabolizable energy values of feeds at maintenance energy intake from equations utilizing chemical assays and in vitro determinations

Feed samples received by commercial analytical laboratories are often undefined or mixed varieties of forages, originate from various agronomic or geographical areas of the world, are mixtures (e.g., total mixed rations) and are often described incompletely or not at all. Six unified single equation approaches to predict the metabolizable energy (ME) value of feeds determined in sheep fed at maintenance ME intake were evaluated utilizing 78 individual feeds representing 17 different forages, grains, protein meals and by-product feedstuffs. The predictive approaches evaluated were two each from National Research Council [National Research Council (NRC), Nutrient Requirements of Dairy Cattle, seventh revised ed. National Academy Press, Washington, DC, USA, 2001], University of California at Davis (UC Davis) and ADAS (Stratford, UK). Slopes and intercepts for the two ADAS approaches that utilized in vitro digestibility of organic matter and either measured gross energy (GE), or a prediction of GE from component assays, and one UC Davis approach, based upon in vitro gas production and some component assays, differed from both unity and zero, respectively, while this was not the case for the two NRC and one UC Davis approach. However, within these latter three approaches, the goodness of fit (r(2)) increased from the NRC approach utilizing lignin (0.61) to the NRC approach utilizing 48 h in vitro digestion of neutral detergent fibre (NDF:0.72) and to the UC Davis approach utilizing a 30 h in vitro digestion of NDF (0.84). The reason for the difference between the precision of the NRC procedures was the failure of assayed lignin values to accurately predict 48 h in vitro digestion of NDF. However, differences among the six predictive approaches in the number of supporting assays, and their costs, as well as that the NRC approach is actually three related equations requiring categorical description of feeds (making them unsuitable for mixed feeds) while the ADAS and UC Davis approaches are single equations, suggests that the procedure of choice will vary dependent Upon local conditions, specific objectives and the feedstuffs to be evaluated. In contrast to the evaluation of the procedures among feedstuffs, no procedure was able to consistently discriminate the ME values of individual feeds within feedstuffs determined in vivo, suggesting that the quest for an accurate and precise ME predictive approach among and within feeds, may remain to be identified. (C) 2004 Elsevier B.V. All rights reserved.

Can arable field margins be managed to enhance their biodiversity, conservation and functional value for soil macrofauna?

1. The establishment of grassy strips at the margins of arable fields is an agri-environment scheme that aims to provide resources for native flora and fauna and thus increase farmland biodiversity. These margins can be managed to target certain groups, such as farmland birds and pollinators, but the impact of such management on the soil fauna has been poorly studied. This study assessed the effect of seed mix and management on the biodiversity, conservation and functional value of field margins for soil macrofauna. 2. Experimental margin plots were established in 2001 in a winter wheat field in Cambridgeshire, UK, using a factorial design of three seed mixes and three management practices [spring cut, herbicide application and soil disturbance (scarification)]. In spring and autumn 2005, soil cores taken from the margin plots and the crop were hand-sorted for soil macrofauna. The Lumbricidae, Isopoda, Chilopoda, Diplopoda, Carabidae and Staphylinidae were identified to species and classified according to feeding type. 3. Diversity in the field margins was generally higher than in the crop, with the Lumbricidae, Isopoda and Coleoptera having significantly more species and/or higher abundances in the margins. Within the margins, management had a significant effect on the soil macrofauna, with scarified plots containing lower abundances and fewer species of Isopods. The species composition of the scarified plots was similar to that of the crop. 4. Scarification also reduced soil- and litter-feeder abundances and predator species densities, although populations appeared to recover by the autumn, probably as a result of dispersal from neighbouring plots and boundary features. The implications of the responses of these feeding groups for ecosystem services are discussed. 5. Synthesis and applications. This study shows that the management of agri-environment schemes can significantly influence their value for soil macrofauna. In order to encourage the litter-dwelling invertebrates that tend to be missing from arable systems, agri-environment schemes should aim to minimize soil cultivation and develop a substantial surface litter layer. However, this may conflict with other aims of these schemes, such as enhancing floristic and pollinator diversity.

Implications for food production, land use and rural development of the European Union's Single Farm Payment: Indications from a survey of farmers' intentions in Germany, Portugal and the UK

The 2003 reform of the European Union's (EU) Common Agricultural Policy introduced a decoupled income support for farmers called the Single Farm Payment (SFP). Concerns were raised about possible future land use and production changes and their impact on rural communities. Here, such concerns are considered against the workings of the SFP in three EU Member States. Various quantitative studies that have determined the likely impact of the SFP within the EU and the study countries are reviewed. We present the results of a farm survey conducted in the study countries in which farmers' responses to a decoupling scenario similar to the SFP were sought. We found that little short-term change was proposed in the three, rather different, study countries with only 30% of the farmers stating that they would alter their mix of farm activities. Furthermore, less than 30% of all respondents in each country would idle any land under decoupling. Of those who would adopt a new activity, the most popular choices were forestry, woodland and non-food crops. (c) 2007 Elsevier Ltd. All rights reserved.

Statistical methods for ordered categorical data based on a constrained odds model

The proportional odds model provides a powerful tool for analysing ordered categorical data and setting sample size, although for many clinical trials its validity is questionable. The purpose of this paper is to present a new class of constrained odds models which includes the proportional odds model. The efficient score and Fisher's information are derived from the profile likelihood for the constrained odds model. These results are new even for the special case of proportional odds where the resulting statistics define the Mann-Whitney test. A strategy is described involving selecting one of these models in advance, requiring assumptions as strong as those underlying proportional odds, but allowing a choice of such models. The accuracy of the new procedure and its power are evaluated.

Genetic structure and systematic relationships within the Ophrys fuciflora aggregate (Orchidaceae: Orchidinae): high diversity in Kent and a wind-induced discontinuity bisecting the Adriatic

A recent phylogenetic study based on multiple datasets is used as the framework for a more detailed examination of one of the ten molecularly circumscribed groups identified, the Ophrys fuciflora aggregate. The group is highly morphologically variable, prone to phenotypic convergence, shows low levels of sequence divergence and contains an unusually large proportion of threatened taxa, including the rarest Ophrys species in the UK. The aims of this study were to (a) circumscribe minimum resolvable genetically distinct entities within the O. fuciflora aggregate, and (b) assess the likelihood of gene flow between genetically and geographically distinct entities at the species and population levels. Fifty-five accessions sampled in Europe and Asia Minor from the O. fuciflora aggregate were studied using the AFLP genetic fingerprinting technique to evaluate levels of infraspecific and interspecific genetic variation and to assess genetic relationships between UK populations of O. fuciflora s.s. in Kent and in their continental European and Mediterranean counterparts. The two genetically and geographically distinct groups recovered, one located in England and central Europe and one in south-eastern Europe, are incongruent with current species delimitation within the aggregate as a whole and also within O. fuciflora s.s. Genetic diversity is higher in Kent than in the rest of western and central Europe. Gene flow is more likely to occur between populations in closer geographical proximity than those that are morphologically more similar. Little if any gene flow occurs between populations located in the south-eastern Mediterranean and those dispersed throughout the remainder of the distribution, revealing a genetic discontinuity that runs north-south through the Adriatic. This discontinuity is also evident in other clades of Ophrys and is tentatively attributed to the long-term influence of prevailing winds on the long-distance distribution of pollinia and especially seeds. A cline of gene flow connects populations from Kent and central and southern Europe; these individuals should therefore be considered part of an extensive meta-population. Gene flow is also evident among populations from Kent, which appear to constitute a single metapopulation. They show some evidence of hybridization, and possibly also introgression, with O. apifera.

The role of biotic and abiotic factors in evolution of ant dispersal in the milkwort family (Polygalaceae)

A phylogenetic approach was taken to investigate the evolutionary history of seed appendages in the plant family Polygalaceae (Fabales) and determine which factors might be associated with evolution of elaiosomes through comparisons to abiotic (climate) and biotic (ant species number and abundance) timelines. Molecular datasets from three plastid regions representing 160 species were used to reconstruct a phylogenetic tree of the order Fabales, focusing on Polygalaceae. Bayesian dating methods were used to estimate the age of the appearance of ant-dispersed elaiosomes in Polygalaceae, shown by likelihood optimizations to have a single origin in the family. Topology-based tests indicated a diversification rate shift associated with appearance of caruncular elaiosomes. We show that evolution of the caruncular elaiosome type currently associated with ant dispersal occurred 54.0-50.5 million year ago. This is long after an estimated increase in ant lineages in the Late Cretaceous based on molecular studies, but broadly concomitant with increasing global temperatures culminating in the Late Paleocene-Early Eocene thermal maxima. These results suggest that although most major ant clades were present when elaiosomes appeared, the environmental significance of elaiosomes may have been an important factor in success of elaiosome-bearing lineages. Ecological abundance of ants is perhaps more important than lineage numbers in determining significance of ant dispersal. Thus, our observation that elaiosomes predate increased ecological abundance of ants inferred from amber deposits could be indicative of an initial abiotic environmental function.

Quantitative proteomics using uniform 15N-labeling, mascot and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with 14N and 15N in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of uniformly 14N/15N-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.

The automation and evaluation of nested clade phylogeographic analysis

Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.

Effects of straw and silicon soil amendments on some foliar and stem-base diseases in pot-grown winter wheat

Four foliar and two stem-base pathogens were inoculated onto wheat plants grown in different substrates in pot experiments. Soils from four different UK locations were each treated in three ways: (i) straw incorporated in the field at 10 t ha−1 several months previously; (ii) silicon fertilization at 100 mg L−1 during the experiment; and (iii) no amendments. A sand and vermiculite mix was used with and without silicon amendment. The silicon treatment increased plant silica concentrations in all experiments, but incorporating straw was not associated with raised plant silica concentrations. Blumeria graminis and Puccinia recondita were inoculated by shaking infected plants over the test plants, followed by suitable humid periods. The silicon treatment reduced powdery mildew (B. graminis) substantially in sand and vermiculite and in two of the soils, but there were no effects on the slight infection by brown rust (P. recondita). Phaeosphaeria nodorum and Mycosphaerella graminicola were inoculated as conidial suspensions. Leaf spot caused by P. nodorum was reduced in silicon-amended sand and vermiculite; soil was not tested. Symptoms of septoria leaf blotch caused by M. graminicola were reduced by silicon amendment in a severely infected sand and vermiculite experiment but not in soil or a slightly infected sand and vermiculite experiment. Oculimacula yallundae (eyespot) and Fusarium culmorum (brown foot rot) were inoculated as agar plugs on the stem base. Severity of O. yallundae was reduced by silicon amendment of two of the soils but not sand and vermiculite; brown foot rot symptoms caused by F. culmorum were unaffected by silicon amendment. The straw treatment reduced severity of powdery mildew but did not detectably affect the other pathogens. Both straw and silicon treatments appeared to increase plant resistance to all diseases only under high disease pressure.

Quantitative proteomics using uniform N-15-labeling, MASCOT, and the trans-proteomic pipeline

Stable isotope labeling combined with MS is a powerful method for measuring relative protein abundances, for instance, by differential metabolic labeling of some or all amino acids with N-14 and N-15 in cell culture or hydroponic media. These and most other types of quantitative proteomics experiments using high-throughput technologies, such as LC-MS/MS, generate large amounts of raw MS data. This data needs to be processed efficiently and automatically, from the mass spectrometer to statistically evaluated protein identifications and abundance ratios. This paper describes in detail an approach to the automated analysis of Uniformly N-14/N-15-labeled proteins using MASCOT peptide identification in conjunction with the trans-proteomic pipeline (TPP) and a few scripts to integrate the analysis workflow. Two large proteomic datasets from uniformly labeled Arabidopsis thaliana were used to illustrate the analysis pipeline. The pipeline can be fully automated and uses only common or freely available software.