Recent glacier retreat in the Caucasus Mountains, Russia, and associated increase in supraglacial debris cover and supra-/proglacial lake development

This paper reports changes in supraglacial debris cover and supra-/proglacial lake development associated with recent glacier retreat (1985-2000) in the central Caucasus Mountains, Russia. Satellite imagery (Landsat TM and ETM+) was used to map the surface area and supraglacial debris cover on six neighbouring glaciers in the Adylsu valley through a process of manual digitizing on a false-colour composite of bands 5, 4, 3 (red, green, blue). The distribution and surface area of supraglacial and proglacial lakes was digitized for a larger area, which extended to the whole Landsat scene. We also compare our satellite interpretations to field observations in the Adylsu valley. Supraglacial debris cover ranges from < 5% to > 25% on individual glaciers, but glacier retreat between 1985 and 2000 resulted in a 3-6% increase in the proportion of each glacier covered by debris. The only exception to this trend was a very small glacier where debris cover did not change significantly and remote mapping proved more difficult. The increase in debris cover is characterized by a progressive upglacier migration, which we suggest is being driven by focused ablation (and therefore glacier thinning) at the up-glacier limit of the debris cover, resulting in the progressive exposure of englacial debris. Glacier retreat has also been accompanied by an increase in the number of proglacial and supraglacial lakes in our study area, from 16 in 1985 to 24 in 2000, representing a 57% increase in their cumulative surface area. These lakes appear to be impounded by relatively recently lateral and terminal moraines and by debris deposits on the surface of the glacier. The changes in glacier surface characteristics reported here are likely to exert a profound influence on glacier mass balance and their future response to climate change. They may also increase the likelihood of glacier-related hazards (lake outbursts, debris slides), and future monitoring is recommended.

A new Snow-SVAT to simulate the accumulation and ablation of seasonal snow cover beneath a forest canopy

A new snow-soil-vegetation-atmosphere transfer (Snow-SVAT) scheme, which simulates the accumulation and ablation of the snow cover beneath a forest canopy, is presented. The model was formulated by coupling a canopy optical and thermal radiation model to a physically-based multi-layer snow model. This canopy radiation model is physically-based yet requires few parameters, so can be used when extensive in-situ field measurements are not available. Other forest effects such as the reduction of wind speed, interception of snow on the canopy and the deposition of litter were incorporated within this combined model, SNOWCAN, which was tested with data taken as part of the Boreal Ecosystem-Atmosphere Study (BOREAS) international collaborative experiment. Snow depths beneath four different canopy types and at an open site were simulated. Agreement between observed and simulated snow depths was generally good, with correlation coefficients ranging between r^2=0.94 and r^2=0.98 for all sites where automatic measurements were available. However, the simulated date of total snowpack ablation generally occurred later than the observed date. A comparison between simulated solar radiation and limited measurements of sub-canopy radiation at one site indicates that the model simulates the sub-canopy downwelling solar radiation early in the season to within measurement uncertainty.

Impact of land cover changes on the South African climate

The Joint UK Land Environmental Simulator (JULES) was run offline to investigate the sensitivity of land surface type changes over South Africa. Sensitivity tests were made in idealised experiments where the actual land surface cover is replaced by a single homogeneous surface type. The vegetation surface types on which some of the experiments were made are static. Experimental tests were evaluated against the control. The model results show among others that the change of the surface cover results in changes of other variables such as soil moisture, albedo, net radiation and etc. These changes are also visible in the spin up process. The model shows different surfaces spinning up at different cycles. Because JULES is the land surface model of Unified Model, the results could be more physically meaningful if it is coupled to the Unified Model.

Transcriptome analysis and crop improvement (a review)

The identification and characterization of differential gene expression from tissues subjected to stress has gained much attention in plant research. The recognition of elements involved in the response to a particular stress enhances the possibility of promoting crop improvement through direct genetic modification. However, the performance of some of the 'first generation' of transgenic plants with the incorporation of a single gene has not always been as expected. These results have stimulated the development of new transgenic constructions introducing more than one gene and capable of modifying complex pathways. Several techniques are available to conduct the analysis of gene regulation, with such information providing the basis for novel constructs specifically designed to modify metabolism. This review deals with techniques that allow the identification and characterization of differentially-expressed genes and the use of molecular pathway information to produce transgenic plants.

Transgenic approaches to crop improvement

Transgenic crops are now grown commercially on several million hectares, principally in North America. To date, the predominant crops are maize (corn), soybean, cotton, and potatoes. In addition, there have been field trials of transgenics from at least 52 species including all the major field crops, vegetables, and several herbaceous and woody species. This review summarizes recent data relating to such trials, particularly in terms of the trends away from simple, single gene traits such as herbicide and insect resistance towards more complex agronomic traits such as growth rate and increased photosynthetic efficiency. Much of the recent information is derived from inspection of patent databases, a useful source of information on commercial priorities. The review also discusses the time scale for the introduction of these transgenes into breeding populations and their eventual release as new varieties.

Reduced height (Rht) and photoperiod insensitivity (Ppd) allele associations with establishment and early growth of wheat in contrasting production systems

Near isogenic lines (NILs) varying for genes for reduced height (Rht) and photoperiod insensitivity (Ppd-D1a) in a cv. Mercia background (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht8c + Ppd-D1a, Rht-D1c, Rht12) were compared at one field site but within contrasting ('organic' vs. 'conventional') rotational and agronomic contexts, in each of 3 years. In the final year, further NILs (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht-B1b + Rht-D1b, Rht-D1b + Rht-B1c) in both Maris Huntsman and Maris Widgeon backgrounds were added together with 64 lines of a doubled haploid (DH) population [Savannah (Rht-D1b) x Renesansa (Rht-8c + Ppd-D1a)]. Assessments included laboratory tests of germination and coleoptile length, and various field measurements of crop growth between emergence and pre jointing [plant population, tillering, leaf length, ground cover (GC), interception of photosynthetically active radiation (PAR), crop dry matter (DM) and nitrogen accumulation (N), far red: red reflectance ratio (FR:R), crop height, and weed dry matter]. All of the dwarfing alleles except Rht12 in the Mercia background and Rht8c in the DHs were associated with reduced coleoptile length. Most of the dwarfing alleles (depending on background) reduced seed viability. Severe dwarfing alleles (Rht-B1c, Rht-D1c and Rht12) were routinely associated with fewer plant numbers and reduced early crop growth (GC, PAR, DM, N, FR:R), and in 1 year, increased weed DM. In the Mercia background and the DHs the semi-dwarfing allele Rht-D1b was also sometimes associated with reductions in early crop growth; no such negative effects were associated with the marker for Rht8c. When significant interactions between cropping system and genotype did occur it was because differences between lines were more exaggerated in the organic system than in the conventional system. Ppd-D1a was associated positively with plant numbers surviving the winter and early crop growth (GC, FR:R, DM, N, PAR, height), and was the most significant locus in a QTL analysis. We conclude that, within these environmental and system contexts, genes moderating development are likely to be more important in influencing early resource capture than using Rht8c as an alternative semi-dwarfing gene to Rht-D1b.

Landscape context and habitat type as drivers of bee diversity in European annual crops

To better understand the dynamics of bee populations in crops, we assessed the effect of landscape context and habitat type on bee communities in annual entomophilous crops in Europe. We quantified bee communities in five pairs of crop-country: buckwheat in Poland, cantaloupe in France, field beans in the UK, spring oilseed rape in Sweden, and strawberries in Germany. For each country, 7-10 study fields were sampled over a gradient of increasing proportion of semi-natural habitats in the surrounding landscape. The CORINE land cover classification was used to characterize the landscape over a 3 km radius around each study field and we used multivariate and regression analyses to quantify the impact of landscape features on bee abundance and diversity at the sub-generic taxonomic level. Neither overall wild bee abundance nor diversity, taken as the number of sub-genera. was significantly affected by the proportion of semi-natural habitat. Therefore, we used the most precise level of the CORINE classification to examine the possible links between specific landscape features and wild bee communities. Bee community composition fell into three distinct groups across Europe: group I included Poland, Germany, and Sweden, group 2 the UK, and group 3 France. Among all three groups, wild bee abundance and sub-generic diversity were affected by 17 landscape elements including some semi-natural habitats (e.g., transitional wood land-shrub), some urban habitats (e.g., sport and leisure facilities) and some crop habitats (e.g., non-irrigated arable land). Some bee taxa were positively affected by urban habitats only, others by semi-natural habitats only, and others by a combination of semi-natural, urban and crop habitats. Bee sub-genera favoured by urban and crop habitats were more resistant to landscape change than those favoured only by semi-natural habitats. In agroecosystems, the agricultural intensification defined as the loss of semi-natural habitats does not necessarily cause a decline in evenness at the local level, but can change community composition towards a bee fauna dominated by common taxa. (C) 2009 Elsevier B.V. All rights reserved.

Quantifying the effect of buffer zones, crop areas and spatial aggregation on the externalities of genetically modified crops at landscape level

The development of genetically modified (GM) crops has led the European Union (EU) to put forward the concept of 'coexistence' to give fanners the freedom to plant both conventional and GM varieties. Should a premium for non-GM varieties emerge in the market, 'contamination' by GM pollen would generate a negative externality to conventional growers. It is therefore important to assess the effect of different 'policy variables'on the magnitude of the externality to identify suitable policies to manage coexistence. In this paper, taking GM herbicide tolerant oilseed rape as a model crop, we start from the model developed in Ceddia et al. [Ceddia, M.G., Bartlett, M., Perrings, C., 2007. Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecol. Modell. 205, pp. 169-180] use a Monte Carlo experiment to generate data and then estimate the effect of the number of GM and conventional fields, width of buffer areas and the degree of spatial aggregation (i.e. the 'policy variables') on the magnitude of the externality at the landscape level. To represent realistic conditions in agricultural production, we assume that detection of GM material in conventional produce might occur at the field level (no grain mixing occurs) or at the silos level (where grain mixing from different fields in the landscape occurs). In the former case, the magnitude of the externality will depend on the number of conventional fields with average transgenic presence above a certain threshold. In the latter case, the magnitude of the externality will depend on whether the average transgenic presence across all conventional fields exceeds the threshold. In order to quantify the effect of the relevant' policy variables', we compute the marginal effects and the elasticities. Our results show that when relying on marginal effects to assess the impact of the different 'policy variables', spatial aggregation is far more important when transgenic material is detected at field level, corroborating previous research. However, when elasticity is used, the effectiveness of spatial aggregation in reducing the externality is almost identical whether detection occurs at field level or at silos level. Our results show also that the area planted with GM is the most important 'policy variable' in affecting the externality to conventional growers and that buffer areas on conventional fields are more effective than those on GM fields. The implications of the results for the coexistence policies in the EU are discussed. (C) 2008 Elsevier B.V. All rights reserved.

Assessing the vulnerability of food crop systems in Africa to climate change

Africa is thought to be the region most vulnerable to the impacts of climate variability and change. Agriculture plays a dominant role in supporting rural livelihoods and economic growth over most of Africa. Three aspects of the vulnerability of food crop systems to climate change in Africa are discussed: the assessment of the sensitivity of crops to variability in climate, the adaptive capacity of farmers, and the role of institutions in adapting to climate change. The magnitude of projected impacts of climate change on food crops in Africa varies widely among different studies. These differences arise from the variety of climate and crop models used, and the different techniques used to match the scale of climate model output to that needed by crop models. Most studies show a negative impact of climate change on crop productivity in Africa. Farmers have proved highly adaptable in the past to short- and long-term variations in climate and in their environment. Key to the ability of farmers to adapt to climate variability and change will be access to relevant knowledge and information. It is important that governments put in place institutional and macro-economic conditions that support and facilitate adaptation and resilience to climate change at local, national and transnational level.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.