Reductions in insecticide use from adoption of Bt cotton in South Africa: impacts on economic performance and toxic load to the environment

The study reported presents the findings relating to commercial growing of genetically-modified Bt cotton in South Africa by a large sample of smallholder farmers over three seasons (1998/99, 1999/2000, 2000/01) following adoption. The analysis presents constructs and compares groupwise differences for key variables in Bt v. non-Bt technology and uses regressions to further analyse the production and profit impacts of Bt adoption. Analysis of the distribution of benefits between farmers due to the technology is also presented. In parallel with these socio-economic measures, the toxic loads being presented to the environment following the introduction of Bt cotton are monitored in terms of insecticide active ingredient (ai) and the Biocide Index. The latter adjusts ai to allow for differing persistence and toxicity of insecticides. Results show substantial and significant financial benefits to smallholder cotton growers of adopting Bt cotton over three seasons in terms of increased yields, lower insecticide spray costs and higher gross margins. This includes one particularly wet, poor growing season. In addition, those with the smaller holdings appeared to benefit proportionately more from the technology (in terms of higher gross margins) than those with larger holdings. Analysis using the Gini-coefficient suggests that the Bt technology has helped to reduce inequality amongst smallholder cotton growers in Makhathini compared to what may have been the position if they had grown conventional cotton. However, while Bt growers applied lower amounts of insecticide and had lower Biocide Indices (per ha) than growers of non-Bt cotton, some of this advantage was due to a reduction in non-bollworm insecticide. Indeed, the Biocide Index for all farmers in the population actually increased with the introduction of Bt cotton. The results indicate the complexity of such studies on the socio-economic and environmental impacts of GM varieties in the developing world.

Hydrochemical variations and contaminant load in the Rio Tinto (Spain) during flood events

The aim of this work is to study the hydrochemical variations during flood events in the Rio Tinto, SW Spain. Three separate rainfall/flood events were monitored in October 2004 following the dry season. In general, concentrations markedly increased following the first event (Fe from 99 to 1130 mg/L; Q(max) = 0.78 m(3)/s) while dissolved loads peaked in the second event (Fe = 7.5 kg/s, Cu = 0.83 kg/s, Zn = 0.82 kg/s; Q(max) = 77 m(3)/s) and discharge in the third event (Q(max) = 127 m(3)/s). This pattern reflects a progressive depletion of metals and sulphate stored in the dry summer as soluble evaporitic salt minerals and concentrated pore fluids, with dilution by freshwater becoming increasingly dominant as the month progressed. Variations in relative concentrations were attributed to oxyhydroxysulphate Fe precipitation, to relative changes in the sources of acid mine drainage (e.g. salt minerals, mine tunnels, spoil heaps etc.) and to differences in the rainfall distributions along the catchment. The contaminant load carried by the river during October 2004 was enormous, totalling some 770 t of Fe, 420 t of Al, 100 t of Cu, 100 t of Zn and 71 t of Mn. This represents the largest recorded example of this flush-out process in an acid mine drainage setting. Approximately 1000 times more water and 1408 200 times more dissolved elements were carried by the river during October 2004 than during the dry, low-flow conditions of September 2004, highlighting the key role of flood Events in the annual pollutant transport budget of semi-arid and and systems and the need to monitor these events in detail in order to accurately quantify pollutant transport. (c) 2007 Elsevier B.V. All rights reserved.

Exploring the spatial relations between soil physical properties and apparent electrical conductivity

Relations between the apparent electrical conductivity of the soil (ECa) and top- and sub-soil physical properties were examined for two arable fields in southern England (Crowmarsh Battle Farms and the Yattendon Estate). The spatial variation of ECa and the soil properties was explored geostatistically. The variogram ranges showed that ECa varied on a similar spatial scale to many of the soil physical properties in both fields. Several features in the map of kriged predictions of ECa were also evident in maps of the soil properties. In addition, the correlation coefficients showed a strong relation between ECa and several soil properties. A moving correlation analysis enabled differences in the relations between ECa and the soil properties to be examined within the fields. The results indicated that relations were inconsistent; they were stronger in some areas than others. A regression of ECa on the principal component scores of the leading components for both fields showed that the first two components accounted for a large proportion of the variance in ECa, whereas the others accounted for little or none. For Crowmarsh topsoil sand and clay, loss on ignition and volumetric water measured in the autumn had large correlations on the first component, and for Yattendon they were large for topsoil sand and clay, and autumn and spring volumetric water. The cross-variograms suggested strong coregionalization between ECa and several soil physical properties; in particular subsoil sand and silt at Crowmarsh, and subsoil sand and clay at Yattendon. The structural correlations from the linear model of coregionalization confirmed the strength of the relations between ECa and the subsoil properties. Nevertheless, no one property was consistently important for both fields. Although a map of ECa can indicate the general patterns of spatial variation in the soil, it is not a substitute for information on soil properties obtained by sampling and analysing the soil. Nevertheless, it could be used to guide further sampling. (c) 2005 Elsevier B.V. All rights reserved.

A comparison of cross-hole electrical and seismic data in fractured rock

Cross-hole anisotropic electrical and seismic tomograms of fractured metamorphic rock have been obtained at a test site where extensive hydrological data were available. A strong correlation between electrical resistivity anisotropy and seismic compressional-wave velocity anisotropy has been observed. Analysis of core samples from the site reveal that the shale-rich rocks have fabric-related average velocity anisotropy of between 10% and 30%. The cross-hole seismic data are consistent with these values, indicating that observed anisotropy might be principally due to the inherent rock fabric rather than to the aligned sets of open fractures. One region with velocity anisotropy greater than 30% has been modelled as aligned open fractures within an anisotropic rock matrix and this model is consistent with available fracture density and hydraulic transmissivity data from the boreholes and the cross-hole resistivity tomography data. However, in general the study highlights the uncertainties that can arise, due to the relative influence of rock fabric and fluid-filled fractures, when using geophysical techniques for hydrological investigations.

Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density

Models developed to identify the rates and origins of nutrient export from land to stream require an accurate assessment of the nutrient load present in the water body in order to calibrate model parameters and structure. These data are rarely available at a representative scale and in an appropriate chemical form except in research catchments. Observational errors associated with nutrient load estimates based on these data lead to a high degree of uncertainty in modelling and nutrient budgeting studies. Here, daily paired instantaneous P and flow data for 17 UK research catchments covering a total of 39 water years (WY) have been used to explore the nature and extent of the observational error associated with nutrient flux estimates based on partial fractions and infrequent sampling. The daily records were artificially decimated to create 7 stratified sampling records, 7 weekly records, and 30 monthly records from each WY and catchment. These were used to evaluate the impact of sampling frequency on load estimate uncertainty. The analysis underlines the high uncertainty of load estimates based on monthly data and individual P fractions rather than total P. Catchments with a high baseflow index and/or low population density were found to return a lower RMSE on load estimates when sampled infrequently than those with a tow baseflow index and high population density. Catchment size was not shown to be important, though a limitation of this study is that daily records may fail to capture the full range of P export behaviour in smaller catchments with flashy hydrographs, leading to an underestimate of uncertainty in Load estimates for such catchments. Further analysis of sub-daily records is needed to investigate this fully. Here, recommendations are given on load estimation methodologies for different catchment types sampled at different frequencies, and the ways in which this analysis can be used to identify observational error and uncertainty for model calibration and nutrient budgeting studies. (c) 2006 Elsevier B.V. All rights reserved.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.