Pauperizing the pastoral periphery: the marginalization of herding communities in the world's dry lands

Environmental degradation and other socio-economic problems are too often discerned in contemporary pastoral systems in the wake of global economic change. This paper identifies cases where pastoral people respond to external pressures and opportunities in positive ways and adapt to changes. We hope that by doing this a framework of pastoral adaptations can be developed. The first part of this paper focuses on impacts of global economic change, which are mostly negative in nature. Through an extensive literature search in geography, anthropology, range management, and development field, the second part introduces cases that we categorize as positive adaptations.     

Water use, growth and yield of green gram under rainfed upland crop sequences in Gangetic Plains of Indian sub-tropics

A field experiment was conducted to study the effect of different crop sequences on water use, growth and yield of green gram (Vigna radiata (L) Wilezek) during 1992-93 and 1993-94 under rainfed upland condition in Indo-Gangetic plains of West Bengal. Green gram sown in the month of March as pre-rainy (summer) season crop, as and when the winter crops vacated the land, produced highest dry matter of 372 gm-2 which was significantly highest in black gram-yellow sarson sequence. The results of the field experimentation revealed that green gram gave highest grain yield to the extent of 10.80 q/ha when sown after black gram (rainy season) followed by yellow sarson (winter season) while the crop produced 10.63 q/ha under sesame-yellow sarson sequence. Highest water use of 267 mm was achieved in green gram under black gram-yellow sarson sequence and the crop gave water use efficiency of 4.07 kg ha-1mm-1 under black gram-yellow sarson sequences.     

The application of GPR to barrier-lagoon sedimentation study in Boao of Hainan Island

Sedimentary successions and internal structure of the coastal barrier-lagoon system of Boao, eastern Hainan Island were studied through utilizing data from test holes and trenches and ground-penetrating-radar (GPR) profiles. During late Pleistocene, fluvial and delta plains developed over an unevenly eroded bedrock during low sea level stand, followed by the formation of littoral and lagoon facies and defined coastal barrier-lagoon-estuary system during the post-glacial uppermost Pleistocene-lower Holocene eustatic rise of the sea level, and the upper Holocene high stand. GPR results show that Yudaitan, a sandy coastal bar backed by a low-laying land (shoal) just east of the active lagoon, is a continuous, parallel and slightly-wavy reflectors indicating homogeneous sandy or sandy gravel sediments, and inclined reflectors partly caused by progradation and accumulation of beach sand and gravel. Quasi-continuous, hummocky and chaotic reflectors from the shoal of Nangang village correspond to mixed accumulation of sands and clay. This research indicates the GPR is a non-intrusive, rapid, and economical method for high-resolution profiling of subsurface sediments in sandy gravelly coast.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

基于空间信息技术的城市应急救援联动系统研究

该文分析国内外城市现代救援体系及其发展趋势，对基于统一的空间信息基础设施平台分布式城市应急救援联动系统(体系结构与系统实现)的关键技术进行研究，以成都市城市应急救援联动系统为例，探讨了城市应急救援联动系统的构成、功能、业务流程、系统平台、用户界面等。