Relationships between climatic factors and flower and boll production in Egyptian cotton (Gossypium barbadense)

Fruiting of cotton plant is determined and influenced by cultivar, climatic conditions, management practices and pests. An understanding of the flowering and boll retention patterns of cotton cultivars can contribute to more efficient and economical crop management. The objective of this investigation was to study the effect of various climatic factors on flower and boll production of Egyptian cotton. This could be used in formulating advanced predictions of the effect of certain climatic conditions on the production of Egyptian cotton. Two uniform field trials, using cotton Gossypium barbadense cv. Giza 75 were carried out in 1992 and 1993 at the Agricultural Research Station, Agricultural Research Center, Ministry of Agriculture, Giza, Egypt, to investigate the relationships between climatic factors, flower and boll production. Climatic factors included maximum and minimum air temperatures along with their difference, evaporation, surface soil temperature (grass temperature or green cover temperature) at 0600 and 1800 h°C⁻¹, sunshine duration, maximum and minimum humidity and wind speed. The effects of climatic factors on flower and boll production were quantified in the absence of water and nutritional deficits and damage effects of insects. Results obtained indicate that evaporation, sunshine duration, humidity, surface soil temperature at 1800 h, and maximum air temperature, were the important climatic factors that significantly affect flower and boll production of Egyptian cotton. Consequently, applying appropriate specific cultural practices that minimize the deleterious effect of these factors will lead to an improvement in cotton yield.     

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.     

Modelling detrital cooling-age populations: insights from two Himalayan catchments

The distribution of detrital mineral cooling ages in river sediment provides a proxy record for the erosional history of mountain ranges. We have developed a numerical model that predicts detrital mineral age distributions for individual catchments in which particle paths move vertically toward the surface. Despite a restrictive set of assumptions, the model permits theoretical exploration of the effects of thermal structure, erosion rate, and topography on cooling ages. Hypsometry of the source‐area catchment is shown to exert a fundamental control on the frequency distribution of bedrock and detrital ages. We illustrate this approach by generating synthetic ⁴⁰Ar/³⁹Ar muscovite age distributions for two catchments with contrasting erosion rates in central Nepal and then by comparing actual measured cooling‐age distributions with the synthetic ones. Monte Carlo sampling is used to assess the mismatch between observed and synthetic age distributions and to explore the dependence of that mismatch on the complexity of the synthetic age signal and on the number of grains analysed. Observed detrital cooling ages are well matched by predicted ages for a more slowly eroding Himalayan catchment. A poorer match for a rapidly eroding catchment may result from some combination of large analytical uncertainties in the detrital ages and inhomogeneous erosion rates within the basin. Such mismatches emphasize the need for more accurate thermal and kinematic models and for sampling strategies that are adapted to catchment‐specific geologic and geomorphic conditions.