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Displaying 1026 - 1030 of 1605Patterns of variability in large-scale irrigation schemes in Mauritania
Large-scale irrigation schemes have not yielded the expected outcomes in Sub-Saharan Africa. In Mauritania, average land productivity of rice schemes lies between 3 and 3.5tha⁻¹ and irrigated land has progressively being abandoned. At the same time, there is new international attention towards interventions in large-scale irrigation in the Sahel. Spatial and temporal variability of production are main causes of low productivity of large-scale irrigation schemes in Mauritania and threats to their sustainability.
Linking invertebrate communities to decomposition rate and nitrogen availability in pine forest soils
Interpretation of biological indicators of soil quality depends on their ability to reflect ecological processes such as decomposition and nutrient mineralization. Mineral soils in pairs of relatively undisturbed and disturbed pine forests in each of three land resource regions of North Carolina (i.e., coastal plain, piedmont, and mountains) were monitored 15 times over two years. Abundance of nematodes in 35 families, collembolans in 8 families, and 16 other microarthropod taxa were enumerated.
Can management induced changes in the carbonate system drive soil carbon sequestration? A review with particular focus on Australia
In many important agricultural regions, soil inorganic carbon (SIC) stocks can rival the amount of carbon found in organic form. Land management practices, including irrigation, fertilization and liming, have the potential to greatly alter the soil inorganic carbon cycle thus creating an important feedback to atmospheric CO₂ concentrations. However, the current literature is less clear regarding the direction and magnitude of this feedback.
Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points
Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART).
comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region
This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification.