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The aim of this research was to evaluate the status of selected soil chemical properties and to explore their spatial variability in various agroecosystems (AEs) of Choke Mountain, northwestern highlands of Ethiopia. A total of 74 geo-referenced composite surface (0-30 cm) soil samples were collected from representative farm lands. Soil chemical properties were analyzed following standard procedures. In addition, semi-variogram model was applied to estimate the spatial variability of soil properties. The results showed that soil chemical properties are below their threshold values in all agroecosystems and soil groups. The value of soil pH ranged from 4.3 to 6.8; Exchangeable acidity ranged from 0.124 to 0.180 cmol (+) kg−1; EC ranged from 0.02 to 0.19 dS m−1; OC ranged from 0.7 to 3.8%; TN ranged from 0.11 to 0.27%; Exchangeable potassium from 0.29 to 2.0 cmol (+) kg−1and CEC from 22 to 56 cmol (+)kg−1. The results also revealed that soil pH, exchangeable acidity, electrical conductivity, and exchangeable potassium showed strong spatial dependence whereas SOC and TN had moderate spatial dependence. The results indicated that soil quality is deteriorating. Hence, crop, soil and site-specific soil management strategies, primarily focusing on improving the content of SOM and addressing the problems of soil acidity are suggested to improve the soil conditions. [ABSTRACT FROM AUTHOR]

Almost all causative factors of diseases depend on location. The Digital Earth approach is suitable for studying diseases globally. Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors. Through modelling, the most important causative factors can be extracted and the epidemiology of the disease can be observed. In this paper, skin cancer (the most common type of cancer) has been modelled based on its causative factors, including climate factors, people's occupations, nutrition habits, socio-economic factors, and usage of chemical fertiliser. To fit the model, a data framework was first designed, and then data were gathered and processed. Finally, the disease was modelled using Generalised Linear Models (GLM), a statistical model based on the location of the factors. The results of this study identify the most important causative factors together with their relative priority. Furthermore, a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors. This work illustrates the ability of the model to predict disease occurrence. Thus, by using this Digital Earth approach, skincancer can be studied in all the key countries around the world. [ABSTRACT FROM AUTHOR]

The photogrammetry techniques are known to be accessible due to its low cost, while the geometric accuracy is a key point to ensure that models obtained from photogrammetry are a feasible solution. This work evaluated the discrepancies in 3D (DSM) and 2D (orthomosaic) models obtained from photogrammetry using control points (GCPs) near a reflective/refractive area (water body), where the objective was to evaluate these points, analysing the independence, normality and randomness and other basic statistic. The images were obtained with a 16 MP Canon PowerShot ELPH 110S with a modified NiR band and a multispectral sensor Parrot Sequoia, both embedded in a hex-rotor UAV in flight over the Unisinos University's artificial lake in the city of São Leopoldo, Rio Grande do Sul, Brazil. Due the distribution of the data found to be not normal, we applied non-parametric tests Chebyshev's Theorem and the Mann-Whitney's U test, where it showed that the values obtained from Sequoia DSM presented significant similarities with the values obtained from the GCP's considering the confidence level of 95%; however, this was not confirmed for the model generated from a Canon camera, showing that we found better results using the multispectral Parrot Sequoia. [ABSTRACT FROM AUTHOR]

Acceptance sampling, a category of statistical quality control, deals with the confidence of the product's quality. In certain times, it is necessary to deal with the error in the demanding distribution counting on the sample size and the pertained population size, in determining the necessitated sample size for the acute exactitude. Further this sample size with minimized error is utilized in deriving the most beneficial OC curve. Neural networks have been used to train the data with the resulting error and their matching toleration level for the sample sizes of different population sizes. This trained network can be used to foster automated acceptance or rejection of the sample size to be used for a better OC curve based on the minimized error, ensuing time reduction of the burdened work. It is better explained in this paper with the geo-statistics data, using SAS program. [ABSTRACT FROM PUBLISHER]