Assessing the users’ need for a spatial decision support system of smallholder farming in Kenya

Accurate data of the natural conditions and agricultural systems with a good spatial resolution are a key factor to tackle food insecurity in developing countries. A broad variety of approaches exists to achieve precise data and information about agriculture. One system, especially developed for smallholder agriculture in East Africa, is the Farm Management Handbook of Kenya. It was first published in 1982/83 and fully revised in 2012, now containing 7 volumes. The handbooks contain detailed information on climate, soils, suitable crops and soil care based on scientific research results of the last 30 years. The density of facts leads to time consuming extraction of all necessary information. In this study we analyse the user needs and necessary components of a system for decision support for smallholder farming in Kenya based on a geographical information system (GIS). Required data sources were identified, as well as essential functions of the system. We analysed the results of our survey conducted in 2012 and early 2013 among agricultural officers. The monitoring of user needs and the problem of non-adaptability of an agricultural information system on the level of extension officers in Kenya are the central objectives. The outcomes of the survey suggest the establishment of a decision support tool based on already available open source GIS components. The system should include functionalities to show general information for a specific location and should provide precise recommendations about suitable crops and management options to support agricultural guidance on farm level.

High Density Single Crystalline GaN Nanodot Arrays Fabricated Using Template-Assisted Selective Growth

High density, uniform GaN nanodot arrays with controllable size have been synthesized by using template-assisted selective growth. The GaN nanodots with average diameter 40nm, 80nm and 120nm were selectively grown by metalorganic chemical vapor deposition (MOCVD) on a nano-patterned SiO2/GaN template. The nanoporous SiO2 on GaN surface was created by inductively coupled plasma etching (ICP) using anodic aluminum oxide (AAO) template as a mask. This selective regrowth results in highly crystalline GaN nanodots confirmed by high resolution transmission electron microscopy. The narrow size distribution and uniform spatial position of the nanoscale dots offer potential advantages over self-assembled dots grown by the Stranski–Krastanow mode.

Quantifying rock fabrics: a test of autocorrelation of the spatial distribution of cristals

A novel test of spatial independence of the distribution of crystals or phases in rocks based on compositional statistics is introduced. It improves and generalizes the common joins-count statistics known from map analysis in geographic information systems. Assigning phases independently to objects in RD is modelled by a single-trial multinomial random function Z(x), where the probabilities of phases add to one and are explicitly modelled as compositions in the K-part simplex SK. Thus, apparent inconsistencies of the tests based on the conventional joins{count statistics and their possibly contradictory interpretations are avoided. In practical applications we assume that the probabilities of phases do not depend on the location but are identical everywhere in the domain of de nition. Thus, the model involves the sum of r independent identical multinomial distributed 1-trial random variables which is an r-trial multinomial distributed random variable. The probabilities of the distribution of the r counts can be considered as a composition in the Q-part simplex SQ. They span the so called Hardy-Weinberg manifold H that is proved to be a K-1-affine subspace of SQ. This is a generalisation of the well-known Hardy-Weinberg law of genetics. If the assignment of phases accounts for some kind of spatial dependence, then the r-trial probabilities do not remain on H. This suggests the use of the Aitchison distance between observed probabilities to H to test dependence. Moreover, when there is a spatial uctuation of the multinomial probabilities, the observed r-trial probabilities move on H. This shift can be used as to check for these uctuations. A practical procedure and an algorithm to perform the test have been developed. Some cases applied to simulated and real data are presented. Key words: Spatial distribution of crystals in rocks, spatial distribution of phases, joins-count statistics, multinomial distribution, Hardy-Weinberg law, Hardy-Weinberg manifold, Aitchison geometry

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Neighborhood characteristics and the spatial concentration of crime: Evidence for Bogotá

Using a unique neighborhood crime dataset for Bogotá in 2011, this study uses a spatial econometric approach and examines the role of socioeconomic and agglomeration variables in explaining the variance of crime. It uses two different types of crime, violent crime represented in homicides and property crime represented in residential burglaries. These two types of crime are then measured in non-standard crime statistics that are created as the area incidence for each crime in the neighborhood. The existence of crime hotspots in Bogotá has been shown in most of the literature, and using these non-standard crime statistics at this neighborhood level some hotspots arise again, thus validating the use of a spatial approach for these new crime statistics. The final specification includes socioeconomic, agglomeration, land-use and visual aspect variables that are then included in a SARAR model an estimated by the procedure devised by Kelejian and Prucha (2009). The resulting coefficients and marginal effects show the relevance of these crime hotspots which is similar with most previous studies. However, socioeconomic variables are significant and show the importance of age, and education. Agglomeration variables are significant and thus more densely populated areas are correlated with more crime. Interestingly, both types of crimes do not have the same significant covariates. Education and young male population have a different sign for homicide and residential burglaries. Inequality matters for homicides while higher real estate valuation matters for residential burglaries. Finally, density impacts positively both crimes.

Slopes of Avian Species-Area Relationships, Human Population Density, and Environmental Factors

There is increasing interest in how humans influence spatial patterns in biodiversity. One of the most frequently noted and marked of these patterns is the increase in species richness with area, the species–area relationship (SAR). SARs are used for a number of conservation purposes, including predicting extinction rates, setting conservation targets, and identifying biodiversity hotspots. Such applications can be improved by a detailed understanding of the factors promoting spatial variation in the slope of SARs, which is currently the subject of a vigorous debate. Moreover, very few studies have considered the anthropogenic influences on the slopes of SARs; this is particularly surprising given that in much of the world areas with high human population density are typically those with a high number of species, which generates conservation conflicts. Here we determine correlates of spatial variation in the slopes of species–area relationships, using the British avifauna as a case study. Whilst we focus on human population density, a widely used index of human activities, we also take into account (1) the rate of increase in habitat heterogeneity with increasing area, which is frequently proposed to drive SARs, (2) environmental energy availability, which may influence SARs by affecting species occupancy patterns, and (3) species richness. We consider environmental variables measured at both local (10 km × 10 km) and regional (290 km × 290 km) spatial grains, but find that the former consistently provides a better fit to the data. In our case study, the effect of species richness on the slope SARs appears to be scale dependent, being negative at local scales but positive at regional scales. In univariate tests, the slope of the SAR correlates negatively with human population density and environmental energy availability, and positively with the rate of increase in habitat heterogeneity. We conducted two sets of multiple regression analyses, with and without species richness as a predictor. When species richness is included it exerts a dominant effect, but when it is excluded temperature has the dominant effect on the slope of the SAR, and the effects of other predictors are marginal.

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities.

Ovenbird (Seiurus aurocapilla) Territory Placement Near Seismic Lines is Influenced by Forest Regeneration and Conspecific Density

The boreal forest of western Canada is being dissected by seismic lines used for oil and gas exploration. The vast amount of edge being created is leading to concerns that core habitat will be reduced for forest interior species for extended periods of time. The Ovenbird (Seiurus aurocapilla) is a boreal songbird known to be sensitive to newly created seismic lines because it does not include newly cut lines within its territory. We examined multiple hypotheses to explain potential mechanisms causing this behavior by mapping Ovenbird territories near lines with varying states of vegetation regeneration. The best model to explain line exclusion behavior included the number of neighboring conspecifics, the amount of bare ground, leaf-litter depth, and canopy closure. Ovenbirds exclude recently cut seismic lines from their territories because of lack of protective cover (lower tree and shrub cover) and because of reduced food resources due to large areas of bare ground. Food reduction and perceived predation risk effects seem to be mitigated once leaf litter (depth and extent of cover) and woody vegetation cover are restored to forest interior levels. However, as conspecific density increases, lines are more likely to be used as landmarks to demarcate territorial boundaries, even when woody vegetation cover and leaf litter are restored. This behavior can reduce territory density near seismic lines by changing the spatial distribution of territories. Landmark effects are longer lasting than the effects from reduced food or perceived predation risk because canopy height and tree density take >40 years to recover to forest interior levels. Mitigation of seismic line impacts on Ovenbirds should focus on restoring forest cover as quickly as possible after line cutting.

Spatial variability of flow statistics within regular building arrays

Turbulence statistics obtained by direct numerical simulations are analysed to investigate spatial heterogeneity within regular arrays of building-like cubical obstacles. Two different array layouts are studied, staggered and square, both at a packing density of λp=0.25 . The flow statistics analysed are mean streamwise velocity ( u− ), shear stress ( u′w′−−−− ), turbulent kinetic energy (k) and dispersive stress fraction ( u˜w˜ ). The spatial flow patterns and spatial distribution of these statistics in the two arrays are found to be very different. Local regions of high spatial variability are identified. The overall spatial variances of the statistics are shown to be generally very significant in comparison with their spatial averages within the arrays. Above the arrays the spatial variances as well as dispersive stresses decay rapidly to zero. The heterogeneity is explored further by separately considering six different flow regimes identified within the arrays, described here as: channelling region, constricted region, intersection region, building wake region, canyon region and front-recirculation region. It is found that the flow in the first three regions is relatively homogeneous, but that spatial variances in the latter three regions are large, especially in the building wake and canyon regions. The implication is that, in general, the flow immediately behind (and, to a lesser extent, in front of) a building is much more heterogeneous than elsewhere, even in the relatively dense arrays considered here. Most of the dispersive stress is concentrated in these regions. Considering the experimental difficulties of obtaining enough point measurements to form a representative spatial average, the error incurred by degrading the sampling resolution is investigated. It is found that a good estimate for both area and line averages can be obtained using a relatively small number of strategically located sampling points.

The spatial distribution and evolution characteristics of North Atlantic cyclones

A climatology of extratropical cyclones is produced using an objective method of identifying cyclones based on gradients of 1-km height wet-bulb potential temperature. Cyclone track and genesis density statistics are analyzed and this method is found to compare well with other cyclone identification methods. The North Atlantic storm track is reproduced along with the major regions of genesis. Cyclones are grouped according to their genesis location and the corresponding lysis regions are identified. Most of the cyclones that cross western Europe originate in the east Atlantic where the baroclinicity and the sea surface temperature gradients are weak compared to the west Atlantic. East Atlantic cyclones also have higher 1-km height relative vorticity and lower mean sea level pressure at their genesis point than west Atlantic cyclones. This is consistent with the hypothesis that they are secondary cyclones developing on the trailing fronts of preexisting “parent” cyclones. The evolution characteristics of composite west and east Atlantic cyclones have been compared. The ratio of their upper- to lower-level forcing indicates that type B cyclones are predominant in both the west and east Atlantic, with strong upper- and lower-level features. Among the remaining cyclones, there is a higher proportion of type C cyclones in the east Atlantic, whereas types A and C are equally frequent in the west Atlantic.

Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood

The variogram is essential for local estimation and mapping of any variable by kriging. The variogram itself must usually be estimated from sample data. The sampling density is a compromise between precision and cost, but it must be sufficiently dense to encompass the principal spatial sources of variance. A nested, multi-stage, sampling with separating distances increasing in geometric progression from stage to stage will do that. The data may then be analyzed by a hierarchical analysis of variance to estimate the components of variance for every stage, and hence lag. By accumulating the components starting from the shortest lag one obtains a rough variogram for modest effort. For balanced designs the analysis of variance is optimal; for unbalanced ones, however, these estimators are not necessarily the best, and the analysis by residual maximum likelihood (REML) will usually be preferable. The paper summarizes the underlying theory and illustrates its application with data from three surveys, one in which the design had four stages and was balanced and two implemented with unbalanced designs to economize when there were more stages. A Fortran program is available for the analysis of variance, and code for the REML analysis is listed in the paper. (c) 2005 Elsevier Ltd. All rights reserved.

Sampling the soil in long-term forest plots: The implications of spatial variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.

Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood

The variogram is essential for local estimation and mapping of any variable by kriging. The variogram itself must usually be estimated from sample data. The sampling density is a compromise between precision and cost, but it must be sufficiently dense to encompass the principal spatial sources of variance. A nested, multi-stage, sampling with separating distances increasing in geometric progression from stage to stage will do that. The data may then be analyzed by a hierarchical analysis of variance to estimate the components of variance for every stage, and hence lag. By accumulating the components starting from the shortest lag one obtains a rough variogram for modest effort. For balanced designs the analysis of variance is optimal; for unbalanced ones, however, these estimators are not necessarily the best, and the analysis by residual maximum likelihood (REML) will usually be preferable. The paper summarizes the underlying theory and illustrates its application with data from three surveys, one in which the design had four stages and was balanced and two implemented with unbalanced designs to economize when there were more stages. A Fortran program is available for the analysis of variance, and code for the REML analysis is listed in the paper. (c) 2005 Elsevier Ltd. All rights reserved.

On the time for fold surfaces to order during the crystallization of polyethylene from the melt and its dependence on molecular parameters

New experiments underpin the interpretation of the basic division in crystallization behaviour of polyethylene in terms of whether or not there is time for the fold surface to order before the next molecular layer is added at the growth front. For typical growth rates, in Regime 11, polyethylene lamellae form with disordered {001} fold surfaces then transform, with lamellar thickening and twisting, towards the more-ordered condition found for slower crystallization in Regime 1, in which lamellae form with and retain {201} fold surfaces. Several linear and linear-low-density polyethylenes have been used to show that, for the same polymer crystallized alone or in a blend, the growth rate at which the change in initial lamellar condition occurs is reasonably constant thereby supporting the concept of a specific time for surfaces to attain the ordered {201}) state. This specific time, in the range from milliseconds to seconds, increases with molecular length, and in linear-low-density polymer, for higher branch contents. (c) 2006 Elsevier Ltd. All rights reserved.

Joint effects of population density and toxicant exposure on population dynamics of Capitella sp I

Very few studies have analyzed the dependence of population growth rate on population density, and even fewer have considered interaction effects of density and other stresses, such as exposure to toxic chemicals. Yet without such studies we cannot know whether chemicals harmful at low density have effects on carrying capacity or, conversely, whether chemicals reducing carrying capacity are also harmful at low density, impeding a population's capacity to recover from disturbance. This study examines the combined effects of population density and a toxicant (fluoranthene) on population growth rate (pgr) and carrying capacity using the deposit-feeding polychaete Capitella sp. I as a test organism. Populations were initiated with a stable age distribution, and population density and age/size distribution were followed during a period of 28 wk. Fluoranthene (FLU), population density, and their interaction influenced population growth rate. Population growth rate declined linearly with the logarithm of population biomass, but the slope of the relationship was steeper for the control populations than for populations exposed to 50 mug FLU/(g sediment dry mass). Populations exposed to 150 mug FLU/(g sediment dry mass) went extinct after 8 wk of exposure. Despite concerns that toxicant effects would be exacerbated at high density, we found the reverse to be the case, and effects of fluoranthene on population growth rate were much reduced in the region of carrying capacity. Fluoranthene did. reduce carrying capacity by 46%, and this could haven important implications for interacting species and/or sediment biogeochemical processes.