Industria y concentración de cultivos: la contribución de la industria del frío en la fruticultura leridana

Para avanzar en el estudio de la concentración espacial de cultivos, se ha elegido el caso de la manzana, la pera y el melocotón en Lleida, desde 1962 a 2000. La evolución de ese fenómeno se ha estudiado mediante técnicas de equilibrio espacial y análisis shift share, encontrándose una pauta espacial de comportamiento distinta entre la manzana y la pera por una parte y el melocotón por otro. En el caso de las técnicas shift share se ha modelado el efecto diferencial como el resultado de un juego de suma nula, y suponiendo que las transferencias de efectos son más probables hacia las regiones más cercanas, se ha avanzado una explicación de las transferencias de superficie que se produjeron entre 1962 y 2000. La diferencia encontrada en el distinto comportamiento espacial de esos cultivos se ha atribuido a la susceptibilidad de cada cultivo para ser conservado frigoríficamente. Se ha desarrollado un modelo que relaciona los incrementos de la capacidad en la industria frigorífica y de la superficie.

The effects of soil pollution and its relation with morbidity in shoreline areas

Soil and subsoil pollution is not only significant in terms of environmental loss, but also a matter of environmental and public health. Solid, liquid and gaseous residues are the major soil contamination agents. They originate from urban conglomerates and industrial areas in which it is impossible to emphasize the chemical, petrochemical and textile industry; thermoelectric, mining, and ironmaster activities. The contamination process can thus be defined as a compound addition to soil, from what qualitative and or quantitative manners can modify soil's natural characteristics and use, producing baneful and deteriorative effects on human health. Studies have shown that human exposition to high concentration of some heavy metals found on soil can cause serious health problems, such as pulmonary or kidney complications, liver and nervous system harm, allergy, and the chronic exposition that leads to death. The present study searches for the correlation among soil contamination, done through a geochemical baseline survey of an industrial contamination area on the shoreline of Sao Paulo state. The study will be conducted by spatial analysis using Geographical Information Systems for mapping and regression analysis. The used data are 123 soil samples of percentage concentration of heavy metals. They were sampled and spatially distributed by geostatistics methods. To verify if there is a relation between heavy metals soil pollution and morbidity an executed correlation and regression analysis will be done using the pollution registers as the independent variables and morbidity as dependable variables. It is expected, by the end of the study, to identify the areas relation between heavy metals soil pollution and morbidity, moreover to be able to provide assistance in terms of new methodologies that could facilitate soil pollution control programs and public health planning. © 2010 WIT Press.

Quantitative, chemically specific imaging of selenium transformation in plants

Quantitative, chemically specific images of biological systems would be invaluable in unraveling the bioinorganic chemistry of biological tissues. Here we report the spatial distribution and chemical forms of selenium in Astragalus bisulcatus (two-grooved poison or milk vetch), a plant capable of accumulating up to 0.65% of its shoot dry biomass as Se in its natural habitat. By selectively tuning incident x-ray energies close to the Se K-absorption edge, we have collected quantitative, 100-μm-resolution images of the spatial distribution, concentration, and chemical form of Se in intact root and shoot tissues. To our knowledge, this is the first report of quantitative concentration-imaging of specific chemical forms. Plants exposed to 5 μM selenate for 28 days contained predominantly selenate in the mature leaf tissue at a concentration of 0.3–0.6 mM, whereas the young leaves and the roots contained organoselenium almost exclusively, indicating that the ability to biotransform selenate is either inducible or developmentally specific. While the concentration of organoselenium in the majority of the root tissue was much lower than that of the youngest leaves (0.2–0.3 compared with 3–4 mM), isolated areas on the extremities of the roots contained concentrations of organoselenium an order of magnitude greater than the rest of the root. These imaging results were corroborated by spatially resolved x-ray absorption near-edge spectra collected from selected 100 × 100 μm2 regions of the same tissues.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage

Soil properties are closely related with crop production and spite of the measures implemented, spatial variation has been repeatedly observed and described. Identifying and describing spatial variations of soil properties and their effects on crop yield can be a powerful decision-making tool in specific land management systems. The objective of this research was to characterize the spatial and temporal variations in crop yield and chemical and physical properties of a Rhodic Hapludox soil under no-tillage. The studied area of 3.42 ha had been cultivated since 1985 under no-tillage crop rotation in summer and winter. Yield and soil property were sampled in a regular 10 x 10 m grid, with 302 sample points. Yields of several crops were analyzed (soybean, maize, triticale, hyacinth bean and castor bean) as well as soil chemical (pH, Soil Organic Matter (SOM), P, Ca2+, Mg2+, H + Al, B, Fe, Mn, Zn, CEC, sum of bases (SB), and base saturation (V %)) and soil physical properties (saturated hydraulic conductivity, texture, density, total porosity, and mechanical penetration resistance). Data were analyzed using geostatistical analysis procedures and maps based on interpolation by kriging. Great variation in crop yields was observed in the years evaluated. The yield values in the Northern region of the study area were high in some years. Crop yields and some physical and soil chemical properties were spatially correlated.

Soil Aggregation, Organic Carbon Concentration, and Soil Bulk Density As Affected by Cover Crop Species in a No-Tillage System

Soil aggregation and the distribution of total organic carbon (TOC) may be affected by soil tillage and cover crops. The objective of this study was to determine the effects of crop rotation with cover crops on soil aggregation, TOC concentration in the soil aggregate fractions, and soil bulk density under a no-tillage system (NTS) and conventional tillage system (CTS, one plowing and two disking). This was a three-year study with cover crop/rice/cover crop/rice rotations in the Brazilian Cerrado. A randomized block experimental design with six treatments and three replications was used. The cover crops (treatments) were: fallow, Panicum maximum, Brachiaria ruziziensis, Brachiaria brizantha, and millet (Pennisetum glaucum). An additional treatment, fallow plus CTS, was included as a control. Soil samples were collected at the depths of 0.00-0.05 m, 0.05-0.10 m, and 0.10-0.20 m after the second rice harvest. The treatments under the NTS led to greater stability in the soil aggregates (ranging from 86.33 to 95.37 %) than fallow plus CTS (ranging from 74.62 to 85.94 %). Fallow plus CTS showed the highest number of aggregates smaller than 2 mm. The cover crops affected soil bulk density differently, and the millet treatment in the NTS had the lowest values. The cover crops without incorporation provided the greatest accumulation of TOC in the soil surface layers. The TOC concentration was positively correlated with the aggregate stability index in all layers and negatively correlated with bulk density in the 0.00-0.10 m layer.

Spatial variability of soil physical properties in two management systems in sugarcane crop

The aim of this study was to characterize the spatial variability of soil bulk density (Bd), soil moisture content (θ) and total porosity (Tp) in two management systems of sugarcane harvesting, with or without burning, in a Haplustox soil, in the 0-0.20 m layer. The study area is located in Rio Brilhante, state of Mato Grosso do Sul, Brazil, in Eldorado Sugar Mill. The plots have presented 180 m length, and 145.6 m width, totaling 90 points distributed in the form of a grid of nine rows by ten columns, with points spaced 20 m from its neighbor. Soil samples were collected at 0-0.20 m layer in 2007/2008 and 2008/2009 crops. The harvest with burning system had a higher density compared to mechanized harvest, in the two study periods. The moisture content as well as the porosity increased proportionally with the decrease of the density of the harvest burning system compared to the mechanized.

Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.

Spatial statistics applied to soybean production data from Paraná State for 2003-04 to 2009-10 crop-years

In the current study, we performed a soybean production spatial distribution analysis in Paraná State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Paraná Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Moran's index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Paraná.

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.

Quantifying the effect of buffer zones, crop areas and spatial aggregation on the externalities of genetically modified crops at landscape level

The development of genetically modified (GM) crops has led the European Union (EU) to put forward the concept of 'coexistence' to give fanners the freedom to plant both conventional and GM varieties. Should a premium for non-GM varieties emerge in the market, 'contamination' by GM pollen would generate a negative externality to conventional growers. It is therefore important to assess the effect of different 'policy variables'on the magnitude of the externality to identify suitable policies to manage coexistence. In this paper, taking GM herbicide tolerant oilseed rape as a model crop, we start from the model developed in Ceddia et al. [Ceddia, M.G., Bartlett, M., Perrings, C., 2007. Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecol. Modell. 205, pp. 169-180] use a Monte Carlo experiment to generate data and then estimate the effect of the number of GM and conventional fields, width of buffer areas and the degree of spatial aggregation (i.e. the 'policy variables') on the magnitude of the externality at the landscape level. To represent realistic conditions in agricultural production, we assume that detection of GM material in conventional produce might occur at the field level (no grain mixing occurs) or at the silos level (where grain mixing from different fields in the landscape occurs). In the former case, the magnitude of the externality will depend on the number of conventional fields with average transgenic presence above a certain threshold. In the latter case, the magnitude of the externality will depend on whether the average transgenic presence across all conventional fields exceeds the threshold. In order to quantify the effect of the relevant' policy variables', we compute the marginal effects and the elasticities. Our results show that when relying on marginal effects to assess the impact of the different 'policy variables', spatial aggregation is far more important when transgenic material is detected at field level, corroborating previous research. However, when elasticity is used, the effectiveness of spatial aggregation in reducing the externality is almost identical whether detection occurs at field level or at silos level. Our results show also that the area planted with GM is the most important 'policy variable' in affecting the externality to conventional growers and that buffer areas on conventional fields are more effective than those on GM fields. The implications of the results for the coexistence policies in the EU are discussed. (C) 2008 Elsevier B.V. All rights reserved.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.