Power and selection of contract terms: The case from the Brazilian orange juice sector

ABSTRACT We propose a model to explain how contract terms are selected in the presence of a form of economic power: contract power. The orange juice sector is used to illustrate an analysis that demonstrates the effects of contract power on the economic organization of the sector. We define contract power as the ability to exploit contractual gaps or failures of contractual provisions, which are strategically left incomplete. Empirical evidence from content analysis of antitrust documents supports the logic of contract power in the orange juice sector in three forms: avoiding changes to payment methods from weight to solid contents (quality); using information asymmetries to manipulate indexes that calculate the formula of orange prices; and deliberately harvesting oranges late in order to dehydrate the fruit, which consequently reduces weight and price. The paper contributes to understanding the selection of contract terms and the debate about how antitrust offices can deal with this issue.

Biomass estimation of Triplectides egleri Sattler (Trichoptera, Leptoceridae) in a stream at Ducke Reserve, Central Amazonia

ABSTRACT Biomass is a fundamental measure for understanding the structure and functioning (e.g. fluxes of energy and nutrients in the food chain) of aquatic ecosystems. We aim to provide predictive models to estimate the biomass of Triplectides egleri Sattler, 1963, in a stream in Central Amazonia, based on body and case dimensions. We used body length, head-capsule width, interocular distance and case length and width to derive biomass estimations. Linear, exponential and power regression models were used to assess the relationship between biomass and body or case dimensions. All regression models used in the biomass estimation of T. egleri were significant. The best fit between biomass and body or case dimensions was obtained using the power model, followed by the exponential and linear models. Body length provided the best estimate of biomass. However, the dimensions of sclerotized structures (interocular distance and head-capsule width) also provided good biomass predictions, and may be useful in estimating biomass of preserved and/or damaged material. Case width was the dimension of the case that provided the best estimate of biomass. Despite the low relation, case width may be useful in studies that require low stress on individuals.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

RECOVERY OF AN OXISOL DEGRADED BY THE CONSTRUCTION OF A HYDROELECTRIC POWER PLANT

ABSTRACT The removal of thick layers of soil under native scrubland (Cerrado) on the right bank of the Paraná River in Selvíria (State of Mato Grosso do Sul, Brazil) for construction of the Ilha Solteira Hydroelectric Power Plant caused environmental damage, affecting the revegetation process of the stripped soil. Over the years, various kinds of land use and management systems have been tried, and the aim of this study was to assess the effects of these attempts to restore the structural quality of the soil. The experiment was conducted considering five treatments and thirty replications. The following treatments were applied: stripped soil without anthropic intervention and total absence of plant cover; stripped soil treated with sewage sludge and planted to eucalyptus and grass a year ago; stripped soil developing natural secondary vegetation (capoeira) since 1969; pastureland since 1978, replacing the native vegetation; and soil under native vegetation (Cerrado). In the 0.00-0.20 m layer, the soil was chemically characterized for each experimental treatment. A 30-point sampling grid was used to assess soil porosity and bulk density, and to assess aggregate stability in terms of mean weight diameter (MWD) and geometric mean diameter (GMD). Aggregate stability was also determined using simulated rainfall. The results show that using sewage sludge incorporated with a rotary hoe improved the chemical fertility of the soil and produced more uniform soil pore size distribution. Leaving the land to develop secondary vegetation or turning it over to pastureland produced an intermediate level of structural soil quality, and these two treatments produced similar results. Stripped soil without anthropic intervention was of the lowest quality, with the lowest values for cation exchange capacity (CEC) and macroporosity, as well as the highest values of soil bulk density and percentage of aggregates with diameter size <0.50 mm, corroborated by its lower organic matter content. However, the percentage of larger aggregates was higher in the native vegetation treatment, which boosted MWD and GMD values. Therefore, assessment of some land use and management systems show that even decades after their implementation to mitigate the degenerative effects resulting from the installation of the Hydroelectric Plant, more efficient approaches are still required to recover the structural quality of the soil.