Different Approaches for Modeling Grouped Survival Data: A Mango Tree Study

Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing interval-censored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

The exponentiated generalized gamma distribution with application to lifetime data

A four-parameter extension of the generalized gamma distribution capable of modelling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone and non-monotone failure rate functions, which are quite common in lifetime data analysis and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the exponentiated Weibull, exponentiated generalized half-normal, exponentiated gamma and generalized Rayleigh, among others. We derive two infinite sum representations for its moments. We calculate the density of the order statistics and two expansions for their moments. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is obtained. Finally, a real data set from the medical area is analysed.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

A Model for comparing reduced impact logging with conventional logging for an Eastern Amazonian Forest

Using data from a logging experiment in the eastern Brazilian Amazon region, we develop a matrix growth and yield model that captures the dynamic effects of harvest system choice on forest structure and composition. Multinomial logistic regression is used to estimate the growth transition parameters for a 10-year time step, while a Poisson regression model is used to estimate recruitment parameters. The model is designed to be easily integrated with an economic model of decisionmaking to perform tropical forest policy analysis. The model is used to compare the long-run structure and composition of a stand arising from the choice of implementing either conventional logging techniques or more carefully planned and executed reduced-impact logging (RIL) techniques, contrasted against a baseline projection of an unlogged forest. Results from log and leave scenarios show that a stand logged according to Brazilian management requirements will require well over 120 years to recover its initial commercial volume, regardless of logging technique employed. Implementing RIL, however, accelerates this recovery. Scenarios imposing a 40-year cutting cycle raise the possibility of sustainable harvest volumes, although at significantly lower levels than is implied by current regulations. Meeting current Brazilian forest policy goals may require an increase in the planned total area of permanent production forest or the widespread adoption of silvicultural practices that increase stand recovery and volume accumulation rates after RIL harvests. Published by Elsevier B.V.

Lepidopteran larva consumption of soybean foliage: basis for developing multiple-species economic thresholds for pest management decisions

BACKGROUND: Defoliation by Anticarsia gemmatalis (Hubner), Pseudoplusia includens (Walker), Spodoptera eridania (Cramer), S. cosmioides (Walker) and S. frugiperda (JE Smith) (Lepidoptera: Noctuidae) was evaluated in four soybean genotypes. A multiple-species economic threshold (ET), based upon the species` feeding capacity, is proposed with the aim of improving growers` management decisions on when to initiate control measures for the species complex. RESULTS: Consumption by A. gemmatalis, S. cosmioides or S. eridania on different genotypes was similar. The highest consumption of P. includens was 92.7 cm(2) on Codetec 219RR; that of S. frugiperda was 118 cm(2) on Codetec 219RR and 115.1 cm(2) on MSoy 8787RR. The insect injury equivalent for S. cosmoides, calculated on the basis of insect consumption, was double the standard consumption by A. gemmatalis, and statistically different from the other species tested, which were similar to each other. CONCLUSIONS: As S. cosmioides always defoliated nearly twice the leaf area of the other species, the injury equivalent would be 2 for this lepidopteran species and 1 for the other species. The recommended multiple-species ET to trigger the beginning of insect control would then be 20 insect equivalents per linear metre. (C) 2010 Society of Chemical Industry

Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada

Grass reference evapotranspiration (ETo) is an important agrometeorological parameter for climatological and hydrological studies, as well as for irrigation planning and management. There are several methods to estimate ETo, but their performance in different environments is diverse, since all of them have some empirical background. The FAO Penman-Monteith (FAD PM) method has been considered as a universal standard to estimate ETo for more than a decade. This method considers many parameters related to the evapotranspiration process: net radiation (Rn), air temperature (7), vapor pressure deficit (Delta e), and wind speed (U); and has presented very good results when compared to data from lysimeters Populated with short grass or alfalfa. In some conditions, the use of the FAO PM method is restricted by the lack of input variables. In these cases, when data are missing, the option is to calculate ETo by the FAD PM method using estimated input variables, as recommended by FAD Irrigation and Drainage Paper 56. Based on that, the objective of this study was to evaluate the performance of the FAO PM method to estimate ETo when Rn, Delta e, and U data are missing, in Southern Ontario, Canada. Other alternative methods were also tested for the region: Priestley-Taylor, Hargreaves, and Thornthwaite. Data from 12 locations across Southern Ontario, Canada, were used to compare ETo estimated by the FAD PM method with a complete data set and with missing data. The alternative ETo equations were also tested and calibrated for each location. When relative humidity (RH) and U data were missing, the FAD PM method was still a very good option for estimating ETo for Southern Ontario, with RMSE smaller than 0.53 mm day(-1). For these cases, U data were replaced by the normal values for the region and Delta e was estimated from temperature data. The Priestley-Taylor method was also a good option for estimating ETo when U and Delta e data were missing, mainly when calibrated locally (RMSE = 0.40 mm day(-1)). When Rn was missing, the FAD PM method was not good enough for estimating ETo, with RMSE increasing to 0.79 mm day(-1). When only T data were available, adjusted Hargreaves and modified Thornthwaite methods were better options to estimate ETo than the FAO) PM method, since RMSEs from these methods, respectively 0.79 and 0.83 mm day(-1), were significantly smaller than that obtained by FAO PM (RMSE = 1.12 mm day(-1). (C) 2009 Elsevier B.V. All rights reserved.

Economic feasibility of cowpea irrigation in Piaui State

The objective of this work was to evaluate the economic feasibility of cowpea irrigation in Piaui State Brazil. Water balances were carried out on a daily basis using the Thornthwaite and Mather (1955) method, for 165 sites, considering twelve sowings dates and available water capacity in the soil of 20, 40 and 60 mm. The net revenues were estimated with a probability of occurrence of 75%, later being spatialized to Piaui State. Cowpea irrigation was shown to economically viable for all sowing dates, irrespective of the available water capacity. Net revenues varied among several regions of the State, in function of the sowing date and available water capacity in the soil. Considering a planning strategy for Piaui State, sowing on February, I was shown to be most favorable, because it enabled higher net revenue values, covering larger areas of the State.

Quantification of the environmental impacts of road conditions in Brazil

This study evaluates the impacts of Brazilian highway conditions on fuel consumption and, consequently, on carbon dioxide (COO emissions. For the purpose of this study, highway conditions refer to the level of highway maintenance: the incidence of large potholes, large surface cracks, uneven sections, and debris. Primary computer collected data related to the fuel consumption of three types of trucks were analyzed. The data were derived from 88 trips taken over six routes, each route representative of one of two highway conditions: better or worse. Study results are initially presented for each type of truck being monitored. The results are then aggregated to approximate the entire Brazilian highway network. In all cases, results confirmed environmental benefits resulting from travel over the better routes. There was found to be an increase in energy efficiency from traveling better roads, which resulted in lower fuel consumption and lower CO(2) emissions. Statistical analysis of the results suggests that, in general, fuel consumption data were significant at *P < 0.05, rejecting the null hypothesis that average fuel consumption from traveling the better routes is statistically equal to average fuel consumption from traveling the worse routes. Improved Brazilian road conditions would generate economic benefits, reduce dependency on and consumption of fossil fuels (due to the increase in energy efficiency), and reduce CO(2) emissions. These findings may have additional relevancy if Brazil needs to reduce carbon dioxide emissions to reach future Kyoto Protocol`s emissions targets, which should take effect in January 2013. (c) 2008 Elsevier B.V. All rights reserved.

Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

Endophytic Colonization of Potato (Solanum tuberosum L.) by a Novel Competent Bacterial Endophyte, Pseudomonas putida Strain P9, and Its Effect on Associated Bacterial Communities

Pseudomonas putida strain P9 is a novel competent endophyte from potato. P9 causes cultivar-dependent suppression of Phytophthora infestans. Colonization of the rhizoplane and endosphere of potato plants by P9 and its rifampin-resistant derivative P9R was studied. The purposes of this work were to follow the fate of P9 inside growing potato plants and to establish its effect on associated microbial communities. The effects of P9 and P9R inoculation were studied in two separate experiments. The roots of transplants of three different cultivars of potato were dipped in suspensions of P9 or P9R cells, and the plants were planted in soil. The fate of both strains was followed by examining colony growth and by performing PCR-denaturing gradient gel electrophoresis (PCR-DGGE). Colonies of both strains were recovered from rhizoplane and endosphere samples of all three cultivars at two growth stages. A conspicuous band, representing P9 and P9R, was found in all Pseudomonas PCR-DGGE fingerprints for treated plants. The numbers of P9R CFU and the P9R-specific band intensities for the different replicate samples were positively correlated, as determined by linear regression analysis. The effects of plant growth stage, genotype, and the presence of P9R on associated microbial communities were examined by multivariate and unweighted-pair group method with arithmetic mean cluster analyses of PCR-DGGE fingerprints. The presence of strain P9R had an effect on bacterial groups identified as Pseudomonas azotoformans, Pseudomonas veronii, and Pseudomonas syringae. In conclusion, strain P9 is an avid colonizer of potato plants, competing with microbial populations indigenous to the potato phytosphere. Bacterization with a biocontrol agent has an important and previously unexplored effect on plant-associated communities.

Feeding Dietary Mannan Oligosaccharides to Juvenile Nile Tilapis, Oreochromis niloticus, Has No Effect on Hematological Parameters and Showed Decreased Feed Consumption

Impaired immune system by environmental stressors can lead fishes to be more susceptible to diseases that limit the economic development of aquaculture systems. This study was set out to determine the effect of six levels of mannan oligosaccharides (MOS; ActiveMOS((R)); Biorigin, Lencois Paulista, Sao Paulo, Brazil) on the performance index and hematology of Nile tilapia, Oreochromis niloticus juveniles. Fish (13.62 g) were randomly distributed into 18 plastic aquaria (300 L; 20 fishes per aquarium) and fed during 45 d with a commercial diet supplemented with 0, 0.2, 0.4, 0.6, 0.8, and 1% dietary MOS, in a totally randomized design trial (n = 3); biometrical and hematological data were collected and analyzed. There were no significant differences in hematological parameters between fish fed control and MOS supplementation diets, and daily feed consumption (FC) decreased (P < 0.05) with increasing levels of dietary MOS. Dietary MOS did not increase leukocyte count and presented negative effects on FC of Nile tilapia. At 0.4% MOS supplementation, the individual weight gain was higher in absolute values but not different (P > 0.05) compared to control diet.

Cattle depredation by puma (Puma concolor) and jaguar (Panthera onca) in central-western Brazil

In this study, data on cattle depredation by puma (Puma concolor) and jaguar (Panthera onca) were recorded for six years (1998 - 2003) in a cattle ranch in central-western Brazil. Depredation represented 18.9% of the overall cattle mortality, being predominant on calves. in biomass, kills represented 0.4% (63.8 kg/km(2)) of the ranch`s annual stock. in economic loss, kills represented 0.3% of the cattle stock value. Depredation was mainly associated with cattle`s age class and location along with the time of birth of calves. The proportion of pastures next to forest with depredation (n=33, 48.5%) was not distinguished to the proportion of pastures not bordering forest with depredation (n=35, 51.5%). However, the proportion of pastures next to forest with depredation represented 54% (n=33) of the 61 total pastures that were at least partially surrounded by forest patches or riparian forests that comprised eight continuum blocks of forest fragments of different sizes in the ranch and adjacent areas. No kills occurred in the central portion (main house) of the farm, close to the headquarters where the pastures not bordering forest. The distances of the kills in relation to areas of native forest was 1317.48 +/- 941.03 m. In order to reduce depredation, calves should be kept as far as possible from forest areas and concentrated cattle breeding and calving seasons should be encouraged. (c) 2007 Elsevier Ltd. All rights reserved.

Environmental, land-use and economic implications of Brazilian sugarcane expansion 1996-2006

Governments are promoting biofuels and the resulting changes in land use and crop reallocation to biofuels production have raised concerns about impacts on environment and food security. The promotion of biofuels has also been questioned based on suggested marginal contribution to greenhouse gas emissions reduction, partly due to induced land use change causing greenhouse gas emissions. This study reports how the expansion of sugarcane in Brazil during 1996-2006 affected indicators for environment, land use and economy. The results indicate that sugarcane expansion did not in general contribute to direct deforestation in the traditional agricultural region where most of the expansion took place. The amount of forests on farmland in this area is below the minimum stated in law and the situation did not change over the studied period. Sugarcane expansion resulted in a significant reduction of pastures and cattle heads and higher economic growth than in neighboring areas. It could not be established to what extent the discontinuation of cattle production induced expansion of pastures in other areas, possibly leading to indirect deforestation. However, the results indicate that a possible migration of the cattle production reached further than the neighboring of expansion regions. Occurring at much smaller rates, expansion of sugarcane in regions such as the Amazon and the Northeast region was related to direct deforestation and competition with food crops, and appear not to have induced economic growth. These regions are not expected to experience substantial increases of sugarcane in the near future, but mitigating measures are warranted.