Weather variables, water balance, growth, and agro industrial yield of sugarcane

The objective of this study was to evaluate the meteorological variables, water deficiency, growth, and agro-industrial yield of sugarcane varieties: RB72454, RB863129, RB867515, RB92579, RB93509, RB931003, RB951541, and RB971755, in rainfed crop in two harvests in the Rio Largo-AL region. The meteorological variables were obtained in an automatic station and water balance was done by Thornthwaite & Mather method. During the study period, the air temperature ranged from 16.6 to 35.9 ºC. In the first production cycle rained 1,806 mm and the crop evapotranspiration was 1,775 mm. In the second cycle, the rainfall totaled 1,632 mm and the crop evapotranspiration was 1,290 mm. The average water excess of two production cycles was 689 mm and the water deficit totaled 665 mm. The average agricultural productivity in the plant was 86.8 t ha-1, in the first ratoon was 75.2 t ha-1 and the agro-industrial yield average was 12.9 and 10.9 tons of sugar per hectare in the plant and first ratoon, respectively. The air temperature was not limiting to the growth of sugarcane and the rainfall was higher than the crop evapotranspiration, but due to poor distribution of the rains there was water deficit. The most productive varieties were RB93509, RB92579, and RB863129.

Weather variables, water balance, growth, and agro industrial yield of sugarcane

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Modelling the population dynamics of the Queensland fruit fly, Bactrocera (Dacus) tryoni: a cohort-based approach incorporating the effects of weather

Queensland fruit fly, Bactrocera (Dacus) tryoni (QFF) is arguably the most costly horticultural insect pest in Australia. Despite this, no model is available to describe its population dynamics and aid in its management. This paper describes a cohort-based model of the population dynamics of the Queensland fruit fly. The model is primarily driven by weather variables, and so can be used at any location where appropriate meteorological data are available. In the model, the life cycle is divided into a number of discreet stages to allow physiological processes to be defined as accurately as possible. Eggs develop and hatch into larvae, which develop into pupae, which emerge as either teneral females or males. Both females and males can enter reproductive and over-wintering life stages, and there is a trapped male life stage to allow model predictions to be compared with trap catch data. All development rates are temperature-dependent. Daily mortality rates are temperature-dependent, but may also be influenced by moisture, density of larvae in fruit, fruit suitability, and age. Eggs, larvae and pupae all have constant establishment mortalities, causing a defined proportion of individuals to die upon entering that life stage. Transfer from one immature stage to the next is based on physiological age. In the adult life stages, transfer between stages may require additional and/or alternative functions. Maximum fecundity is 1400 eggs per female per day, and maximum daily oviposition rate is 80 eggs/female per day. The actual number of eggs laid by a female on any given day is restricted by temperature, density of larva in fruit, suitability of fruit for oviposition, and female activity. Activity of reproductive females and males, which affects reproduction and trapping, decreases with rainfall. Trapping of reproductive males is determined by activity, temperature and the proportion of males in the active population. Limitations of the model are discussed. Despite these, the model provides a useful agreement with trap catch data, and allows key areas for future research to be identified. These critical gaps in the current state of knowledge exist despite over 50 years of research on this key pest. By explicitly attempting to model the population dynamics of this pest we have clearly identified the research areas that must be addressed before progress can be made in developing the model into an operational tool for the management of Queensland fruit fly. (C) 2003 Published by Elsevier B.V.

Peach brown rot incidence related to pathogen infection at different stages of fruit development in an organic peach production system

Brown rot, caused by Monilinia fructicola, is the most widespread disease for organic peach production systems in Brazil. The objective of this study was to determine the favorable periods for latent infection by M. fructicola in organic systems. The field experiment was carried out during 2006, 2007 and 2008 using the cultivar Aurora. After thinning fruits were bagged using white paraffin bags, and the treatments were performed by removing the bags and exposing the fruit for four days to the natural infection during each of seven fruit stages from pit hardening to harvest. Throughout the entire growing season, the conidial density and the weather variables were measured and related to the disease incidence using multiple regression analyses. At the fourth day after harvest in each season, the cumulative disease incidence was assessed, and it ranged from 40 to 98%. The incidence of brown rot on fruit that were exposed during the embryo growing stage was lower than that of unbagged fruit throughout the entire season in 2006 and 2008. The relative humidity and the conidia density were significantly correlated to disease incidence. Based on our results, M. fructicola can infect peaches during any stage of fruit development, and control of the disease must be revised to account for organic peach production systems. (C) 2011 Elsevier Ltd. All rights reserved.

Long range forecasts of the numbers of Helicoverpa punctigera and H-armigera (Lepidoptera : Noctuidae) in Australia using the Southern Oscillation Index and the Sea Surface Temperature

The use of long-term forecasts of pest pressure is central to better pest management. We relate the Southern Oscillation Index (SOI) and the Sea Surface Temperature (SST) to long-term light-trap catches of the two key moth pests of Australian agriculture, Helicoverpa punctigera (Wallengren) and H. armigera (Hubner), at Narrabri, New South Wales over 11 years, and for H. punctigera only at Turretfield, South Australia over 22 years. At Narrabri, the size of the first spring generation of both species was significantly correlated with the SOI in certain months, sometimes up to 15 months before the date of trapping. Differences in the SOI and SST between significant months were used to build composite variables in multiple regressions which gave fitted values of the trap catches to less than 25% of the observed values. The regressions suggested that useful forecasts of both species could be made 6-15 months ahead. The influence of the two weather variables on trap catches of H. punctigera at Turretfield were not as strong as at Narrabri, probably because the SOI was not as strongly related to rainfall in southern Australia as it is in eastern Australia. The best fits were again given by multiple regressions with SOI plus SST variables, to within 40% of the observed values. The reliability of both variables as predictors of moth numbers may be limited by the lack of stability in the SOI-rainfall correlation over the historical record. As no other data set is available to test the regressions, they can only be tested by future use. The use of long-term forecasts in pest management is discussed, and preliminary analyses of other long sets of insect numbers suggest that the Southern Oscillation Index may be a useful predictor of insect numbers in other parts of the world.

Shrews in suburbia: an application of Goodman's extinction model

An equation is applied for calculating the expected persistence time of an unstructured population of the white-toothed shrew Crocidura russula from Preverenges, a suburban area in western Switzerland. Population abundance data from March and November between 1977 and 1988 were fit to the logistic density dependence model to estimate mean population growth rate as a function of population density. The variance in mean growth rate was approximated with two different models. The largest estimated persistence time was less than a few decades, the smallest less than 10 years. The results are sensitive to the magnitude of variance in population growth rate. Deviations from the logistic density dependence model in November are quite well explained by weather variables but those in March are uncorrelated with weather variables. Variability in population growth rates measured in winter months may be better explained by behavioural mechanisms. Environmental variability, dispersal of juveniles and refugia within the range of the population may contribute to its long-term survival.

Quantifying Uncertainty in Real Time Performance Measurement for Highway Winter Maintenance Operations – Phase 2, RB03-013, 2014

Winter weather in Iowa is often unpredictable and can have an adverse impact on traffic flow. The Iowa Department of Transportation (Iowa DOT) attempts to lessen the impact of winter weather events on traffic speeds with various proactive maintenance operations. In order to assess the performance of these maintenance operations, it would be beneficial to develop a model for expected speed reduction based on weather variables and normal maintenance schedules. Such a model would allow the Iowa DOT to identify situations in which speed reductions were much greater than or less than would be expected for a given set of storm conditions, and make modifications to improve efficiency and effectiveness. The objective of this work was to predict speed changes relative to baseline speed under normal conditions, based on nominal maintenance schedules and winter weather covariates (snow type, temperature, and wind speed), as measured by roadside weather stations. This allows for an assessment of the impact of winter weather covariates on traffic speed changes, and estimation of the effect of regular maintenance passes. The researchers chose events from Adair County, Iowa and fit a linear model incorporating the covariates mentioned previously. A Bayesian analysis was conducted to estimate the values of the parameters of this model. Specifically, the analysis produces a distribution for the parameter value that represents the impact of maintenance on traffic speeds. The effect of maintenance is not a constant, but rather a value that the researchers have some uncertainty about and this distribution represents what they know about the effects of maintenance. Similarly, examinations of the distributions for the effects of winter weather covariates are possible. Plots of observed and expected traffic speed changes allow a visual assessment of the model fit. Future work involves expanding this model to incorporate many events at multiple locations. This would allow for assessment of the impact of winter weather maintenance across various situations, and eventually identify locations and times in which maintenance could be improved.

Factors affecting density of airborne Gibberella zeae inoculum

Fusarium head blight (FHB) is a disease of increasing concern in the production of wheat (Triticum aestivum). This work studied some of the factors affecting the density of airborne Gibberella zeae inoculum. Spore samplers were placed at the edge of a field in order to observe spore deposition over a period of 45 days and nights in September and October, the period that coincides with wheat flowering. Gibberella zeae colonies were counted for each period and values transformed to relative density. A stepwise regression procedure was used to identify weather variables helpful in predicting spore cloud density. In general, a predominant night-time spore deposition was observed. Precipitation and daily mean relative humidity over 90% were the factors most hightly associated with peak events of spores in the air. Models for predicting spore cloud density simulated reasonably well with the fluctuation of airborne propagules during both night and day, with potential to be integrated into an FHB risk model framework.

Effect of air pollution on diabetes and cardiovascular diseases in São Paulo, Brazil

Type 2 diabetes increases the risk of cardiovascular mortality and these patients, even without previous myocardial infarction, run the risk of fatal coronary heart disease similar to non-diabetic patients surviving myocardial infarction. There is evidence showing that particulate matter air pollution is associated with increases in cardiopulmonary morbidity and mortality. The present study was carried out to evaluate the effect of diabetes mellitus on the association of air pollution with cardiovascular emergency room visits in a tertiary referral hospital in the city of São Paulo. Using a time-series approach, and adopting generalized linear Poisson regression models, we assessed the effect of daily variations in PM10, CO, NO2, SO2, and O3 on the daily number of emergency room visits for cardiovascular diseases in diabetic and non-diabetic patients from 2001 to 2003. A semi-parametric smoother (natural spline) was adopted to control long-term trends, linear term seasonal usage and weather variables. In this period, 45,000 cardiovascular emergency room visits were registered. The observed increase in interquartile range within the 2-day moving average of 8.0 µg/m³ SO2 was associated with 7.0% (95%CI: 4.0-11.0) and 20.0% (95%CI: 5.0-44.0) increases in cardiovascular disease emergency room visits by non-diabetic and diabetic groups, respectively. These data indicate that air pollution causes an increase of cardiovascular emergency room visits, and that diabetic patients are extremely susceptible to the adverse effects of air pollution on their health conditions.

Methodologies for the generation of design summer years for building energy simulation using UKCP09 probabilitic climate projections

Design summer years representing near-extreme hot summers have been used in the United Kingdom for the evaluation of thermal comfort and overheating risk. The years have been selected from measured weather data basically representative of an assumed stationary climate. Recent developments have made available ‘morphed’ equivalents of these years by shifting and stretching the measured variables using change factors produced by the UKCIP02 climate projections. The release of the latest, probabilistic, climate projections of UKCP09 together with the availability of a weather generator that can produce plausible daily or hourly sequences of weather variables has opened up the opportunity for generating new design summer years which can be used in risk-based decision-making. There are many possible methods for the production of design summer years from UKCP09 output: in this article, the original concept of the design summer year is largely retained, but a number of alternative methodologies for generating the years are explored. An alternative, more robust measure of warmth (weighted cooling degree hours) is also employed. It is demonstrated that the UKCP09 weather generator is capable of producing years for the baseline period, which are comparable with those in current use. Four methodologies for the generation of future years are described, and their output related to the future (deterministic) years that are currently available. It is concluded that, in general, years produced from the UKCP09 projections are warmer than those generated previously. Practical applications: The methodologies described in this article will facilitate designers who have access to the output of the UKCP09 weather generator (WG) to generate Design Summer Year hourly files tailored to their needs. The files produced will differ according to the methodology selected, in addition to location, emissions scenario and timeslice.

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Climate and construction delays: case study in Chile

Purpose – Construction projects usually suffer delays, and the causes of these delays and its cost overruns have been widely discussed, the weather being one of the most recurrent. The purpose of this paper is to analyze the influence of climate on standard construction work activities through a case study. Design/methodology/approach – By studying the extent at which some weather variables impede outdoor work from being effectively executed, new maps and tables for planning for delays are presented. In addition, a real case regarding the construction of several bridges in southern Chile is analyzed. Findings – Few studies have thoroughly addressed the influences of major climatic agents on the most common outdoor construction activities. The method detailed here provides a first approximation for construction planners to assess to what extent construction productivity will be influenced by the climate. Research limitations/implications – Although this study was performed in Chile, the simplified method proposed is entirely transferable to any other country, however, other weather or combinations of weather variables could be needed in other environments or countries. Practical implications – The implications will help reducing the negative social, economic and environmental outcomes that usually emerge from project delays. Originality/value – Climatic data were processed using extremely simple calculations to create a series of quantitative maps and tables that would be useful for any construction planner to decide the best moment of the year to start a project and, if possible, where to build it.

Susceptibility of mosquito vectors to Dirofilaria immitis on Madeira Island, Portugal

Dirofilaria immitis (Leidy, 1856), an agent of heartworm disease, is an important parasite from both the veterinary standpoint and as a model to study human filariasis. It is a mosquito-borne filarial nematode which inhabits the right ventricle and pulmonary arteries of dogs. D. immitis is an important disease agent on Madeira Island with about 30% of dogs testing positive for this worm. Nevertheless, the vectors of this parasite in Madeira have never been studied, nor has the interaction between pathogen and vector, or the environmental variables that might influence heartworm transmission. Innate susceptibility to infection is only one component of vector competence, and field isolation of naturally infected mosquitoes has shown the capability of D. immitis to exploit a great diversity of vector species under natural conditions. The purpose of this work was to determine which mosquitoes are vectors of heartworm disease, the relation between population density and environment, and the association between immune response of the vector to the filarial parasite. Seasonal abundance of Culex theileri and Culex pipiens molestus was studied. Correlation and canonical correspondence analysis were performed using abundance data of these two species with selected weather variables, including mean temperature, relative humidity and accumulated precipitation. The most important factor determining Cx. theileri abundance was accumulated precipitation, while Cx. pipiens molestus abundance did not have any relationship with weather variables. Field studies were performed to verify whether Cx. theileri Theobald functions as a natural vector of D. immitis on Madeira Island, Portugal. Cx. theileri tested positive for D. immitis for the first time. The same study was made regarding Cx. p. molestus. Two abnormal L2 stage filarial worms were found in Malpighian tubules in field caught Cx. p. molestus. In the laboratory, two strains of Cx. p. molestus were studied for their susceptibility to D. immitis. None presented infective-stage larvae. Finally, because Cx. p. molestus is an autogenous mosquito, we evaluated the reproductive costs when this mosquito mounts an immune response against D. immitis in the absence of a blood meal. This mosquito showed an active immune response when inoculated intrathoracically with microfilariae (mf) of the heartworm. The ovaries from mosquitoes undergoing melanotic encapsulation developed more eggs than those which could not melanize the mf. This fact is contradictory with some previous studies of reproductive costs in Armigeres subalbatus and Ochlerotatus trivittatus, and it was the first time that an autogenous mosquito was used to study this subject.

Índice de área foliar e produtividade do tomate sob condições de ambiente protegido

The tomato cultivation in the greenhouse has been expanded in the last years, mainly, in the South and Southeast regions of Brazil, whose purpose is to improve the productivity and the quality of the agricultural products, offering regularity in the production. The present study aimed to determine, along the crop cycle, the relationship between the leaf area index and the productivity, and at the end of the cycle, the components of production of the tomato in the greenhouse. The models were generated through polynomial equations of 1st and 2nd order, having as independent variable the number of days after the transplanting. It was verified that it is possible to determine, in the greenhouse, through mathematical models, the leaf area index of the tomato crop considering the days after the transplanting. Basing on values of leaf area index, the productivity of the crop and the period of the maximum productivity can be determined, aiding the farmers to determine the best sowing and transplanting time of the tomato crop.