20 resultados para Seasonal variations

em eResearch Archive - Queensland Department of Agriculture


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Decision-making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream-flows in north-eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.

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SUMMARY Seasonal conditions in the pre to post natal period and selected periods before and during wool growth were described using climatic measures and estimates of the quality and quantity of pasture on offer derived from a validated pasture production model (GRASP). The variation in greasy and clean fleece weight, yield, staple length, fibre diameter, neck and side wrinkle score of Merinos grazing Mitchell grass in north west Queensland was explained in terms of these pasture and climatic measures and animal characteristics such as reproductive status, age and skin area. Multiple regression equations predicting clean and greasy fleece weight from the proportion of days in the wool growth period that the green pool in the pasture was less than one kg/ha, the percentage utilisation of the pasture, age, reproductive status and skin area of the ewes explained 87% and 79% of the variation respectively. Equations with similar predictors explained 58-85% of the variation of the other components. The inclusion of pasture conditions in the pre to post natal period did not significantly improve the predictions of the animal’s later performance. 22nd Biennial Conference.

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Phosphonate fungicides are used widely in the control of diseases caused by Phytophthora cinnamomi Rands. For the most part phosphonate is seen as a safe to use on crops with phytotoxicity rare. However, recent research has shown that phosphonate has detrimental effects on the floral biology of some indigenous Australian plants. Since phosphonate fungicides are regularly used for the control of Phytophthora root rot in avocados, research was carried out to study the translocation of phosphonate fungicide in 'Hass' trees and any effects on their floral biology. Field-grown trees were sprayed with 0, 0.06 or 0.12 M mono-dipotassium phosphonate (pH 7.2) at summer flush maturity, floral bud break or anthesis. Following treatment, phosphonic acid concentrations were determined in leaves, roots, inflorescence rachi and flowers and in vitro pollen germination and pollen tube growth studied. Phosphonic acid concentration in the roots and floral parts was related to their sink strength at the respective times of application with concentration in roots highest (36.9.mg g±1) after treatment at summer flush maturity and in flowers (234.7 mg g±1) after treatment during early anthesis. Phosphonate at >0.03 M was found to be significantly phytotoxic to in vitro pollen germination and pollen tube growth. However, this rate gave a concentration far in excess of that measured in plant tissues following standard commercial applications of mono-dipotassium phosphonate fungicide. There was a small effect on pollen germination and pollen tube growth when 0.06 and 0.12 M mono-dipotassium phosphonate was applied during early anthesis. However, under favourable pollination and fruit set conditions it is not expected to have commercial impact on tree yield. However, there may be detrimental commercial implications from phosphonate sprays at early anthesis if unfavourable climatic conditions for pollination and fruit set subsequently occur. A commercial implication from this study is that phosphonic acid root concentrations can be elevated and maintained with strategic foliar applications of phosphonate fungicide timed to coincide with peaks in root sink strength. These occur at the end of the spring and summer flushes when shoot growth is relatively quiescent. Additional foliar applications may be advantageous in under high disease-pressure situations but where possible should be timed to minimize overlap with other significant growth events in the tree such as rapid inflorescence, and fruit development and major vegetative flushing.

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This study highlights the importance of considering how seasonality of rainfall affects availability of resources and consequently species distributions within tropical ecosystems. The endangered northern bettong, Bettongia tropica Wakefield is thought to be restricted to habitats where seasonal availability of hypogeous fungi, their principal food resource, remains high. To test this hypothesis fungal abundance was quantified in the early wet, late wet, early dry and late dry seasons within known bettong habitat. A relationship was found between precipitation and fungal availability, with the abundance of hypogeous fungi being significantly lower in the late dry season. Fungal availability correlated strongly with the seasonal rainfall pattern determined from 74-year monthly means. This contrasts with a previous study where mycophagy, measured by faecal analysis, remained high across seasons presumably because of aseasonal rainfall during that study period. Alloteropsis semialata R.Br. (cockatoo grass) use by bettongs increased significantly during the period of low fungal availability. This suggests that the importance of cockatoo grass as an alternative food resource during annual and extended dry periods has previously been underestimated. With the frequency and intensity of drought expected to increase with global climate change, these findings have significant implications for bettong management. The important and possibly equivalent dependence of B. tropica on both hypogeous fungi and A. semialata helps to explain their habitat preference and identifies this species as a true ecotonal specialist.

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Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

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To quantify the role of Johnson grass, Sorghum halepense, in the population dynamics of the sorghum midge, Stenodiplosis sorghicola, patterns of flowering of Johnson grass and infestation by sorghum midge were studied in two different climatic environments in the Lockyer Valley and on the Darling Downs in south-eastern Queensland for 3 years. Parasitism levels of S. sorghicola were also recorded. In the Lockyer Valley, Johnson grass panicles were produced throughout the year but on the Darling Downs none were produced between June and September. In both areas, most panicle production occurred between November and March and infestation by S. sorghicola was the greatest during this period. The parasitism levels were between 20% and 50%. After emergence from winter diapause, one to two generations of S. sorghicola developed on S. halepense before commercial grain sorghum crops were available for infestation. Parasitoids recorded were: Aprostocetus diplosidis, Eupelmus australiensis and two species of Tetrastichus. Relationships between sorghum midge population growth rate and various environmental and population variables were investigated. Population size had a significant negative effect (P < 0.0001) on population growth rate. Mortality due to parasitism showed a significant positive density response (P < 0.0001). Temperature, rainfall, open pan evaporation, degree-days and host availability showed no significant effect on population growth rate. Given the phenology of sorghum production in south-eastern Queensland, Johnson grass provides an important bridging host, sustaining one to two generations of sorghum midge. Critical studies relating population change and build-up in sorghum to sorghum midge populations in Johnson grass are yet to be performed.

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Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

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This paper describes a study to identify those factors which control the persistence of the Subtropical legume Stylosanthes hippocampoides, formerly S. guianensis cv. Oxley (fine stem stylo). The dynamics of S. hippocampoides populations was recorded in permanent quadrats at 2 stocking rates in a grazing study conducted between 1987 and 1992 in south-eastern Queensland. Density of mature plants fluctuated between 10 and 60 plants/m(2) during the 5 years with the major contributing factors being variations in seedling recruitment and survival, which, in turn, reflected the size of the soil seed bank and seasonal rainfall. Plant density was consistently higher at the lower stocking rate of 1 beast/1.5 ha compared with 1 beast/1 ha; however, the effect of stocking rate was minor compared with fluctuation due to seasonal variation in rainfall. The maximum life span of the original plants exceeded 5 years, while the survival of seedling cohorts was strongly impacted by seasonal rainfall. Total exclosure from grazing during summer increased the size of the soil seed bank although a precise time period during summer was not identified, while grazing at the lower stocking pressure produced the same outcome. It was concluded that the large seasonal variation that occurs in S. hippocampoides density is driven by large seasonal variation in seedling recruitment, which, in turn, is influenced by the size of the soil seed bank.

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Bemisia tabaci, biotype B, commonly known as the silverleaf whitefly (SLW) is an alien species that invaded Australia in the mid-90s. This paper reports on the invasion ecology of SLW and the factors that are likely to have contributed to the first outbreak of this major pest in an Australian cotton cropping system, population dynamics of SLW within whitefly-susceptible crop (cotton and cucurbit) and non-crop vegetation (sowthistle, Sonchus spp.) components of the cropping system were investigated over four consecutive growing seasons (September-June) 2001/02-2004/05 in the Emerald Irrigation Area (EIA) of Queensland, Australia. Based on fixed geo-referenced sampling sites, variation in spatial and temporal abundance of SLW within each system component was quantified to provide baseline data for the development of ecologically sustainable pest management strategies. Parasitism of large (3rd and 4th instars) SLW nymphs by native aphelinid wasps was quantified to determine the potential for natural control of SLW populations. Following the initial outbreak in 2001/02, SLW abundance declined and stabilised over the next three seasons. The population dynamics of SLW is characterised by inter-seasonal population cycling between the non-crop (weed) and cotton components of the EIA cropping system. Cotton was the largest sink for and source of SLW during the study period. Over-wintering populations dispersed from weed host plant sources to cotton in spring followed by a reverse dispersal in late summer and autumn to broad-leaved crops and weeds. A basic spatial source-sink analysis showed that SLW adult and nymph densities were higher in cotton fields that were closer to over-wintering weed sources throughout spring than in fields that were further away. Cucurbit fields were not significant sources of SLW and did not appear to contribute significantly to the regional population dynamics of the pest. Substantial parasitism of nymphal stages throughout the study period indicates that native parasitoid species and other natural enemies are important sources of SLW mortality in Australian cotton production systems. Weather conditions and use of broad-spectrum insecticides for pest control are implicated in the initial outbreak and on-going pest status of SLW in the region.

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The variation in liveweight gain in grazing beef cattle as influenced by pasture type, season and year effects has important economic implications for mixed crop-livestock systems and the ability to better predict such variation would benefit beef producers by providing a guide for decision making. To identify key determinants of liveweight change of Brahman-cross steers grazing subtropical pastures, measurements of pasture quality and quantity, and diet quality in parallel with liveweight were made over two consecutive grazing seasons (48 and 46 weeks, respectively), on mixed Clitoria ternatea/grass, Stylosanthes seabrana/grass and grass swards (grass being a mixture of Bothriochloa insculpta cv. Bisset, Dichanthium sericeum and Panicum maximum var. trichoglume cv. Petrie). Steers grazing the legume-based pastures had the highest growth rate and gained between 64 and 142 kg more than those grazing the grass pastures in under 12 months. Using an exponential model, green leaf mass, green leaf %, adjusted green leaf % (adjusted for inedible woody legume stems), faecal near infrared reflectance spectroscopy predictions of diet crude protein and diet dry matter digestibility, accounted for 77, 74, 80, 63 and 60%, respectively, of the variation in daily weight gain when data were pooled across pasture types and grazing seasons. The standard error of the regressions indicated that 95% prediction intervals were large (+/- 0.42-0.64 kg/head.day) suggesting that derived regression relationships have limited practical application for accurately estimating growth rate. In this study, animal factors, especially compensatory growth effects, appeared to have a major influence on growth rate in relation to pasture and diet attributes. It was concluded that predictions of growth rate based only on pasture or diet attributes are unlikely to be accurate or reliable. Nevertheless, key pasture attributes such as green leaf mass and green leaf% provide a robust indication of what proportion of the potential growth rate of the grazing animals can be achieved.

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We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

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The growth of the Australian eastern king prawn (Melicertus plebejus) is understood in greater detail by quantifying the latitudinal effect. The latitudinal effect is the change in the species’ growth rate during migration. Mark–recapture data (N = 1635, latitude 22.21°S–34.00°S) presents northerly movement of the eastern king prawn, with New South Wales prawns showing substantial average movement of 140 km (standard deviation: 176 km) north. A generalized von Bertalanffy growth model framework is used to incorporate the latitudinal effect together with the canonical seasonal effect. Applying this method to eastern king prawn mark–recapture data guarantees consistent estimates for the latitudinal and seasonal effects. For M. plebejus, it was found that growth rate peaks on 25 and 29 January for males and females, respectively; is at a minimum on 27 and 31 July, respectively; and that the shape parameter, k (per year), changes by –0.0236 and –0.0556 every 1 degree of latitude south increase for males and females, respectively.

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Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact 'Hass' avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The R-v(2) and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but <= 1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of R-v(2) = 0.89, RMSEP = 1.43% dry matter with a bias of -0.021 in the range 16.1-39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890-980, 1005-1050, 1330-1380 and 1700-1790 nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole 'Hass' avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.

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Thrips can be important pests of capsicum and chilli crops, causing damage through their feeding and by vectoring viral diseases. As different species vary in their ability to transmit viruses and in their susceptibility to insecticides, it is important to know which species are present in a crop. The seasonal occurrence of thrips in capsicum and chilli crops in the Bundaberg district of south-east Queensland was investigated from July 2002 to June 2003. Fifty flowers were collected weekly from crops on seven farms and the adult thrips extracted and identified. Thrips palmi Karny and Frankliniella occidentalis (Pergande) were collected in the greatest numbers, with T. palmi predominant in autumn crops (March to July) and F. occidentalis predominant in spring crops (August to November). Pseudanaphothrips achaetus (Bagnall) was common, while Thrips tabaci Lindeman, Thrips imaginis Bagnall and Frankliniella schultzei (Trybom) were collected in low numbers.