Relationship between the Southern Oscillation Index and Australian sugarcane yields

1994 ◽  
Vol 45 (7) ◽  
pp. 1557 ◽  
Author(s):  
I Kuhnel
Keyword(s):  
Southern Hemisphere ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Significance Testing ◽  
The Past ◽  
Northern Areas ◽  
Eastern Australia ◽  
North Eastern ◽  
Sugarcane Yield ◽  
The Relationship

This study examines the relationship between the Southern Oscillation Index and the sugarcane yield anomalies at 27 mills in north-eastern Australia (Queensland) for the period 1950-1989. The major results of this work indicate that the SO1 alone seems to have only a limited value as predictor of total sugarcane yields over large areas (i.e. the whole of Queensland). However, on a smaller scale, the SO1 appears to be a useful indicator of yields for the northern sugarcane districts. In these northern areas, the highest correlations with the SO1 are reached during the southern hemisphere spring and summer months 6 to 11 months prior to the harvest. They are negative and explain about 40% of the total variance. They also suggest that a positive SO1 during the spring and summer months tends to be followed by lower-than-normal yields at the following harvest and vice versa. This signal is rather robust and withstands rigorous significance testing. Moreover, it appears that the relationship between the SO1 and the sugarcane yields has been relatively strong and stable for the past 40 years, but weakened substantially during the 1930-1940 period.

scholarly journals The Correlation of Southern Oscillation Index (SOI) with the Rainfall at Rainy and Dry Season Peak in Kintamani-Bangli Region period 1986-2015

2019 ◽  
Vol 20 (2) ◽  
pp. 6
Author(s):  
Wayan Mita Restitiasih ◽  
I Ketut Sukarasa ◽  
I Wayan Andi Yuda
Keyword(s):  
Correlation Coefficient ◽  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Management Plan ◽  
Correlation Study ◽  
Dry Season ◽  
Rainfall Data ◽  
The Relationship

A correlation study of the Southern Oscillation Index (SOI) on rainfall at the peak of the wet and dry season in the Kintamani-Bangli region has been carried out by taking SOI values and rainfall data for the period 1986-2015. The rainfall data used were recorded at 2 rain posts, namely Kembangsari and Kintamani. The research aimed to determine the relationship of fluctuations in the value of SOI with the intensity of rainfall, so that it can be used as a regional management plan when El Nino occurs. The method used in this study is correlation. The results obtained from the correlation that is the relationship between SOI value and rainfall in February were quite strong in the Kembangsari post with correlation coefficient of 0.409. Whereas for the Kintamani post the correlation obtained was weak with a correlation coefficient of 0.308. Then in August a weak correlation occurred in the Kembangsari post with a correlation coefficient of 0.2398 and was quite strong in the Kintamani post with a correlation coefficient of 0.4662. So that the influence of El Nino in the Kintamani area in February was more dominant in the Kembangsari post and in August at the Kintamani post.

The Southern Oscillation Index as a predictor of seasonal rainfall in the arable areas of the inland Australian subtropics

1993 ◽  
Vol 44 (6) ◽  
pp. 1337 ◽  
Author(s):  
JS Russell ◽  
IM McLeod ◽  
MB Dale ◽  
TR Valentine
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Four Seasons ◽  
Eastern Australia ◽  
Australian Continent ◽  
Ace Algorithm ◽  
Linear Trends

A detailed study has been carried out in four regions in the subtropics of Eastern Australia to determine the relationship between the Southern Oscillation Index (SOI) and subsequent seasonal rainfall. The period studied was from 1915 to 1991 for 3-monthly periods of spring (SON), summer (DJF), autumn (MAM) and winter (JJA). The 3-monthly prior SOI values were plotted against seasonal rainfall of the four regions and four seasons. These data were widely scattered but with a linear trend showing increased seasonal rainfall as the SOI increased. Linear trends were plotted for each season and region. Comparisons were made between the use of the ACE algorithm, which transforms the SOI and rainfall data, and the use of linear trends. Polynomials were used to calculate equations for each region and season, but only spring and summer produced satisfactory ACE functions. Estimates were made of spring and summer rainfall relative to prior SOI values for each region. While the SOI as a predictor of rainfall broadly estimates spring and summer rainfall, this variable has limited usefulness on its own. One of the options available with the ACE program is that additional independent variables can be added as required. Current research suggests that sea surface temperature data from specific ocean areas surrounding the Australian continent is the most useful additional variable at present. However the complexity of such an analysis is greatly increased.

Responses of serradella and subterranean clover to phosphorus from superphosphate and Duchess rock phosphate

1985 ◽  
Vol 25 (4) ◽  
pp. 902 ◽  
Author(s):  
MDA Bolland
Keyword(s):  
Rock Phosphate ◽  
Phosphorus Content ◽  
Subterranean Clover ◽  
Fertilizer Application ◽  
Sampling Time ◽  
Eastern Australia ◽  
North Eastern ◽  
Herbage Yield ◽  
Seed Yields ◽  
The Relationship

The responses of yellow serradella, slender serradella and subterranean clover to phosphorus from superphosphate and an apatite rock phosphate from Duchess, north-eastern Australia, were measured in a field experiment near Esperance, W.A. Over the 16 months of the experiment, dry herbage yields depended upon the phosphorus content of dried herbage. For each sampling time, the relationship between dry herbage yield and the phosphorus content of dried herbage was similar for both fertilizers and for all three species. At each rate of fertilizer application, the amount of phosphorus absorbed by the plants from the rock phosphate was less than that from superphosphate, and this limited yield. When fertilized with superphosphate, subterranean clover absorbed less phosphorus than the serradellas for each rate of fertilizer application, and this also reduced yield. Thus less phosphorus from superphosphate was required to produce serradella than was required to produce the same weight of subterranean clover. When fertilized with rock phosphate, 1982 herbage production was in the order: yellow serradella> subterranean clover>slender serradella. 1982 seed yields were: subterranean clover>yellow serradella> slender serradella. 1 983 herbage yields were: slender serradella>yellow serradella> subterranean clover. For herbage yields, for each rate of fertilizer application, this order was also dictated by the amount of phosphorus absorbed by each of the three species.

scholarly journals Forecasting rainfall based on the Southern Oscillation Index phases at longer lead-times in Australia

2013 ◽  
Vol 35 (4) ◽  
pp. 373 ◽  
Author(s):  
David H. Cobon ◽  
Nathan R. Toombs
Keyword(s):  
Key Management ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Warm Season ◽  
Lead Times ◽  
Phase System ◽  
Management Decisions ◽  
Operational Forecasts ◽  
Eastern Australia ◽  
Forecast Quality

Under the extensive grazing conditions experienced in Australia, pastoralists would benefit from a long lead-time seasonal forecast issued for the austral warm season (November–March). Currently operational forecasts are issued publicly for rolling 3-month periods at lead-times of 0 or 1 month, usually without an indication of forecast quality. The short lag between the predictor and predictand limits use of forecasts because pastoralists operating large properties have insufficient time to implement key management decisions. The ability to forecast rainfall based on the Southern Oscillation Index (SOI) phase system was examined at 0–5-month lead-times for Australian rainfall. The SOI phase system provided a shift of adequate magnitude in the rainfall probabilities (–40 to +30%) and forecast quality for the 5-month austral warm season at lead-times >0 months. When data used to build the forecast system were used in verification, >20% of locations had a significant linear error in probability space (LEPS) and Kruskal–Wallis (KW) test for lead-times of 0–2 months. The majority of locations showing forecast quality were in northern Australia (north of 25°S), predominately in north-eastern Australia (north of 25°S, east of 140°E). Pastoralists in these areas can now apply key management decisions with more confidence up to 2 months before the November–March period. Useful lead-times of ≥3 months were not found.

scholarly journals Will the Australian nuclear medicine technologist workforce meet anticipated health care demands?

2008 ◽  
Vol 32 (2) ◽  
pp. 282 ◽  
Author(s):  
Edwina Adams ◽  
Deborah Schofield ◽  
Jennifer Cox ◽  
Barbara Adamson
Keyword(s):  
Nuclear Medicine ◽  
Census Data ◽  
Service Needs ◽  
The Past ◽  
Eastern Australia ◽  
North Eastern ◽  
Nuclear Medicine Technologist ◽  
Professional Body ◽  
Care Demands

Determination of national nuclear medicine technologist workforce size was made from census data in 2001 and 1996 and from the professional body in 2004. A survey conducted by the authors in 2005 provided retention patterns in north-eastern Australia and suggested causes. Utilisation of nuclear medicine diagnostic services was established through the Medicare Benefits Schedule group statistics. More than half the nuclear medicine technologist workforce is under 35 years of age. Attrition commences from age 30, with very few workers over 55 years. In 2005 there was a 12% attrition of the survey workforce. In the past decade, service provision increased while workforce size decreased and the nuclear medicine technologist workforce is at risk of failing to meet the anticipated rise in health service needs.

scholarly journals Redclaw crayfish (Cherax quadricarinatus): spatial distribution and dispersal pattern in Java, Indonesia

2021 ◽  
pp. 16
Author(s):  
Surya Gentha Akmal ◽  
Agus Santoso ◽  
Yonvitner ◽  
Ernik Yuliana ◽  
Jiří Patoka
Keyword(s):  
Spatial Distribution ◽  
New Records ◽  
Spatial Behaviour ◽  
Native Range ◽  
Field Sampling ◽  
Dispersal Pattern ◽  
Cherax Quadricarinatus ◽  
Eastern Australia ◽  
North Eastern ◽  
The Relationship

Cherax quadricarinatus is a parastacid crayfish native to parts of north-eastern Australia and southern New Guinea. It is a relatively large and highly fecund species in comparison with other crayfish of this genus. Since C. quadricarinatus was previously assessed as an invasive species in Indonesia, further monitoring of this species in this region was recommended. Detailed understanding of its spatial behaviour can be the basis for further research aimed at improved management. Field sampling was performed outside its native range in Java, Indonesia from 2019–2020, resulting in data useful for modelling the species' spatial distribution. The occurrence of the species was confirmed in 66 of 70 surveyed localities with 51 new records for Indonesia. Future investigations focused on the relationship between the spatial distribution and dispersal pattern of C. quadricarinatus and its interactions with native biota and entire ecosystems were recommended.

The resolution and potential value of Australian seasonal rainfall forecasts based on the five phases of the Southern Oscillation Index

2009 ◽  
Vol 60 (3) ◽  
pp. 230 ◽  
Author(s):  
Andrew L. Vizard ◽  
Garry A. Anderson
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Mean Value ◽  
Expected Value ◽  
Variance Ratio ◽  
Large Shift ◽  
Potential Value ◽  
Eastern Australia ◽  
Forecasting System ◽  
Cape York

We assess the resolution of the Southern Oscillation Index (SOI) seasonal rainfall forecasting system and calculate the loss in potential value of the forecasting system using a cost/loss model. Forecasts of the probability of a ‘dry’ autumn, winter, spring, and summer were obtained for 226 towns across Australia, based on the 5 phases of the SOI. For every town the variance ratio, the observed forecast variance as a proportion of the variance of a perfect forecasting system, was calculated for each season. Value score curves, showing the expected value of the forecasts as a proportion of the expected value of perfect information, were calculated for every town for each season. Maps of variance ratio and maps of mean value scores across Australia were produced by ordinary kriging. In all seasons and regions the SOI forecasting system had a variance ratio of less than 0.20, indicating that resolution and skill were never high. Variance ratios greater than 0.10 only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. The variance ratio was less than 0.05 for the majority of Australia during autumn, winter, and summer. The mean value scores for actions that are only triggered by a large shift in the forecast from climatology were uniformly close to zero in all seasons and regions, indicating that little or no value can be derived in such cases. Actions triggered by a moderate shift of the forecast were also generally associated with low value scores. Mean value scores above 0.20 were limited to actions with a decision threshold close to climatology and only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. We conclude that the imperfect resolution of the SOI forecasting system has a substantial effect on potential value. The forecasting system can potentially deliver value to users with actions that are triggered by a small shift in the forecast from climatology, especially in eastern Australia during spring, but not to users with actions that are only triggered by a large shift of the forecasts from climatology.

Sign in / Sign up

Export Citation Format

Share Document