3 research outputs found

    Crop Updates 2008 - Lupins, Pulses and Oilseeds

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    This session covers twenty six papers from different authors: Regional Roundup 1. SOUTH EAST AGRICULTURAL REGION, Mark Seymour Department of Agriculture and Food, and Robert Johnson CBH Group, Esperance 2. CENTRAL AGRICULTURAL REGION, Ian Pritchard, Department of Agriculture and Food 3. GREAT SOUTHERN AND LAKES REGION, Raj Malik, Department of Agriculture and Food 4. NORTHERN AGRICULTURAL REGION, Wayne Parker and Martin Harries, Department of Agriculture and Food LUPINS 5. Cropping lupins in wide rows in Western Australia, Martin Harries and Bob French, Department of Agriculture and Food 6. The effect of sowing time and radish density on lupin yield, Martin Harries and Jo Walker, Department of Agriculture and Food 7. Lupin agronomy affects crop competitiveness with annual ryegrass, Bob French and Laurie Maiolo, Department of Agriculture and Food 8. Identification of lupin mutants with tolerance to isoxaflutole, Leigh Smith, Department of Agriculture and Food PULSES 9. Chickpea 2007 Crop Variety Testing (CVT) and National Variety Testing (NVT), Alan Harris, Rod Hunter, Tanveer Khan and Jenny Garlinge, Department of Agriculture and Food 10. Desi chickpea breeding: Evaluation of advanced lines, Tanveer Khan1, Poran Gaur2, Kadambot Siddique3, Heather Clarke4, Neil Turner4, William MacLeod4, Stuart Morgan1, Alan Harris1, 1Department of Agriculture and Food, 2International Crop Research Institute for the Semi Arid Tropics (ICRISAT); 3The University of Western Australia; 4Centre for Legumes in Mediterranean Agriculture 11. Can wide rows buffer chickpea growth against dry environments? Bob French and Wendy Vance, Department of Agriculture and Food, and School of Environmental Sciences, Murdoch University 12. Field pea 2007 Crop Variety Testing (CVT) and National Variety Testing (NVT), Alan Harris, Rod Hunter, Tanveer Khan and Jenny Garlinge, Department of Agriculture and Food 13. Australian Field Pea improvement Program (AFPIP): Evaluation of advanced breeding lines, Tanveer Khan1, Phillip Chambers1, Chris Veitch1, Stuart Morgan1, Alan Harris1, and Tony Leonforte 2, 1Department of Agriculture and Food, 2Department of Primary Industries, Victoria 14. Ability of semi-leafless field peas to recover after rolling, Mark Seymour and Rodger Beermier, Department of Agriculture and Food 15. Field pea germplasm enhancement for black spot resistance, Tanveer Khan, Stuart Morgan, Alan Harris and Phillip Chambers, Department of Agriculture and Food 16. Application of ‘Blackspot Manager’ model to identifying a low risk sowing date for field pea in South Australia and Western Australia in 2007, Moin Salam1, Jenny Davidson2, Jean Galloway1, Pip Payne2, Tess Humphries2, Bill MacLeod1 and Art Diggle1, 1Department of Agriculture and Food, 2SARDI, South Australia 17. Late post emergent herbicide sprays for field pea, Mark Seymour and Rodger Beermier, Department of Agriculture and Food 18. Adding triasulfuron to croptopping mixes does not affect the yield of field pea, Mark Seymour, Department of Agriculture and Food 18. Herbicide tolerance of field pea varieties, Harmohinder Dhammu and Mark Seymour, Department of Agriculture and Food 19. Breeding highlights of the PBA lentil program, Michael Materne1, Kerry Regan2, Chris Veitch2 and Phil Chambers2, 1Department of Primary Industries, Victoria 2Department of Agriculture and Food CANOLA 20. How late can I sow canola in 2008? Mohammad Amjad, Andy Sutherland and Pat Fels, Department of Agriculture and Food 21. Direct harvesting canola, Glen Riethmuller1, Wallace Cowling2, Milton Sanders2, Eliot Jones2 and Chris Newman1, 1Department of Agriculture and Food, Western Australia, 2Canola Breeders Western Australia Pty Ltd 22. Agronomic performance of new hybrid canola and juncea canola in low, medium and high rainfall environments of Western Australia, Mohammad Amjad, Andy Sutherland and Pat Fels, Department of Agriculture and Food 23. Comparative performance of new canola varieties in commercial-scale field trials of Oilseeds WA – 2007, Mohammad Amjad1, John Duff2 and David Sermon3 1Department of Agriculture and Food, 2Oilseeds Western Australia and John Duff & Associates, Perth; 3ConsultAg, Perth 24. The effect of rotation crops, trash retention and prophylactic sprays on arthropod abundance in a following canola crop, Svetlana Micic, Anthony Dore and Geoff Strickland, Department of Agriculture and Food OATS 25. Fungicide options for controlling disease in oats, Raj Malik and Blakely Paynter, Department of Agriculture and Food 26. Herbicide tolerance of new oat varieties, Harmohinder Dhammu, Vince Lambert and Chris Roberts, Department of Agriculture and Foo

    Description and Cross-Sectional Analyses of 25,880 Adults and Children in the UK National Registry of Rare Kidney Diseases Cohort

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    Introduction: The National Registry of Rare Kidney Diseases (RaDaR) collects data from people living with rare kidney diseases across the UK, and is the world's largest, rare kidney disease registry. We present the clinical demographics and renal function of 25,880 prevalent patients and sought evidence of bias in recruitment to RaDaR. Methods: RaDaR is linked with the UK Renal Registry (UKRR, with which all UK patients receiving kidney replacement therapy [KRT] are registered). We assessed ethnicity and socioeconomic status in the following: (i) prevalent RaDaR patients receiving KRT compared with patients with eligible rare disease diagnoses receiving KRT in the UKRR, (ii) patients recruited to RaDaR compared with all eligible unrecruited patients at 2 renal centers, and (iii) the age-stratified ethnicity distribution of RaDaR patients with autosomal dominant polycystic kidney disease (ADPKD) was compared to that of the English census. Results: We found evidence of disparities in ethnicity and social deprivation in recruitment to RaDaR; however, these were not consistent across comparisons. Compared with either adults recruited to RaDaR or the English population, children recruited to RaDaR were more likely to be of Asian ethnicity (17.3% vs. 7.5%, P-value < 0.0001) and live in more socially deprived areas (30.3% vs. 17.3% in the most deprived Index of Multiple Deprivation (IMD) quintile, P-value < 0.0001). Conclusion: We observed no evidence of systematic biases in recruitment of patients into RaDaR; however, the data provide empirical evidence of negative economic and social consequences (across all ethnicities) experienced by families with children affected by rare kidney diseases

    Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

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    Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure.People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window).Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p Background Methods Findings Interpretation Funding</p
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