Management of early sown wheat: matching genotype to environment

Kenton Porker1, James Hunt2*, Felicity Harris3, Sarah Noack4, Michael Moodie5, Kelly Angel6, Michael Straight7, Genevieve Clarke6, Dylan Bruce8, Ashley Wallace9, Neil Fettell10, Greg Brooke11, Helen McMillan10, Barry Haskins12, Mick Brady5, Todd McDonald5, Brenton Spriggs13, Sue Buderick13, Darcy Warren7.

1 South Australian Research and Development Institute, Hartley Grove, Urrbrae SA 5064

2 Department of Plant, Animal and Soil Sciences, La Trobe University, 5 Ring Rd, Bundoora VIC 3086

3 NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga NSW 2650

4 Hart Field-Site Group, 155 Main North Rd, Clare SA 5453

5 Frontier Farming Systems, 7B Byrne Ct, Mildura VIC 3500

6 BCG, 73 Cumming Ave, Birchip VIC 3483

7 FAR Australia, 4/97-103 Melbourne St, Mulwala NSW 2647

8 South Australian Research and Development Institute, 70 Farrell Flat Rd, Clare SA 5453

9 Agriculture Victoria, 110 Natimuk Rd, Horsham VIC 3400

10 CWFS, 1 Fifield Rd, Condobolin NSW 2877

11 NSW Department of Primary Industries, Trangie Agricultural Research Centre, PMB 19, Trangie NSW 2823

12 AgGrow Agronomy & Research, 7 Francine Ct, Yoogali NSW 2680

13 South Australian Research and Development Institute, McKenzie Rd, Minnipa SA 5654

*Presenting author; j.hunt@latrobe.edu.au

Abstract

Australian wheat breeding programs have responded to the need for cultivars better suited to early sowing and have begun releasing a new generation of winter wheats. However, it is not known which of the new cultivars are best adapted to different environments across the wheat belt, or over what period they can be established and still achieve yields competitive with spring wheats sown in their optimal window. We grew four new winter cultivars and four elite spring checks in experiments with four times of sowing (mid-March, early-April, mid-April, early-May) in 11 different environments across SE Australia during 2017 and 2018. We found that yield of the best winter wheats was comparable with spring wheats sown in their optimal window. Due to the stable flowering time of winter cultivars, adaptation was driven by cultivar flowering time and coincidence with optimal flowering periods in the different environments. The fast winter cultivar Longsword tended to yield best in low yielding (<2.5 t/ha) environments with an early flowering window. The mid-slow winter cultivar DS Bennett yielded most in higher yielding environments (>2.5 t/ha) with a later flowering window, apart from the Mid North of SA where the mid-fast cultivar Illabo was superior. Highest yields of winter wheats were achieved when sown during April and declined when sown in either mid-March or early May.

Improving yield on sodic soil: assessing the value of genetic improvement

Schilling,RK1, Taylor J1 Armstrong R2, Christopher J3, Dang Y3, Rengasamy P1, Sharma DL4, Smith R4, Tavakkoli E5, McDonald GK1

1 The University of Adelaide, School of Agriculture, Food and Wine, Waite Campus PMB 1 Glen Osmond SA 5064, rhiannon.schilling@adelaide.edu.au

  1. 2. Department of Economic Development, Jobs, Transport and Resources, Natimuk Rd, Horsham Vic, 3400

3 School of Agriculture and Food Science, University of Queensland, Tor St Toowoomba, Qld, 4350

4 Department of Primary Industries and Regional Development, Western Australia, Baron Hay Court, South Perth, WA, 6151

NSW Department of Primary Industries, Pine Gully Rd, Wagga Wagga, NSW, 2650

Abstract:

Soils with alkaline sodic (dispersive) subsoils are widespread in the Australian grains belt.  Improving the tolerance of wheat to the range of stresses encountered in these soils has the potential to improve yield and water use efficiency.  Wheat varieties were tested at sites on alkaline soils with varying degrees of sodicity in all mainland States.  The lines were also screened for tolerance to high boron, pH and aluminium.  Genetic correlations among sites from the southern and western regions were high but were markedly different from the Queensland sites.  The benefit of tolerance to multiple stresses was expressed at sodic sites with yields less than about 3 t/ha and tolerance to soil constraints was estimated to improve yields by up to 10% when yields were less than 2 t/ha.

Establishing a value proposition for future traits in a climate-changing world

Greg Rebetzke1, Kathryn Bechaz2, Michelle Murfit3, Gina Bange4, Tina Rathjen1, Fernanda Dreccer5, Warren Muller6, Andrew Fletcher7, Bangyou Zheng5, Enli Wang1, Zhigan Zhao1, Neil Fettell8

1 CSIRO Agriculture and Food, Black Mountain Science and Innovation Park, Canberra ACT, 2601, greg.rebetzke@csiro.au

2 NSWDPI, Yanco Agricultural Institute, Yanco NSW 2703

3 DPIRD, Merredin WA 6415

4 Uni Sydney, PBI Narrabri NSW 2390

5 CSIRO Agriculture and Food, QBP, St Lucia QLD, 4067

6 CSIRO Land and Water, Canberra ACT 2601

7 CSIRO Agriculture and Food, Centre for Environment and Life Sciences, PMB 5, Wembley, WA 6913

8 Central West Farming Systems, 1 Fifield Rd Condobolin, NSW 2877

Abstract:

Increasing climate variability is as great a concern as increasing air temperatures forecast with climate change. The challenge for breeders is in identifying and selecting traits that are genetically correlated with environments into the future and/or difficult to manage away from their breeding nurseries. We report on studies targeting constitutively-expressed traits (e.g. increased rates of spike and grain-filling and increased coleoptile length) to establish their value proposition for increasing grain yield in future environments. The work supports the potential for higher rates of grain-filling and longer coleoptiles as traits where genetics are available now in pre-emptive selection in breeding programs. Further, there is not expected to be any cost associated with these traits in grain yield or quality, or in cooler, wetter seasons.

Interactive effect of elevated CO2 and supplemental N on above- and belowground growth and water use of dryland wheat

Shihab Uddin1 2 3 *, Shahnaj Parvin3 4, Markus Löw2, Sabine Tausz-Posch5, Roger Armstrong6 7, Garry O’Leary6, Glenn Fitzgerald2 6, Michael Tausz5

1NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.

2Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Creswick, VIC, Australia.

3Department of Agronomy, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh.
4School of Ecosystem and Forest Sciences, The University of Melbourne, Creswick, VIC, Australia.

5Department of Agriculture, Science and Environment, School of Health and Applied Sciences, CQUniversity Australia, Rockhampton, QLD, Australia.

6Agriculture Victoria, 110 Natimuk Road, Horsham, VIC 3400, Australia.

7Department of Animal, Plant and Soil Sciences, La Trobe University, Bundoora, VIC, Australia

*Corresponding author: email: shihab.uddin@dpi.nsw.gov.au

Abstract:

Elevated atmospheric CO2 concentration (e[CO2]) stimulates biomass and yield of crops through the ‘CO2 fertilisation effect’. Stimulation of biomass with supplemental nitrogen (N) under e[CO2] may influence water use dynamics, which is particularly important in relatively low yielding dryland Mediterranean regions where timing of water limitations interacts with N availability and intra-seasonal variability is high. This study investigated the interactive effect of N supply (with and without supplemental N) and [CO2] (ambient [CO2] and e[CO2]∼550 µmol mol-1) on aboveground biomass, root length and water use of two wheat cultivars putatively differing in N use efficiency using a Free Air CO2 Enrichment (FACE) facility in Horsham, Victoria. Elevated [CO2] stimulated biomass and grain yield, and this stimulation was influenced by both cultivar and N supplementation. The observed differential response of cultivars to e[CO2] and N rates suggests that there is potential to select germplasm that maximises the benefit from CO2 fertilisation under a wide range of soil N availability.

 

Can N mitigate adverse effects of elevated temperature in wheat?

1.Mariano Cossani1, Daniel Cozzolino2, Victor Sadras1

1South Australian Research and Development Institute

2RMIT University

Abstract:

Delaying sowing reduced yield potential at 0.67 t/ha per °C of mean temperature during critical period for yield determination (20 days pre-10 days post anthesis). Good N nutrition and longer-season spring cultivars reduced the yield gaps in relation to temperature. Responses to N become more erratic when temperatures during the critical period increase above ~14.5 °C. Strategic N management (50-100 kg N/ha) may help to mitigate the effect of higher temperatures on grain number and yield.

Practising precision II: Making precision pay

Hazel McInerney1, Peter McInerney1and Jon Medway2

1 3D-Ag Pty Ltd – 150 Dukes Road, Wagga Wagga, NSW, 2650, www.3D-Ag.com.au   more@3D-Ag.com.au

Terrabyte Services Pty Ltd – 21 Turner Street, Wagga Wagga, NSW, 2650,  jmedway@terrabyte.net.au

Abstract:

PIP (Precision in Practice) is an innovative new approach to identify and treat management zones.  PIP is a two phase process -Phase 1 enables farmers to accurately and cost effectively identify zones within paddocks or management units that are statistically different.  This is addressed in Practising precision I.  This paper addresses PIP Phase 2 – the agronomic and farm system implications of this tool to determine the optimal allocation of resources in the production system, so that both the soil resource and farm profit improve.

Using the zone and landscape information developed by PIP Phase 1, in conjunction with the experience of the land manager and their agronomist, PIP Phase 2 supports the development of a soil sampling plan by zone. Understanding that soil chemistry may not be the only issue, laboratory results are examined in the context the soil and landscape findings and where appropriate ameliorants, seed and fertilizer requirements can be accurately entered into controller maps for variable rate application.

The case study below demonstrates the potential for savings to be made.

Persistence of annual and perennial legumes 12 years after sowing in the Monaro region of New South Wales

Belinda Hackney1, 2, Jo Powells3, Susan Orgill1, 2

1 NSW Department of Primary Industries, PMB, Pine Gully Rd, Wagga Wagga, NSW 2650

2 Graham Centre for Agricultural Innovation, Charles Sturt University and NSW Department of Primary Industries, Pugsley Place, Wagga Wagga, NSW, 2650

3South East Local Land Services, Cooma, NSW 2630

Abstract:

The productivity of introduced and native-based pastures across the Monaro region of NSW is often constrained by a low legume content. Full pasture renovation is frequently precluded by landscape, soil or economic constraints with producers often spreading legume seed with fertiliser in an attempt to increase the legume content of pastures. Four methods of legume introduction into an existing pastures (surface broadcasting and direct drilling with and without a pre-sowing glyphosate knockdown) at two landscape positions (north and south facing aspect) for four legume species, subterranean clover (Trifolium subterraneum), Caucasian clover (T. ambiguum), Talish clover  (T. tumens) and lucerne (Medicago sativa), were investigated. Direct drilling after a glyphosate knockdown was the most successful method of introduction with subterranean clover achieving the highest seedling density. However, after 12 years, few legumes could be found on the north-facing aspect and subterranean clover had not survived on the south facing aspect. Legumes were found only in the direct drilled-glyphosate knockdown treatment; lucerne having the highest plant density and herbage availability. Where legume treatments had failed, populations of tall speargrass (Austrostipa scabra), a native perennial grass, had returned to their original density. A. scabra density was significantly lower on the south facing aspect in the lucerne and Caucasian clover treatments. There is capacity to introduce legumes into existing pastures but seed-soil contact and reduction in competition from existing pasture species at establishment is crucial to long-term persistence

Learning from progressive wheat growers: A case-study for the Wheat Yield Content in Kansas, US

Romulo Lollato1, Dorivar Ruiz Diaz1, Erick DeWolf2, Mary Knapp1, Dallas Peterson1, Allan Fritz1

1 Kansas State University, 2004 Throckmorton Center, Manhattan, Kansas, 66506, lollato@ksu.edu

2 Kansas State University, 4024 Throckmorton Center, Manhattan, Kansas, 66506

Abstract:

There is limited information on agronomic practices affecting wheat (Triticum aestivum L.) yield in intensively managed dryland systems despite the opportunity to narrow the existing yield gap (YG). We used a unique database of 100 intensively-managed field-years entered in the Kansas wheat yield contest during the 2010-2017 harvest seasons to i) quantify the YG, ii) describe wheat management, and iii) identify management opportunities and weather patterns associated with yield. We simulated wheat yield potential (Yw) using SSM-Wheat model for each field-year to estimate YG as the difference between Yw and actual yield (Ya), and used eleven statistical approaches to test the association of management practices and weather variables with Ya. Wheat Ya averaged 5.5 t/ha and simulated Yw averaged 6.4 t/ha, resulting in an YG of 0.9 t/ha (15% of Yw). High-yielding fields had lower maximum (Tmax) and minimum (Tmin) temperatures and greater cumulative solar radiation (RS) and precipitation during grain fill. Varieties susceptible to fungal diseases responded to foliar fungicide (0.8 to 1.4 t/ha) while resistant varieties did not. Seeding rate was negatively associated with Ya, as yield quantile 0.99 was 7.5 t/ha and decreased by 2.7 t/ha for every 100 seeds m-2 increase in seeding rate above 305 seeds m-2. In-furrow phosphorus fertilizer, previous crop, tillage practice, and nitrogen timing, were also associated with Ya. We conclude that fields entered in yield contests have closed the exploitable YG, and there are opportunities to improve Ya through improved management in regions with stagnant wheat yield.

Net water benefit of cover crops in Northern grains production. Farming water with ground cover

Andrew Erbacher1, David Lawrence2, David Freebairn3, Neil Huth4, Brook Anderson4 & Graham Harris2

 1 Department of Agriculture and Fisheries, 22-26 Lagoon St, Goondiwindi, Qld, 4390, andrew.erbacher@daf.qld.gov.au

2 Department of Agriculture and Fisheries, 203 Tor St, Toowoomba, Qld, 4350.

3 DM Freebairn, Wilston, Qld, 4051

4 CSIRO, 203 Tor St, Toowoomba, Qld, 4350.

Abstract:

Low groundcover increases risk of soil erosion and reduces fallow efficiency. To remedy this cover crops can be grown to increase groundcover, but does the increased ground cover improve fallow water accumulation enough to recover the water used to grow the cover crop? To answer this, cover crops were planted into a long fallow following skip row sorghum, and sprayed out prior to growing wheat. The main cover crop was White French millet, which had different termination timings imposed. Other crops included sorghum, lablab and a mixed species (millet, lablab and tillage radish), which were all sprayed out at the same time as the mid-terminated millet. By planting of the subsequent wheat crop, all cover crop treatments, except the lablab, had recovered the water they used prior to termination, with some accumulating more plant available water (PAW) than the control. Increased ground-cover improved establishment of the wheat and all cover crop treatments had higher grain yield than the bare control in a drier than average season. These results confirm a crop can be grown and sprayed out to improve ground cover in a long fallow, without having a net negative effect on PAW, with yield benefits in the following crop in excess of what can be explained by increased soil water.

Paddock scale modelling and mapping of dry matter yield using UAV derived datasets: A case from dairy farming systems in Victoria

Senani Karunaratne, Elizabeth Morse-McNabb, Anna Thomson, Dani Stayches, Joe Jacobs

Agriculture Victoria Research, Ellinbank VIC 3821, website: www.agriculture.vic.gov.au; E: senani.karunaratne@ecodev.vic.gov.au

Abstract:

Traditionally, quantification of dry matter (DM) yield at a paddock or farm scale is undertaken using a rising plate meter (RPM) which provides paddock scale estimates via calibrated equations to predict pasture DM yield. This approach ignores the inherent spatial variability within a paddock which may limit optimum utilisation. In this study, unmanned aerial vehicle (UAV) datasets were coupled with modern machine learning data analytical methods to model and map pasture DM yield variability across individual paddocks at 1 m spatial resolution. The results revealed that the near infrared spectral band had the highest influence in predicting the pasture DM yield. However, the use of additional UAV-derived data sources, such as digital surface and digital terrain models as proxies for pasture height, further improved the prediction. Height derived from the UAV datasets was identified as the second most important variable in prediction of the pasture DM yield. Derived models were cross-validated and also independently validated through data splitting which resulted in concordance values of 0.90 and 0.40 respectively.  Model comparison with the calibration equation derived using a RPM revealed that both methods reported equal validation of the results based on the cross-validation. However, the RPM model surpassed the independent validation results of the UAV-machine learning modelling approach. There is potential to explore a wider spectral range and other ancillary datasets for model improvement, in order to improve these machine learning models for prediction of DM yield across the paddock scale.

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Host

The Australian Society of Agronomy is the professional body for agronomists in Australia. It has approximately 500 active members drawn from government, universities, research organisations and the private sector.

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David Marland Photography david_marland@hotmail.com Graham Centre for Agricultural Innovation, Charles Sturt University

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