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Horsham, Australia

Pandey B.R.,University of Melbourne | Burton W.A.,Grains Innovation Park | Nicolas M.E.,University of Melbourne | Salisbury P.A.,University of Melbourne
Euphytica | Year: 2016

To determine the levels of heterosis in F1 hybrids of juncea canola under well-watered and water deficit conditions, glasshouse experiments were conducted at the University of Melbourne, Parkville campus between 2012 and 2014. Three juncea canola hybrids, their parents and an open-pollinated juncea canola control cultivar (OasisCL) were grown. Plants were subjected to two treatments—well-watered and water deficit after first open flower to maturity. Measurements were recorded on days to various phenological stages, biomass production, seed yield and yield components. Yield heterosis was determined as superiority of F1 hybrids over mid parent and better parent. Two juncea canola hybrids—HJM1Z-2013 and HJM1Z-0027 out-yielded OasisCL and showed significant mid parent and better parent heterosis for seed yield under water deficit. Number of pods per plant was the major yield component affected by water deficit whereas seeds per pod, thousand seed weight and harvest index were stable. Water deficit had significant negative effects on biomass production at harvest. The study revealed the possibility of exploiting yield heterosis of juncea canola under terminal drought conditions. However, further experiments are recommended to verify the results as the results were from pot experiments in controlled conditions. © 2016 Springer Science+Business Media Dordrecht

Makowski D.,French National Institute for Agricultural Research | Asseng S.,University of Florida | Ewert F.,University of Bonn | Bassu S.,French National Institute for Agricultural Research | And 93 more authors.
Agricultural and Forest Meteorology | Year: 2015

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. © 2015 Elsevier B.V.

Maiorano A.,Montpellier SupAgro | Martre P.,Montpellier SupAgro | Asseng S.,University of Florida | Ewert F.,University of Bonn | And 39 more authors.
Field Crops Research | Year: 2016

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24. °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. © 2016 Elsevier B.V.

Devadas R.,University of Vic | Jones S.D.,University of Vic | Fitzgerald G.J.,Grains Innovation Park | Mccauley I.,Future Farming Systems Research | And 5 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

Water and Nitrogen (N) are critical inputs for crop production. Remote sensing data collected from multiple scales, including ground-based, aerial, and satellite, can be used for the formulation of an efficient and cost effective algorithm for the detection of N and water stress. Formulation and validation of such techniques require continuous acquisition of ground based spectral data over the canopy enabling field measurements to coincide exactly with aerial and satellite observations. In this context, a wireless sensor in situ network was developed and this paper describes the results of the first phase of the experiment along with the details of sensor development and instrumentation set up. The sensor network was established based on different spatial sampling strategies and each sensor collected spectral data in seven narrow wavebands (470, 550, 670, 700, 720, 750, 790 nm) critical for monitoring crop growth. Spectral measurements recorded at required intervals (up to 30 seconds) were relayed through a multi-hop wireless network to a base computer at the field site. These data were then accessed by the remote sensing centre computing system through broad band internet. Comparison of the data from the WSN and an industry standard ground based hyperspectral radiometer indicated that there were no significant differences in the spectral measurements for all the wavebands except for 790nm. Combining sensor and wireless technologies provides a robust means of aerial and satellite data calibration and an enhanced understanding of issues of variations in the scale for the effective water and nutrient management in wheat.

Rodda M.S.,Grains Innovation Park | Kant P.,Grains Innovation Park | Lindbeck K.D.,Australian Department of Primary Industries and Fisheries | Gnanasambandam A.,Grains Innovation Park | And 2 more authors.
Australasian Plant Pathology | Year: 2015

A high-throughput and reliable method to screen field pea germplasm for bacterial blight resistance was developed. The method uses spray inoculation of seedlings in small pots with a bacterial suspension followed by incubation in a glasshouse. This is less laborious compared to previous stab-inoculation methods, and takes less than 1 month (c.a. 25–27 days) from seed sowing to disease assessment. It uses a surfactant to achieve good coverage of bacterial suspension and required leaf wetness for progression of the disease. Disease symptoms in the glasshouse were similar to those observed in field, and data were highly correlated with those from field experiments. The method was validated using individual and combination of isolates of Pseudomonas syringae pathovars pisi and/or syringae to test their ability to effectively differentiate between resistant and susceptible pea genotypes. Screening of diverse field pea landraces and wild relatives using present screening method has identified potentially new sources of resistance to P. syringae pvs pisi and syringae. The developed method is being used to evaluate bacterial blight resistance within the Australian field pea breeding program. © 2015, Australasian Plant Pathology Society Inc.

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