In-season yield prediction using VARIwise
Keynote
McCarthy, Alison, O'Keeffe, Kieran and McKay, Andrew. 2019. "In-season yield prediction using VARIwise." Australian Association of Cotton Scientists 2019 Australian Cotton Research Conference (AACS 2019). Armidale, Australia 28 - 30 Oct 2019
| Paper/Presentation Title | In-season yield prediction using VARIwise |
|---|---|
| Presentation Type | Keynote |
| Authors | McCarthy, Alison (Author), O'Keeffe, Kieran (Author) and McKay, Andrew (Author) |
| Journal or Proceedings Title | AACS 2019 Australian Cotton Research Conference: Taking cotton research to new heights: Conference program and abstract book |
| Number of Pages | 1 |
| Year | 2019 |
| Web Address (URL) of Paper | http://www.australiancottonscientists.org/wp-content/uploads/AACS-2019CottonResearchConference_Proceedings.pdf |
| Conference/Event | Australian Association of Cotton Scientists 2019 Australian Cotton Research Conference (AACS 2019) |
| Event Details | Australian Association of Cotton Scientists 2019 Australian Cotton Research Conference (AACS 2019) Event Date 28 to end of 30 Oct 2019 Event Location Armidale, Australia |
| Abstract | In-season yield prediction supports improved agronomic management and planning for crop sales and insurance contracts. Yield is currently often estimated using rules of thumb and manual boll counts. Data analytics approaches have been developed using site- and season specific multispectral satellite imagery-based correlations that require significant datasets for wider scale transferability. An alternative approach is to forecast yield using known soil-plant-atmosphere interactions in crop production models and calibrated using available field data. USQ has developed software ‘VARIwise’ to provide yield prediction throughout the season combining these models with: In the 2017/18 and 2018/19 seasons, VARIwise was evaluated at one cotton site in Goondiwindi and 16 sites in Griffith. Management zones in the field monitored using the UAV were identified from vegetation index surveys, yield maps or satellite images. Phantom 4 UAV imagery was collected monthly at each site between January and picking for calibrating the crop model. The sites had varying levels of fruit removal, hail damage and heat stress. In the 2017/18 Griffith trial, the percentage yield prediction errors were 10.2% in January, 6.0% in February, 2.5% in March, and 0.5% at picking, and in the 2018/19 Griffith trial the errors were 18.8% in January, 4.9% in February, 9.5% in March, and 10.1% at picking. In the 2018/19 Goondiwindi trial, the yield prediction percentage errors were 8.7% in February, 5.9% in March, 7.1% in April and 2.6% in May. The prediction errors at Griffith were higher in the 2018/19 season than the 2017/18 season because the sites experienced hail and heat stress that are not currently represented within the VARIwise crop model. The yield predictor will be evaluated in 2019/20 to improve performance under insect and hail damage. |
| Keywords | agronomic management and planning; crop sales; in-season yield; insurance contracts; VARIwise |
| ANZSRC Field of Research 2020 | 300206. Agricultural spatial analysis and modelling |
| 400799. Control engineering, mechatronics and robotics not elsewhere classified | |
| 300403. Agronomy | |
| Byline Affiliations | Centre for Agricultural Engineering |
| Cotton Research and Development Corporation, Australia | |
| Institution of Origin | University of Southern Queensland |
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