Dr Javier A. Fernandez1, M.Sc. Carla Gho Brito1, Dr. Daniel Smith1, Bangyou Zheng2, Prof Scott C. Chapman1
1The University of Queensland, St Lucia, Australia, 2CSIRO Agriculture and Food, St Lucia, Australia
Biography:
Javier A. Fernandez is a Postdoctoral Research Fellow at The University of Queensland, Australia. His research specializes in crop physiology, plant-environment interactions, and crop modelling reflected by over 30 publications. He is currently engaged in the use of statistical, digital, and model technologies to assess crop growth and development, with the goal of enhancing production, resource use efficiency, and sustainability of agricultural systems in Australia. Javier received his BS from the National University of the Northeast in Argentina, and his PhD from Kansas State University. He is a recipient of several honours and awards from university, professional societies, and governmental organizations.
Abstract:
Digital technologies in agriculture have increasingly emerged as effective solutions for optimized crop management. However, the timely assessment of crop water stress remains a significant challenge despite advances in remote sensing utilization in the past decades. This study investigated the potential and challenges of using remote sensing and real-time infrared sensing systems in the estimation of crop evapotranspiration (ET) across a large set of Australian environments. Using National Variety Trials (NVT), canopy temperature thermometers and RGB cameras were deployed across wheat and sorghum field trials for characterizing crop surface temperature fluctuations. For wheat, 71 NVT main season trials were monitored across QLD, NSW, SA, VIC, and WA during 2020, 2021, and 2022 growing seasons. For sorghum, 3 NVT trials were monitored across QLD and NSW during 2022. ET was estimated at each location using the surface energy budget approach and converted into valuable data insights for NVT. The sensitivity of this approach was evaluated and compared against APSIM simulated-based ET. We discussed challenges and opportunities in the development of a scalable approach for rapid processing of large and unstructured data in field trials. Using real-world farming conditions, these findings can contribute to validate the efficiency and scalability of these technologies for improving profitability and sustainability of cropping systems.