Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R² = 0.86, RMSE = 0.18 t ha⁻¹). RF models for individual paddocks in VIC (R² = 0.89, RMSE = 0.15 t ha⁻¹) and NSW (R² = 0.87, RMSE = 0.07 t ha⁻¹) performed well, but moderate performance was seen for SA (R² = 0.45, RMSE = 0.25 t ha⁻¹). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.

Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics,i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed anin silicoapproach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yieldsin situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.

There has been increasing recognition for the need to reform doctoral training practices to foster students’ personal epistemology. This study describes the design and evaluation of a learning experience designed to help students understand the scholarly publication process. Firstly, this study discusses the design of the learning experience, describing the collaborative process of writing an interdisciplinary publication using both online and face-to-face learning. Secondly, this study evaluates the effectiveness of the learning experience by examining students’ reflections. We show that participation in the learning experience helped students to develop their academic writing proficiency, collaboration and teamwork, intercultural competence, and ability to engage in reflective practice. Importantly, we show that each student also created more individualised knowledge, gaining insight into how they and others think. This study, therefore, demonstrates that personal epistemology can be fostered through collaboration in a doctoral writing group context.