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Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage. • Factors driving seasonal irrigation usage were explored in an irrigation district. • A Bayesian hierarchical model (BHM) was developed for 1099 crop water use records. • Seasonal irrigation usage was mainly influenced by soil and irrigation practices. • The BHM showed good accuracy in estimating seasonal irrigation usage (R2 = 0.62). [ABSTRACT FROM AUTHOR]

• The effect of training samples on within-season crop classification was examined. • The sample size should be sufficiently large in within-season crop classification. • Proportions of each crop type should be similar for training and application. • Training using samples from previous years led to a minor decline in accuracy. • Overall classification accuracy exceeded 86 % for the third and later months. Within-season crop classification using multispectral imagery is an effective way to generate timely crop maps that can support water and crop management; however, developing such models is challenging due to limited satellite imagery and ground truth data available during the season. This study investigated ways to optimize the use of multi-year samples in a within-season crop classification model, aiming to enable accurate within-season crop mapping across years. Our study focused on classifying field-scale corn/maize, cotton, and rice in south-eastern Australia from 2013 to 2019. The crop classification model was based on the random forest and support vector machine algorithms applied to Landsat 8 multispectral bands. We designed four experiments to understand the influences of training sample selection on model accuracy. Specifically, we analyzed how the within-season classification accuracies are affected by 1) training sample size; 2) proportions of classification classes; 3) the inclusion of a non-crop class (e.g., fallow land) in the training sample, and 4) training samples collected from different years. We found that 1) the training sample size should be sufficiently large to ensure within-season classification accuracy; 2) using training samples for each crop type in proportion to their occurrence within the landscape results in more accurate multi-year classification; 3) the inclusion of the non-crop class can reduce the accuracy with which crop types are distinguished, so the proportion of the non-crop class should be maintained at a relatively low level, and 4) predicting the current year with training samples from previous years can lead to a minor decline in accuracy compared to using samples only from the current year. These training sample settings were adopted to develop a final model. We found that the model accuracy continues to improve as more input imagery is added as the cropping season progresses, with a rapid rate of initial improvement which then slows. December, the third month of the summer growing season, is the earliest time that reliable maps were generated, with an overall accuracy of 86 % and user's accuracies for all crops exceeding 80 %. Our proposed experiments are robust and transferable to other regions and seasons to assist the development of within-season crop maps, and can thus be valuable tools to support agricultural management. [ABSTRACT FROM AUTHOR]

The commencement of SEAS5 model for operational seasonal climate forecasting by the European Centre for Medium-Range Weather Forecasts (ECMWF) is a new development. It replaces the ECMWF System 4, which had a large international community of seasonal climate forecast users. To assist potential users of SEAS5 forecasts, a systematic and detailed evaluation of forecast skill and reliability of climate variables over land areas is valuable. In this regional study, we evaluate SEAS5 performance in forecasting precipitation and daily minimum temperature (Tmin) and daily maximum temperature (Tmax) for the Australian continent, based on 36 years of re-forecast data. We evaluate forecasts after simple mean-corrections, and statistically calibrated forecasts using the Bayesian joint probability (BJP) modelling approach. We also provide a comparison with System 4. A new simpler and more efficient BJP algorithm is introduced to facilitate this study and support wider use of the algorithm in other applications. • Seasonal climate forecasts of the new ECMWF SEAS5 model are evaluated for Australia. • SEAS5 offers considerable skill for forecasting the coming month. • The skill drops significantly beyond one month forecast horizon. • A simpler and more efficient Bayesian joint probability (BJP) algorithm is introduced. • Forecasts are more skillful and reliable after calibration using the BJP algorithm. [ABSTRACT FROM AUTHOR]