Interannual climate variability impacts sugarcane yields. Local climate data such as daily rainfall, temperature and radiation were used to describe yields collected from three locations-Victoria sugar mill (1951-1999), Bundaberg averaged across all mills (1951-2010) and Condong sugar mill (1965-2013). Three regression methods, which have their own inbuilt variable selection process were investigated. These methods were (i) stepwise regression, (ii) regression trees and (iii) random forests. Although there was evidence of overlap, the variables that were considered most important for explaining yields by the stepwise regressions were not always consistent with the variables considered most important by the regression trees. The stepwise regression models for Bundaberg and Condong delivered a model that was difficult to explain biophysically, whereas the regression trees offered a much more intuitive and simpler model that explained similar levels of variation in yields to the stepwise regression method. The random forest approach, which extends on the regression tree algorithm generated a variable importance list which overcomes model sensitivities caused by sampling variability, thereby making it easier to identify important variables that explain yield. The variable importance list for Victoria indicated that maximum temperature (February-April), radiation (January-March) and rainfall (July-October) were important predictors for explaining yields. For Bundaberg, emphasis clearly centred on rainfall, particularly for the period January to April. Interestingly, the random forest model did not rate rainfall highly as a predictor for Condong. Here the model favoured radiation (February to April), minimum temperature (March-April) and maximum temperature (January to April). Improved understanding of influential climate variables will help improve regional yield forecasts and decisions that rely on accurate and timely yield forecasts.

There is currently no operational method of managing irrigation in Australia's sugar industry on the basis of systematic, direct monitoring of sugar plant physiology. Satellite remote sensing systems, having come a long way in the past 10 years now offer the potential to apply the current ground-based 'FAO' or 'crop coefficient (K_c)' approach in a way that offers a synoptic view of crop water status across fields. In particular, multi-constellation satellite remote sensing, utilising a combination of freely available Landsat and Sentinel 2 imagery, supplemented by paid-for imagery from other existing satellite systems is capable of providing the necessary spatial resolution and spectral bands and revisit frequency. The significant correlations observed between K_c and spectral vegetation indices (VIs), such as the widely used normalised difference vegetation index (NDVI) in numerous other crops bodes well for the detection and quantification of the spatial difference in evapotranspiration (ET_c) in sugar which is necessary for irrigation scheduling algorithms. Whilst the NDVI may not serve as the appropriate index for sugarcane, given the potential of the NDVI to saturate at the high leaf area index observed in fully developed cane canopies, other VIs such as the Green-NDVI (GNDVI) may provide the response required. In practise, with knowledge of an appropriate K_c-VI relationship, K_c obtained from time-series (weekly) remotely sensed data, integrated with local agrometeorological data to provide ET_o, would provide estimates of ET_c from which site-specific irrigated water requirements (IWR) could be estimated. The use of UAVs equipped with multispectral sensors, even active optical sensors (AOS), to 'fill the gaps' in optical data acquisition due to cloud cover is conceivable. Cross calibration of any passive imaging system, as with the multi-constellation satellite data is essential. The use of radar images (microwave remote sensing) (for example, Sentinel 1&2 C-SAR, 5m) offers all weather, day-and-night capabilities although further work is necessary to understand the link between the radar back scatter, which is responding to surface texture, and evapotranspiration (and K_c). Further R&D in ascertaining the K_c-VI relationships during crop growth is necessary, as is the testing of multi-sensor cross-calibration and the relationship between radar remote sensing and K_c. Existing irrigation advisory delivery systems in Australia such as IrriSAT should be investigated for their applicability to the sugar industry. The estimated season cost to a user for a sugarcane irrigation advisory service in Australia, based on the use of data from existing optical satellite imaging systems and utilising the K_c approach, is likely to be of the order of US$2-3/ha.

Chapter 9 builds on the themes of previous chapters, which focus on the deployment of technology in sugarcane cultivation, by addressing the issue of precision agriculture in sugarcane production through a detailed case study from the Burdekin region of Australia. Precision agriculture involves the use of spatial information about crop performance and the biophysical characteristics of the production system at the field and sub-field scales, in order to optimize agronomic management decisions. The chapter uses a 26.7 ha field in the Burdekin sugarcane growing region of Australia to illustrate how precision agriculture technologies might be used to enhance sugarcane production. In this case, precision agriculture achieved a saving of A$330/ha in gypsum application costs through the use of variable rate application.

Ensuring the sustainability of crop production, whilst simultaneously taking actions to mitigate the environmental impacts of agriculture, is a current global priority. Given around 75% of global food crop yields benefit from pollination services provided by diverse wild and managed insect taxa, management strategies that support diverse communities of pollinator taxa are valuable to ensure ongoing pollination service provisioning and agricultural production. In addition to pollination, realised crop yields are also influenced by other biotic and abiotic factors which vary across different spatial and temporal scales. This thesis addresses three important aspects of crop pollination, namely the need to merge disparate research fields, the degree to which pollinator taxa service multiple crops and regions and how pollination interacts with crop tree physiological factors such as tree vigour.

First, I reviewed the literature to evaluate the knowledge gaps concerning pollinator effectiveness and the utility of using remote sensing in crop pollination research. I conducted surveys and pollen deposition trials to identify pollinators in avocado, mango and macadamia crops in three geographically distinct growing regions in Australia across three years. Using single visit deposition rates, bipartite networks and spatial analyses I also investigated pollinator service provisioning and the land use types that influence pollinator communities in these crop and regions. Using hand pollination trials over two years I investigated the impact of supplemental cross pollination on the yield of avocado trees.

My first review identified important research directions to account for pollination processes occurring at a community level including: plant-pollinator interactions, heterospecific pollen transfer and variation in pollination outcomes. My second review identified the areas in which remote sensing technologies can facilitate our understanding of interactions between pollinators, pollination services, environmental and plant physiological factors which affect final harvest measures.

Using multi-crop, multi-year and multi-region crop-pollinator networks I demonstrated that shared wild pollinator taxa visit multiple crops across several regions. In particular, honey bees (A. mellifera) and two families of wild visitors, Syrphidae and Calliphoridae, are present across all regions and crops. Further, regional comparisons for both avocado and mango crops identified additional shared families that were locally abundant such as Coccinellidae and native Apidae.

I found that the effect of additional cross pollination on trees of different vigour varied between individual orchard blocks and across years. General patterns relating to the impact of interaction between tree vigour and pollination on yield were discernible in this study, with lower and medium vigour trees responding more positively to supplemental pollination than high vigour trees. High variability in results and differences in effect response across orchard blocks highlight the need to investigate further factors at a tree and block scale, in future analyses.

My research indicates that there is significant potential to identify shared pollinators that provide services across multiple crops. Pollination management strategies that are regionally specific and that include bee and non-bee taxa and co-flowering crop species are needed to ensure ongoing effective and resilient pollination services are delivered to crop systems. The merging of different research fields, such as remote sensing, pollinator ecology and precision agriculture offers exciting new approaches to facilitate our understanding of these complex crop-pollinator interactions.