Climate change has been unequivocally known to be real, and its impacts are recognized as one of the most pressing global issues in the current decade. Evidences from a variety of different studies show that the impacts of climate change have become a major threat to agriculture and the food security in both the developed and developing countries across the world. With unabated increase in the anthropogenic atmospheric greenhouse gas emission that is known to interfere with `the regional and global circulation systems, global warming, and climate change in the twenty first century and beyond, would continue to be the most important agenda of debate and scientific discourses. Moreover, the negative impacts of climate change have been observed to be more pronounced in the high latitude areas, such as the mountainous countries in the Himalayas and elsewhere. Studies have also strongly postulated that the adverse impacts of climate change are most likely to be more severe in the developing countries in south Asia, Southeast Asia, Sub-Saharan Africa, and Latin America, due to their low adoption of farming technologies and lack of capacity to respond.

As many developing countries in the region report the negative impacts of climate change, Bhutan—an agrarian country located in the Eastern Himalayas, is no exception. The country has experienced increasing incidences of risks and disasters associated with climatic variabilities in the recent years. There have been pervasive issues of changing weather patterns that make farming a highly risky and vulnerable occupation. Apart from this, there are other pre-disposing factors that make Bhutan an even more sensitive and vulnerable to the climate variabilities. The highly rugged mountainous topography is one of the most important factors that make farming very challenging, as farmlands are concentrated in the river valleys with scattered land parcels in the mountain slopes. Thus the farms are highly prone to natural disasters that are triggered by weather and climatic events. The dramatic rise in elevation even over a small distance is another factor, which influence the orographic effects of mass air-flow systems, contributing to sudden changes in weather, thus negatively impacting crops and farms. Moreover, the Himalayan Mountains are considered to be geologically fragile, with inherently infertile soils for farming. This further makes farming prone to the destructive natural events, including those of weather phenomena. All of these, contribute to the challenging and subsistence nature of farming in the Bhutanese agricultural system. Therefore, this research was undertaken to study climate change and its impacts on various aspects of agriculture across different agro-ecological zones in Bhutan. The study used a combination of farmers’ perception analysis, geo-spatial techniques and modelling approaches to fill the much-needed knowledge gap on the issues of climate change faced by the Bhutanese farmers, and provides insights into the past, present and future climate change impact scenarios. Much of the climate and impact reports on Bhutan were based on hearsay, and scientific studies are few and far between.

As part of this research, a thorough review of agricultural production management system and pertinent issues of climate change impacts in Bhutan was undertaken. It was found that agriculture in Bhutan and the study region have been largely affected by the rising temperature, droughts and precipitation changes, which in turn have led to many other issues, ranging from water availability, crop and infrastructure damage to land degradation. Moving further, assessment of land cover changes in Punatsang Chhu Basin of Bhutan indicated large scale changes, especially in the high elevation areas. The findings from this study show that there is an increased rate of glacier retreat, with repercussions of Glacial Lake Outburst Floods (GLOF) and erosive activities of rivers and streams that directly and indirectly affect farming downstream in the river valleys. This indicates that mountain agriculture is highly vulnerable to the impacts of climate change and is at high risks of food insecurity. Likewise, the results from the analysis of farmers’ perceptions across the various agro-ecological zones in Bhutan indicated that farming has become more and more challenging. The extreme weather events, such as untimely rains, droughts and windstorms have become frequent occurrences, thus inflicting between 1-19% crop damages. The monsoon rains were assessed to be highly unpredictable, and untimely, which were perceived to have impacted the decisions of farmers, due to drying up of water sources, crop losses, land fallowing and cropping pattern changes. Farmers have also perceived issues of the emergence of diseases and pests, which together with aggressive incursion of invasive species (Parthenium hysterophorus, for example) would herald uncertainties and rising pressure on Bhutan’s limited arable land. Further, based on the projections of the Intergovernmental Panel on Climate Change (IPCC), MaxEnt modelling of rice distribution indicated large changes in crop suitability shift. Such a crop suitability change, especially from high to low suitability in the major rice growing areas, indicates decline in crop area and yield under the impact of climate change in the near future. Therefore, for improving the resilience and sustainability of the Bhutanese farms, a comprehensive climate change adaptation plan, backed up by in-depth research and policy instrument must be put in place.

The impacts of climate change on tea production systems may be very variable, at both the national and global levels. In particular, Sri Lanka is considered vulnerable to climate fluctuations due to a variety of geographic, socioeconomic, and political factors. The predicted effects of climate change could have serious and irreversible consequences for tea production, quality, and habitats. Therefore, the consequences of climate change on Sri Lanka's tea industry should be extensively researched to determine its impact on production and quality, which in turn related to export revenues and employment for rural populations. However, information is exiguous on how climate change could affect climate/land suitability and tea quality under rainfed conditions in Sri Lanka. To narrow this gap, this study aimed at evaluating the effects of climate change on climate/land suitability for tea and its quality using a case study of Sri Lanka, a well-known high-quality black tea producer, as a classic example of a susceptible region. The study used species distribution techniques, geographic information system (GIS), remote sensing (RS)–based applications, and chemical analysis of tea leaves. The systematic review suggested that the impacts of the current and future climate on tea production systems outweigh the beneficial impacts, having multidimensional and multifaceted consequences. Tea yield increases when CO₂ levels rise, but this positive effect could be hindered by rising temperatures. Further, tea yield would be negatively impacted by drought, uneven rainfall, and extreme weather events. For tea quality attributes, climate change can serve as both a boon and a bane, leaving questions and giving research priority to quantifying the thresholds of biochemicals to define tea quality, according to customer satisfaction. Climate change affects tea habitats by causing losses, gains, and shifts in climate suitability. Further review suggested the scarcity of appropriate method to model impacts of future climate changes on tea quality and for determining climate suitability for tea. It also highlighted the importance of implementation of adaptive and mitigation measures in tea production to alleviate the undesirable impacts of climate change. At regional scale climate modelling for Sri Lanka's tea sector, indicated that precipitation seasonality, annual mean temperature and annual precipitation are the three most important bioclimatic variables of tea habitat distribution in Sri Lanka. Land suitability classes for tea cultivation comprised of low suitability (42.1%), unsuitable (28.5%), moderate (12.4%), highly suitable (13.9%), and very highly suitable (2.5%). There is a chance of decrease in optimal and medium suitability areas in low-elevation regions in the future, with overall decline assessed to be between 8-17% for all suitability areas. This indicating that climate change will have a negative effect on the habitat suitability of tea in Sri Lanka by 2050 and 2070. Further, the refinement in land suitability classification through inclusion of other climatic and environmental variables (solar radiation, temperature, rainfall, topographic and soil) in climate model made two suggestions namely (1) there is a noticeable difference between tea- and non-tea-growing areas in terms of all above factors" (2) under future climate change scenario, tea-growing regions in Sri Lanka could expand to a range of locales, if some key variables are carefully managed.

For tea quality assessment, model showed a significant interaction effect of weather conditions, cultivar, and geographical location over the concentrations of major tea quality biochemicals (total polyphenol content (TPC), free sugar, protein, and theanine) in tea leaves. The bioclimatic variables present seasonality (monthly range in temperature and precipitation), monthly trends (mean monthly temperature, monthly total precipitation), and extreme environmental variables (temperature of the coldest and warmest month, and precipitation of the wettest and driest months). They particularly caused changes in the four tested biochemicals of tea. The thresholds of all tested biochemicals are likely to increase with future climate change as temperatures and rainfall intensities are likely to increase. The distribution class with "very high" concentrations of TPC and theanine is expected to increase by 10% and 14%, respectively, in the future, while protein and free sugar classes are expected to decrease by 14% and 12%, respectively. For tea quality assessment, model showed a significant interaction effect of weather conditions, cultivar, and geographical location over the concentrations of major tea quality biochemicals (total polyphenol content (TPC), free sugar, protein, and theanine) in tea leaves. The bioclimatic variables present seasonality (monthly range in temperature and precipitation), monthly trends (mean monthly temperature, monthly total precipitation), and extreme environmental variables (temperature of the coldest and warmest month, and precipitation of the wettest and driest months). They particularly caused changes in the four tested biochemicals of tea. The thresholds of all tested biochemicals are likely to increase with future climate change as temperatures and rainfall intensities are likely to increase. The distribution class with "very high" concentrations of TPC and theanine is expected to increase by 10% and 14%, respectively, in the future, while protein and free sugar classes are expected to decrease by 14% and 12%, respectively.

• Using advanced predictive modelling approaches, we have successfully used both satellite and drone-based multispectral imagery to predict canola maturity parameters to a high degree of accuracy (seed colour change, root mean squared error – RMSE of <10%).

• Simple normalised difference vegetation index (NDVI) based regression modelling was unable to account for location- and variety-induced variation resulting in significantly higher prediction errors than when using more advanced predictive modelling approaches.

• Significant potential exists for using this technology in a canola windrow-timingdecision support tool that would overcome the many challenges of current industry practice. However, additional investigation is required to validate the performance of this technology application across multiple seasons and further progress modelling approaches.

The offshore islands of New South Wales host millions of migratory seabirds that gather in dense colonies on islands to breed. Seabirds have the capacity to drive ecosystem function through dual roles of marine-derived nutrient subsidies via guano deposition and bioengineering through burrow-nesting. Broughton Island is managed as part of the Myall Lakes National Park estate and has experienced a range of environmental disturbances in the past decade including the introduction of invasive plants and mammals, which led to significant changes to seabird populations and native vegetation communities. In response to the threats imposed by grazing rabbits and predatory rats on seabird habitat and breeding success, these invasive animals were successfully eradicated from the island in 2009 with the goal of restoring seabird populations and plant communities. The trajectory of ecological change, however, remained largely unknown. The aims of the research presented in this thesis were to first gain scope on the effect of seabird nutrient subsidies and nesting activities on island soils and plants in colonies of the most abundant seabird species on the island, Ardenna pacifica (wedge-tailed shearwaters).

The results revealed novel evidence of seabird colony soils more depleted in soil C, N and P compared to both adjacent and sloping areas of hydrological accumulation. It was also found that vegetation was distinctly different within seabird colonies and was defined by the presence of an invasive cactus, Opuntia stricta. This result will be the first to describe in detail how burrowing seabirds on islands with deep and sandy soils in a subtropical climate, affect their environment, thereby giving new insights onto the mechanisms driving ecosystem function and the management implications for such islands.

Another key research aim was to elucidate the effectiveness of eradication of rats and rabbits was effective in restoring native vegetation cover and richness on Broughton Island by analysing data collected from 7 years of vegetation surveys. Overall positive effects were seen in vegetation height, species richness, and ground cover, but it may take successional plant communities longer time to recover and require additional interventions for optimal outcomes. It was concluded that positive outcomes of vegetation recovery may be confounded by areas with disturbance by burrowing seabirds, and was supported by the evidence supplied by the research comparing vegetation and soil characteristics inside and outside of seabird colonies.

Two experimental habitat suitability models were created taking different but complementary approaches to predict preferred and projected colony habitat on Broughton Island. Both models had high accuracy at detecting suitable habitat on the island, and both models identified unoccupied areas of high habitat suitability which were used in conjunction with other results to make robust conclusions.

Identifying the fundamental effects of seabirds on soils and plants in nesting areas provided evidence to predict how expanding seabird colonies may change the soil and vegetation environment on this distinctive island ecosystem. The spatial results, combined with the knowledge of biophysical effects on soils and vegetation from seabird colonies, identified precise areas which are predicted to experience change in vegetation and guano subsidies if seabird colonies should expand to these highly suitable areas. Since expansion of seabird colonies into suitable habitat is likely now Broughton Island is predator-free, the opportunity for effective biocontrol of weeds, and protection of habitat now exists.

This work demonstrates how multifaceted approach using field surveys, laboratory and geospatial analyses strengthen ecological conclusions and can be applied to effective and real world conservation plans on islands experiencing ecological changes. The results will be utilised by the New South Wales National Parks and Wildlife Service to inform future island management.

The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architecture impacted performance, with reasonable estimates achieved on hedge, single leader and conventional systems (3.4, 5.0, and 8.2 average percentage error, respectively) while fruit load of trellised orchards was over-estimated (at 25.2 average percentage error). Yield estimations were made for multiple orchards via: (i) human count of fruit load on ~5% of trees (FARM), (ii) human count of 18 trees randomly selected within three NDVI stratifications (CAL), (iii) multi-view counts (MV-Raw) and (iv) multi-view corrected for occluded fruit using manual counts of CAL trees (MV-CAL). Across the nine orchards for which results for all methods were available, the FARM, CAL, MV-Raw and MV-CAL methods achieved an average percentage error on packhouse counts of 26, 13, 11 and 17%, with SD of 11, 8, 11 and 9%, respectively, in the 2019–2020 season. The absolute percentage error of the MV-Raw estimates was 10% or less in 15 of the 20 orchards assessed. Greater error in load estimation occurred in the 2020–2021 season due to the time-spread of flowering. Use cases for the tree level data on fruit load was explored in context of fruit load density maps to inform early harvesting and to interpret crop damage, and tree frequency distributions based on fruit load per tree.

Globally, the production and trade of fruits and nuts from horticultural tree crops (HTCs) is increasing due to greater demand from a growing world population. Among those HTCs, is the mango, venerated as the “king of fruits” due to its nutritional, health and the economic benefits it provides to both developed and developing nations. Its production and trade have consistently risen since the early 1960s, when official reporting begun. Global production increased from 10.9 million tons in 1961 to over 57 million tons in 2021, representing a 422% increase. According to the Food and Agricultural Organization of the United Nation (FAO), mango contributed USD 0.6 million to approximately USD 3.7 billion from 1961 to 2021 in export value to the global economy.

With increasing food demand, there is the need for increased production, optimizing efficiencies and minimizing environmental impact through the more judicious use of crop inputs. Part of this solution is the development of technologies and analytics that can more accurately and efficiently measure the spatial and temporal variability in tree health, timings of key phenological stages that dictate key management practices and production (yield and quality). The outcomes of these applications also assist with improved decision making around harvest planning and logistics, minimizing potential food wastage along the value chain, market access and forward selling. The current commercial practice for measuring these key parameters in tree crops, including mango, is predominantly by in-field within season assessment which is costly, time and labour intensive, can be inaccurate due to a nonrepresentative area of the orchard being evaluated and human subjectivity.

A review of prior literature identified some recent technological advancements such as the use of weather parameters ‘Growing Degree Days’ for determining tree growth phase and fruit maturation; proximal sensing/ machine vision and the targeted manual fruit counts of individual trees for calibrating remotely sensed imagery. However, these methods are costly, time and labour intensive. Additionally, the approaches can lack spatial granularity, scalability, commercial readiness and provide accuracies fairly similar to current commercial practices. From the publications reviewed across many horticulture and agricultural crops, remote sensing (RS) and associated cutting edge analytics (e.g. Machine learning (ML)) presents as the most likely technology to improve current management and forecasting practices in mango. However, a large knowledge gap still remains.

This study sought to address this knowledge gap by undertaking, four key objectives:

1. Assess the potential of Sentinel-2 satellite data derived vegetation indices (VIs) in distinguishing phenological stages of mango (Chapter 2)

2. Assess the potential of Sentinel-1 satellite data in distinguishing mango phenology and investigating its relationship with weather variables (Chapter 3)

3. Explore the relationship between very high-resolution satellite imagery data and fruit count for predicting mango yield at multiple scales (Chapter 4), and finally

4. Integrate time series remote sensing and weather variables for mango yield prediction using a machine learning approach (Chapter 5).

The study was conducted in commercial mango orchards in both Ghana and Australia covering the period 2015 to 2022 using RS data obtained from platforms such as Sentinel-1 (S1), Sentinel-2 (S2), Landsat-8 and WorldView-2 (WV2) and WorldView-3 (WV3) to derive VIs. A number of Statistical and ML approaches including Linear regression (LR), Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBOOST), Ridge, Least Absolute Shrinkage and Selection Operators (LASSO) and Partial Least Square (PLSR) regressions were employed at various stages throughout the study

Four publications were produced during the study, with the key findings of each publication presented as follows:

• The S2-derived Enhanced Vegetation Index (EVI) was identified as the index that best distinguished five phenological stages of mango (Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D)) of four mango farms in Ghana.

• S1-derived radar VI was identified to be responsive for distinguishing three phenological stages (Start of Season (SoS), Peak of Season (PoS) and End of Season (EoS)) from a mango farm in Ghana. These stages align well with three of the key phenological stages (F/FS, M/H and D) retrieved in the optical data (S2) experiment above. It was also established that although weather is known to influence growth and yield of HTCs, for the weather conditions of the study area, its influence on phenology was marginal.

• The evaluation of 24 WV3-derived VIs from individual tree canopies and associated fruit counts collected across many locations, seasons and cultivars (n = 1958), identified no consistent generic relationship between the predictor (24 VIs) and response (fruit count) variables at the individual orchard level. The subsequent modelling of all composite data through ML algorithms, identified the RF-based yield prediction accuracy was better at the farm level than the individual tree level with percentage root mean square error (PRMSE) of 10.1% and 26.5% respectively, for the combined model (i.e. a model trained on all cultivars, locations and seasons data). The potential of developing an ML-based yield variability map at the individual tree level to support precision agriculture was demonstrated.

• An RF-based time series model is capable of predicting block and farm level mango yield around 3 - 5 months ahead of the commencement of the commercial harvest season. The block level combined RS/weather-based RF model for 2021 produced the best result (mean absolute error (MAE) = 2.9 t/ha), marginally better than the RS only RF model (MAE = 3.4 t/ha). The farm level model error (FLEM) was generally lower than the block level model error, for both the combined RS/weather-based RF model (farm = 3.7%, block (NMAE) = 33.6% for 2021) and the RS-based model (farm = 4.3%, block = 38.4% for 2021). The errors thus, ranged from 3.7% to 82.7% and 28.7% to 70.7% at the farm and block levels respectively, for the RS/weather-based RF model across the 8-year time series. Factors such as irregular bearing and data associated limitations were possible causes of errors in the study. The study demonstrated the ability to improve yield prediction accuracy from a finer (e.g. block level) to coarser (e.g. farm level) scales as positive (overprediction) and negative (underprediction) errors tend to cancel out.

Overall, this study demonstrated the potential of integrating RS and weather variables for accurate mango yield prediction. Nevertheless, challenges including a lack of extensive farm level standardized data and the high cost of high-resolution imagery exist. Additionally, whilst these methodologies did demonstrate some benefit over existing practice, further validation of these methodologies is required over more growing locations, cultivars and seasons. This also includes the extrapolation of models at multiple scales, particularly regional and national levels (which were beyond the scope of this study). Furthermore, future research could explore the potential of this method to produce robust estimates in other perennial tree crops.