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This study aimed to investigate some main factors affecting secondary students’ choice of high school in Tra Vinh province. A group of 777 participants including 440 urban and 337 rural secondary students in Tra Vinh city and some districts of Tra Vinh province was selected to answer the questionnaire. The results from the questionnaire revealed that three groups of factors (educational environment, school distinction and personal conditions) had nearly equal overall mean scores and influenced students more than the three remaining factors (social network, extra activities and location-transportation) in choosing their high school. In addition, no significant difference was found between urban and rural secondary students in choosing their high school. Based on the findings above, some possible recommendations were suggested to the executive boards of high school, secondary school teachers and secondary school students in order to assist them in choosing their appropriate high school.

This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.

This study describes flash drought (FD) inferred from the Evaporative Stress Index (ESI) over Australia and its relationship to vegetation. During 1975-2020, FD occurrence ranges from less than one per decade in the central arid regions to 10 per decade toward the coasts. Although FD can occur in any season, its occurrence is more frequent in summer in the north, winter in the southern interior and southwest, and across a range of months in the far southeast and Tasmania. With a view towards real-time monitoring, FD “declaration” is defined as the date when the ESI declines to at least -1, i.e., drought conditions, after at least 2 weeks of rapid decline. Composite analysis shows that evaporative demand begins to increase about 5-6 weeks before declaration with an increase in solar radiation, while evapotranspiration initially increases with evaporative demand but then decreases in response to the soil moisture depletion. Solar radiation increases simultaneously with precipitation deficit, both reaching their peak around declaration. FD intensity peaks with soil moisture depletion, 2-3 weeks after declaration. The composite wind speed only shows a modest increase around declaration. The composite FD ends 4 weeks after rapid decreases in solar radiation and increases in precipitation. Satellite-derived vegetation health composites show pronounced decline in the non-forested regions, peaking about 4-8 weeks after FD declaration, followed by a recovery period lasting about 12 weeks after flash drought ends. The forest-dominated regions, however, are little impacted. Modelled pasture growth data shows reduced values for up to 3 months after the declaration month covering the main agricultural areas of Australia.

The Northern Australia Climate Program (NACP) is a fully integrated research, development and extension (RDandE) program operating across extensive pastoral regions of northern Australia. The NACP aims to improve existing climate models and forecast tools, develop new products to meet user needs and build the capacity of rangeland producers to manage the challenges posed by droughts (or failed wet seasons) and climate variability. Climate information gaps identified through earlier surveys of graziers and communities in rural and remote Australia informed the design of the research component of the NACP, which aims to address the low and variable accuracy of seasonal climate forecasts in many regions, the need for proof of value of forecasts and relevance of existing forecast systems and technologies, and perceived lack of effective support from climate experts for the use of climate resources and technologies in agricultural decision making. The development and extension components of the program aim to improve climate literacy and the use of climate information. Building on the research program, they deliver a climate service that provides local extension and technical support, with a focus on building trust in climate information through locally sourced, industry connected NACP trained and supported extension advisers called Climate Mates. Two-way information flow between decision makers and researchers, facilitated by the Climate Mates, ensures that forecasts and decision- and discussion-support tools developed through the program are regionally relevant and targeted to the needs of end users. Monitoring and evaluation of the program indicates that this approach is contributing to positive outcomes in terms of awareness and knowledge of climate forecasting and products, and their adoption and use in decision making (i.e. practice change). In the longer term, the Climate Mates have potential for enduring impact beyond the program, leaving a knowledgeable and trusted climate resource across regional northern Australia.

Australia is an agricultural nation characterised by one of the most naturally diverse climates in the world, which translates into significant sources of risk for agricultural production and subsequent farm revenues. Extreme climatic events have been significantly affecting large parts of Australia in recent decades, contributing to an increase in the vulnerability of crops, and leading to subsequent higher risk to a large number of agricultural producers. However, attempts at better managing climate related risks in the agricultural sector have confronted many challenges. First, crop insurance products, including classical claim-based and index-based insurance, are among the financial implements that allow exposed individuals to pool resources to spread their risk. The classical claim-based insurance indemnifies according to a claim of crop loss from the insured customer, and so can easily manage idiosyncratic risk, which is the case where the loss occurs independently.Nevertheless, the existence of systemic weather risk (covariate risk), which is the spread of extreme events over locations and times (e.g., droughts and floods), has been identified as the main reason for the failure of private insurance markets, such as the classical multi-peril crop insurance, for agricultural crops. The index-based insurance is appropriate to handle systemic but not idiosyncratic risk. The indemnity payments of the index-based insurance are triggered by a predefined threshold of an index (e.g., rainfall), which is related to such losses. Since the covariate nature of a climatic event, it sanctions the insurers to predict losses and ascertain indemnifications for a huge number of insured customers across a wide geographical area. However, basis risk, which is related to the strength of the relationship between the predefined indices used to estimate the average loss by the insured community and the actual loss of insured assets by an individual, is a major barrier that hinders uptake of the index-based insurance. Clearly, the high basis risk, which is a weak relationship between the index and loss, destroys the willingness of potential customers to purchase this insurance product. Second, the impact of multiple synoptic-scale climate mode indices (e.g., Southern Oscillation Index (SOI) and Indian Ocean Index (IOD)) on precipitation and crop yield is not identical in different spatial locations and at different times or seasons across the Australian continent since the influence of large-scale climate heterogeneous over the different regions. The occurrence, role, and amplitude of synoptic-scale climate modes contributing to the variability of seasonal crop production have shifted in recent decades. These variables generally complicate the climate and crop yield relationship that cannot be captured by traditional modelling and analysis approaches commonly found in published agronomic literature such as linear regression. In addition, the traditional linear analysis is not able to model the nonlinear and asymmetric interdependence between extreme insurance losses, which may occur in the case of systemic risk. Relying on the linear method may lead to the problem that different behaviour may be observed from joint distributions, particularly in the upper and lower regions, with the same correlation coefficient. As a result, the likelihood of extreme insurance losses can be underestimated or overestimated that lead to inaccuracies in the pricing of insurance policies. Another alternative is the use of the multivariate normal distribution, where the joint distribution is uniquely defined using the marginal distributions of variables and their correlation matrix. However, phenomena are not always normally distributed in practice. It is therefore important to develop new, scientifically verified, strategic measures to solve the challenges as mentioned above in order to support mitigating the influences of the climate-related risk in the agricultural sector. Copulas provide an advanced statistical approach to model the joint distribution of multivariate random variables. This technique allows estimating the marginal distributions of individual variables independently with their dependence structures. It is clear that the copula method is superior to the conventional linear regression since it does not require variables have to be normally distributed and their correlation can be either linear or non-linear. This doctoral thesis therefore adopts the advanced copula technique within a statistical modelling framework that aims to model: (1) The compound influence of synoptic-scale climate indices (i.e., SOI and IOD) and climate variables (i.e., precipitation) to develop a probabilistic precipitation forecasting system where the integrated role of different factors that govern precipitation dynamics are considered; (2) The compound influence of synoptic-scale climate indices on wheat yield; (3) The scholastic interdependencies of systemic weather risks where potential adaptation strategies are evaluated accordingly; and (4) The risk-reduction efficiencies of geographical diversifications in wheat farming portfolio optimisation. The study areas are Australia’s agro-ecological (i.e., wheat belt) zones where major seasonal wheat and other cereal crops are grown. The results from the first and second objectives can be used for not only forecasting purposes but also understanding the basis risk in the case of pricing climate index-based insurance products. The third and fourth objectives assess the interactions of drought events across different locations and in different seasons and feasible adaptation tools. The findings of these studies can provide useful information for decision-makers in the agricultural sector. The first study found the significant relationship between SOI, IOD, and precipitation. The results suggest that spring precipitation in Australia, except for the western part, can be probabilistically forecasted three months ahead. It is more interesting that the combination of SOI and IOD as the predictors will improve the performance of the forecast model. Similarly, the second study indicated that the largescale climate indices could provide knowledge of wheat crops up to six months in advance. However, it is noted that the influence of different climate indices varies over locations and times. Furthermore, the findings derived from the third study demonstrated the spatio-temporally stochastic dependence of the drought events. The results also prove that time diversification is potentially more effective in reducing the systemic weather risk compared to spatially diversifying strategy. Finally, the fourth objective revealed that wheat-farming portfolio could be effectively optimised through the geographical diversification. The outcomes of this study will lead to the new application of advanced statistical tools that provide a better understanding of the compound influence of synoptic-scale climatic conditions on seasonal precipitation, and therefore on wheat crops in key regions over the Australian continent. Furthermore, a comprehensive analysis of systemic weather risks performed through advanced copula-statistical models can help improve and develop novel agricultural adaptation strategies in not only the selected study region but also globally, where climate extreme events pose a serious threat to the sustainability and survival of the agricultural industry. Finally, the evaluation of the effectiveness of diversification strategies implemented in this study reveals new evidence on whether the risk pooling methods could potentially mitigate climate risks for the agricultural sector and subsequently, help farmers in prior preparation for uncertain climatic events.

Development and implementation of advanced statistical models for analyzing stochastic dependencies of systemic weather risk can help farmers, agricultural policy-makers and financial agents to address potential risk adaptation strategies and mitigation of threats to the agricultural industry. This study develops copula-based statistical models to provide a better understanding of systemic weather risks with agricultural and weather event data from Australia. In particular, we adopt a C-vine approach to model the joint insurance losses caused by drought events occurring simultaneously across different locations, and consecutively in different growing seasons. This modelling approach is enriched by a clustering analysis process through the multidimensional Kruskal–Shephard scaling method. Daily rainfall data (1889–2012) recorded in sixteen meteorological stations across Australia’s wheat belt spanning different climatic conditions are employed. On a regional scale, droughts occurring in the west are more scattered during the October–December period and for April–June and October–December in the eastern, south-eastern and southern regions. On a national scale, drought events in the east are likely to spread out to the south-east and the south but not to the west. The results also reveal that the drought events in different seasons may not be perfectly correlated. Therefore, we conclude that spatial and temporal diversification strategies are likely to feasibly reduce the systemic weather risk in Australia. In particular, the average risk-reducing effect of the entire insured area across regional, national and temporal scales ranges between 0.62–0.94, 0.48–0.76, and 0.25–0.33, corresponding to 5%- (extreme drought) and 25%-quantiles (moderate drought). The findings suggest that diversifying risks over time is potentially more effective than spatial diversification. The outcomes may also act as an efficient tool for agricultural risk reduction, but simultaneously, it may also provide immensely useful information for suitable pricing of weather index-based insurance products.

Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index (TVDI), we specified the relationships and changes underway in Vietnam’s Ba river basin, one of the largest river systems in the South Central Coast. Using Google Earth Engine, we extracted land use data from Landsat images and calculated TVDI values from Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000 to 2019. We found, first, that agricultural area and deforestation rose by 7.2% and 2.4% annually, respectively. These changes were driven by economic development, rising crop prices, illegal logging, wildfires, and emergence of new agricultural areas. Second, areas classified in the driest TVDI intervals (dry and very dry) occupied 57% of the basin in 2019, 70% of which was agricultural lands and 20% other (mainly urban and bare lands). These driest land categories expanded at an average rate of 0.08% to 0.1% per year. Moreover, 90% of areas classified as “very wet” and “wet” were forest. We observed a strong relationship between LULC change and TVDI. Climate change and LULC change thus appear to be propelling the basin’s climate toward increasing dryness, suggesting the need for policies to reduce the agricultural area and expand forests while developing more adaptive and sustainable livelihoods.

This chapter aims to design and evaluate data-driven models based on a hybrid complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) technique and support vector machine model (SVM) to forecast multistep wind speed in Australia. Wind speed forecasting for 6-hourly, daily, and monthly horizons are developed at the four study sites of Albany, Capital, Macarthur, and Woolnorth located in wind belt regions. The outcomes show that the standalone SVM and Volterra models had similar computation efficiency and model performances, while standalone ARIMA had the worst performance for all sites and all timescales. The proposed CEEMDAN–SVM is superior to standalone models and is effectively applied in optimizing the combined model. The CEEMDAN–SVM outperformed the alternative models at all sites for 6-hourly and daily forecasting horizons and outperformed at two sites for a monthly scale. The proposed algorithms were found to be effective in high-precision wind speed forecasting.

The intention of this research was to try and address the research question 'Is machine learning algorithm, Multivariate Adaptive Regression Splines model, a versatile forecasting model for solar radiation?' The objective of this chapter is to develop a machine learning (ML) algorithm to validate and assess errors for the method used to forecast solar radiation based on historical data. The specific aims are to construct (1) short-term (daily) global solar radiation model using the MARS algorithm considering the nonlinear behavior of surface-level solar radiation with its predictor variables; and (2) long-term (monthly) global solar radiation model using the MARS algorithm to enable the solar energy assessment over a long-term period and considering. This chapter carried out short-term and long-term solar radiation forecasting model development for regional Queensland. Short-term forecasting provides predictions up to 7 days ahead. These forecasts are valuable for grid operators in order to make important decisions for grid operation. It will provide valuable information regarding the time scheduling of power systems (Wan et al., 2015). Long-term forecasting has been carried out considering 1-month ahead, 3-month, and 6-month ahead forecast. This is useful for energy companies to make decisions and negotiate contracts with energy producers (Marti´n et al., 2010) and also for effective operation and maintenance planning of solar power systems (Koca et al., 2011). The information gathered from the seasonal analysis can be used for studying the seasonal patterns of the solar energy and for Seasonal Thermal Energy Storage (i.e., STES) (Allen et al., 1984) where the heat acquired from solar collectors in hot months can be stored for future use when needed, including during winter months.

The upstream construction of hydropower dams may drastically intensify climate change impacts due to changing the natural river flood-drought cycle and reducing the amount of water that flows into the lower Mekong Delta river, leading to hydrological and environmental health impacts. However, until now the influence of drought on residents’ health in the lower MDR, where river drought is highly sensitive to recently built hydropower plants, has not been examined. The objectives of this study are, for the first time, to detect the health impacts of river drought on residents and to evaluate the contribution of hydropower dams to the impacts of drought on health in the lower Mekong Delta Region (MDR). We applied the multi-step approaches of a Detection and Attribution study. First, we detected the effects of the river drought on the risk of hospitalization using a Multivariable Fractional Polynomials algorithm (MFP). Second, we linked the long-term changes of the river water level (RWL) to the operation of the first hydropower dam in the upper MDR using the interrupted time-series model (ITS). Finally, we quantified the hospitalizations and related economic loss attributed to the river drought. The results show that the percentage changes in risk of all-cause, respiratory, and renal hospitalizations attributed to the river drought were 2%, 2%, and 7%. There were significant reductions in average level and trend of the RWL during the post-1995 period, when the first hydropower dam began operation in the upper MDR, even though the cumulative rainfall in the MDR had not changed. The all-cause hospitalizations attributed to the river drought were 1134 cases during the period 1995–2014, which resulted in total additional cost at two provincial hospitals of US $360,385. This current study demonstrates the link between hydropower dams, river drought, and health impacts. As the MDR is highly vulnerable to climate change, these findings about the devastating impacts of hydropower dams and environmental change have important implications for the lives of downstream residents.

Background: ImageJ software is used to quantify the joint space width (JSW) of hand and wrist in patients with rheumatoid arthritis (RA) as well as in the healthy control group. Method: Forty-one RA patients and 31 healthy controls were included in this study. All of 72 participants underwent digital radiography of the bilateral hand and wrist; then, all the images were opened by ImageJ software to measure the width of wrist and hand joint space (total 2160 joints). Joint space narrowing (JSN) was defined if the width was less than the mean - 2SD of the control group. Result: The mean JSW of all sites of wrist and hand joints of RA patients were significantly reduced as compared to those in the control group (p Conclusion: ImageJ software is simple and convenient that helps rheumatologists quantify the width of joint space for diagnosis and follow-up in RA patients.

This chapter aims to design and evaluate Artificial Neural Networks (ANN), an intelligent data analytic model to predict daily Steadman Heat Index (SHI) using temperature and humidity. Using 15 stations in Australia, trend analysis for the period 1950–2017 is performed using Mann–Kendal test statistics Sen’s slope methods. Twelve ANN models are developed with a three-layer network employing different combinations of the training algorithm, hidden transfer, and output function. The Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms are utilized to determine the best combination of learning algorithms, hidden transfer, and output functions of the optimum ANN model. Assessment of model performance includes the spread and distribution of predicted SHI, Legates and McCabe Index, Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, the Willmott’s Index of Agreement, and Nash–Sutcliffe Coefficient of Efficiency. The designed model appears to be a suitable intelligent data analytic tool for weather prediction, climate change studies, and probable evaluation of dry climatic conditions in the near future replying to historical datasets to model their future values. The findings have implications for disaster risk management particularly mitigating heatwave risk and consequences on human populations, ecosystems, and other areas including agricultural, health, and wellbeing.

Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily flood index (FI) forecasting for Lockyer Valley in southeast Queensland, Australia, using data from January 01, 1950, to December 31, 2012. The MEMD-M5 tree is evaluated against MEMD-RF, standalone M5 tree, and RF models via statistical metrics, diagnostic plots with error distributions between simulated and observed daily flood index. The results indicate that MEMD-M5 tree outperforms the comparative models by attaining maximum values of r = 0.990, WI = 0.992, ENS = 0.979, and L = 0.920. The MEMD-M5 tree outperforms other models by registering the least value of RMSE and MAE and can precisely emulate 97.94% of daily FI value. Graphical diagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a greater resemblance to that of the observed data supporting the outcomes of statistical evaluation. Such advancements in flood prediction models, attained through data intelligent analytical methods, are very vital and effective in ensuring better mitigation and civil protection in emergency providing an early warning system, disaster risk reduction, disaster policy suggestions, and reduction of the property damage.

Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Nino–Southern Oscillation (ENSO). The chapter applies an advanced statistical copula approach to model lag relationships between monthly Southern Oscillation Index (SOI), an ENSO indicator, and monthly Flood Index (FI) that can be used for flood prediction. Copula parameters were numerically derived from under a hybrid-evolution Markov chain Monte Carlo (MCMC) approach within a Bayesian framework. The empirical findings showed that monthly SOI data from Aug to Dec have a significant correlation with monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems.

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding flood event preparedness plans and mitigation measures. Introducing an initial framework of spatially probabilistic analysis of flood research, this study highlights an integrated statistical copula and satellite data-based approach to modelling the complex dependence structures between flood event characteristics, i.e., duration (D), volume (V) and peak (Q). The study uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution of ∼5 km) during 1981–2019 to derive a Standardized Antecedence Precipitation Index (SAPI) and its characteristics through a time-dependent reduction function for Myanmar. An advanced vine copula model was applied to model joint distributions between flood characteristics for each grid cell. The southwest (Rakhine, Bago, Yangon, and Ayeyarwady) and south (Kayin, Mon, and Tanintharyi) regions are found to be at high risk, with a probability of up to 40% of flood occurrence in August and September in the south (Kayin, Mon, and Tanintharyi) and southwest regions (Rakhine, Bago, Yangon, and Ayeyarwady). The results indicate a strong correlation among flood characteristics; however, their mean and standard deviation are spatially different. The findings reveal significant differences in the spatial patterns of the joint exceedance probability of flood event characteristics in different combined scenarios. The probability that duration, volume, and peak concurrently exceed 50th-quantile (median) values are about 60–70% in the regions along the administrative borders of Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, and Keyan. In the worst case and highest risk areas, the probability that duration, volume, and peak exceed the extreme values, i.e., the 90th-quantile, about 10–15% in the southwest of Sagaing, southeast of Chin, Nay Pyi Taw, Mon and areas around these states and up to 30% in the southeast of Dekkhinathiri township (Nay Pyi Taw). The proposed approach could improve the evaluation of exceedance probabilities used for flood early warning and risk assessment and management. The proposed framework is also applicable at larger scales (e.g., regions, continents and globally) and in different hydrological design events and for risk assessments (e.g., insurance).

An agricultural producer's crop yield and the subsequent farming revenues are affected by many complex factors, including price fluctuations, government policy and climate (e.g., rainfall and temperature) extremes. Geographical diversification is identified as a potential farmer adaptation and decision support tool that could assist producers to reduce unfavourable financial impacts due to the variabilities in crop price and yield, associated with climate variations. There has been limited research performed on the effectiveness of this strategy. This paper proposes a new statistical approach to investigate whether the geographical spread of wheat farm portfolios across three climate broad-acre (i.e., rain-fed) zones could potentially reduce financial risks for producers in the Australian agro-ecological zones. A suite of popular and statistically robust tools applied in the financial sector based on the well-established statistical theories, comprised of the Conditional Value-at-Risk (CVaR) and the joint copula models were employed to evaluate the effectiveness geographical diversification. CVaR is utilised to benchmark the losses (i.e., the downside risk), while the copula function is employed to model the joint distribution among marginal returns (i.e., profit in each zone). The mean-CVaR optimisations indicate that geographical diversification could be a feasible agricultural risk management approach for wheat farm portfolio managers in achieving their optimised expected returns while controlling the risks (i.e., target levels of risk). Further, in this study, the copula-based mean-CVaR model is seen to better simulate extreme losses compared to the conventional multivariate-normal models, which underestimate the minimum risk levels at a given target of expected return. Among the suite of tested copula-based models, the vine copula in this study is found to be a superior in capturing the tail dependencies compared to the other multivariate copula models investigated. The present study provides innovative solutions to agricultural risk management with advanced statistical models using Australia as a case study region, also with broader implications to other regions where farming revenues may be optimized through copula-statistical models. Keywords: Copula models, Portfolio optimisation, Conditional value-at-risk, Agriculture management, Crop decision, Geographical diversification

Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.

This article employs a global computable general equilibrium economic model (GTAP-E-PowerS) to examine the impact on the world economy if households in every country self-supply power to meet 30–100% of residential demand, with subsequent monetary savings diverted to consuming more food. Results show the power generation sector reduces output levels by 14%–42% across various countries if households 100% self-supply. Coal mining sectors are adversely affected in numerous countries with contractions of 9%–28% ($6,086-$18,935 million) in the United States and 4%–13% ($2,505–$8,143 million) in Australia. Improved outcomes for the world environment are found with reductions of CO2e emission levels of 2.24%–7.38% (or 924–3,042 MtCO2 equivalent). The agriculture and food-processing sectors expand significantly in many countries but also cause major increases in land prices, particularly in land-scarce countries in Middle East, Europe, Japan, and Taiwan. Results also show the security of food and energy supply are improved along with environmental gains from lower emission levels. However, the energy sector is adversely affected and those countries with a heavy reliance on fossil fuel extraction and mining activities experience significant reductions in real GDP.

Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Nino Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Nina event is more evident than during an El Nino event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Nina and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt.

Skillful probabilistic seasonal rainfall forecasts play a vital role in supporting water resource users, developing agricultural risk-management plans, and improving decision-making processes. This chapter applies a novel statistical copula-based approach to develop a probabilistic seasonal rainfall forecast model using multiple large-scale oceanic and atmospheric climate indices. Here, a d -vine copula is used to forecast the seasonal cumulative rainfall in 16 weather stations across the Australia's Wheatbelt. These stations span different climate conditions recording historical data for the period 1889–2012. The seasonal rainfalls are forecast in different quantile levels using different climate predictor data sets. The corrected Akaike information criterion (AIC)–conditional log-likelihood is then used to screen the most influential covariates to be additively incorporated into the multivariate probabilistic forecast model, resulting in a parsimonious predictive model. The mutually inclusive correlations between El Nino–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices and seasonal rainfall are found to be statistically significant. Therefore, using the climate information, skillful rainfall forecasts can be made three to 6 months ahead. The d -vine copula model is found to outperform the traditional quantile regression methods in forecasting rainfall in the median and the upper levels. The information from lagged, concurrent, and combined climate indices is therefore demonstrated to be a potentially useful predictor for forecasting seasonal rainfall in Australia's Wheatbelt region.

Aquaculture farming carries one of the most important roles in the Vietnam economy. This paper focuses on the implementation of wireless sensor network (WSN) in shrimp farms, including the automation, remote control, and monitoring of the pond parameters. Using embedded computer Raspberry Pi 3, wireless communication modules Xbee S2c, the system can help farmers control pumps, actuator, and light via their web browser. Besides, parameters of the pond such as temperature and illuminating power are updated on the Internet via cloud service. The implementation of this project is expected to reduce energy consumption, human resource, and fast reaction to the change of the production pond, especially for large farms with multiple ponds.

International audience; AbstractClimate strongly influences agricultural profitability. Climate risks on agriculture can be managed by moving production to other areas, but this may be costly and reduce profitability if climatic conditions are dynamic. Here, we test whether integrating spatial diversification and climate information could minimize climate risk, while not sacrificing profitability. We use 27 years of farm business profit and climate data (1991–2018) from four of Australia’s most climatically diverse regions where grazing underpins socioeconomic activity. We show that spatial diversification coupled with seasonal climate information from El Niño-Southern Oscillation (ENSO) provides better estimates of optimized risk-profit tradeoffs in different ENSO years compared to estimates without climate information. Conditional Value-at-Risk (CVaR) (a measure of financial risk) is lower when climate information is used in El Niño (1.23 $/ha), La Niña (1.19 $/ha), and Neutral years (1.22 $/ha), compared to when no climate information (1.57 $/ha) is used. When targeting high profits, CVaR is reduced by 15, 86, and 22% in El Niño, La Niña, and Neutral years, respectively, compared to a 5% reduction when no climate information is used. When aiming to minimize CVaR in drought (El Niño), profit is higher using climate-informed spatial diversification (expected profits of 2.71 $/ha), relative to when it is not (expected profits of 2.30 $/ha). Climate-informed spatial diversification also provides options to graziers to achieve much higher gains (expected profits of up to ~ 4.13 $/ha) under La Niña versus 2.62 $/ha when no climate information is used. Here, we show for the first time that seasonal climate information coupled with spatial diversification can provide strategies to help graziers reduce risk and increase profitability. Our approach is applicable to other parts of the world and could be used to decrease climate risk and increase profitability for other agricultural sectors exposed to variable climatic conditions.

Coffea canephora (robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated mean R2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.

Rice is one of the world’s most dominant staple foods, and hence rice farming plays a vital role in a nation’s economy and food security. To examine the applicability of synthetic aperture radar (SAR) data for large areas, we propose an approach to determine rice age, date of planting (dop), and date of harvest (doh) using a time series of Sentinel-1 C-band in the entire Mekong Delta, Vietnam. The effect of the incidence angle of Sentinel-1 data on the backscatter pattern of paddy fields was reduced using the incidence angle normalization approach with an empirical model developed in this study. The time series was processed further to reduce noise with fast Fourier transform and smoothing filter. To evaluate and improve the accuracy of SAR data processing results, the classification outcomes were verified with field survey data through statistical metrics. The findings indicate that the Sentinel-1 images are particularly appropriate for rice age monitoring with R2 = 0.92 and root-mean-square error (RMSE) = 7.3 days (n = 241) in comparison to in situ data. The proposed algorithm for estimating dop and doh also shows promising results with R2 = 0.92 and RMSE = 6.2 days (n = 153) and R2 = 0.70 and RMSE = 5.7 days (n = 88), respectively. The results have indicated the ability of using Sentinel-1 data to extract growth parameters involving rice age, planting and harvest dates. Information about rice age corresponding to the growth stages of rice fields is important for agricultural management and support the procurement and management of agricultural markets, limiting the negative effects on food security. The results showed that multitemporal Sentinel-1 data can be used to monitor the status of rice growth. Such monitoring system can assist many countries, especially in Asia, for managing agricultural land to ensure productivity.