Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in 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

A linear high-efficiency millimeter-wave CMOS Doherty radiator leveraging on-antenna active load-modulation

This thesis presents a Doherty Radiator architecture that explores multi-feed antennas to achieve an on-antenna Doherty load modulation network and demonstrate high-speed high-efficiency transmission of wideband modulated signals. On the passive circuits, we exploit the multi-feed antenna concept to realize compact and high-efficiency on-antenna active load modulation for close-to-ideal Doherty operation, on-antenna power combining, and mm-Wave signal radiation. Moreover, we analyze the far-field transmission of the proposed Doherty Radiator and demonstrate its wide Field-of-View (FoV). On the active circuits, we employ a GHz-bandwidth adaptive biasing at the Doherty Auxiliary power amplifier (PA) path to enhance the Main/Auxiliary Doherty cooperation and appropriate turning-on/-off of the Auxiliary path. A proof-of-concept Doherty Radiator implemented in a 45nm CMOS SOI process over 62-68GHz exhibits a consistent 1.45-1.53× PAE enhancement at 6dB PBO over an idealistic class-B PA with the same PAE at P1dB. The measured Continuous-Wave (CW) performance at 65GHz demonstrates 19.4/19.2dBm PSAT/P1dB and achieves 27.5%/20.1% PAE at peak/6dB PBO, respectively. For single-carrier 1Gsym/s 64-QAM modulation, the Doherty Radiator shows average output power of 14.2dBm with an average 20.2% PAE and -26.7dB EVM without digital predistortion. Consistent EVMs are observed over the entire antenna FoV, demonstrating spatially undistorted transmission and constant Doherty PBO efficiency enhancement.M.S

Study on the Use of Construction and Demolition Waste for Road Base or Subbase Pavement Construction in Hanoi

Reuse or recycling of construction and demolition waste (CDW) has become an inevitable trend in the world. Currently, the amount of CDW generated in Hanoi is estimated at more than 4,000 tons per day, of which only about 30% has been controlled and recycled. The CDW comes from many different sources such as construction, repair, renovation, demolition of houses, residential buildings, public buildings, transportation infrastructure works, etc... The CDW commonly comprises soil, bricks, mortar and concrete, and has been reused in many applications around the world. In Vietnam, there are also some research programs set up for reutilizing the material, however, has not been concretely applied in practice. In order to consider the applicability of CDW in road construction, an experimental program was conducted using CDW as the aggregate for the cement treated grain material base layer in road pavement structure. The weight ratios of cement used in the mixture were 4%, 5%, 6%, 7% and 8%. The test results showed that the main mechanical properties of compressive strength, split tensile strength and elastic modulus of the mixture, increased proportionally with the cement content in the mixture

Investigating and implementing a student vocational education model for educational innovation

The development of each student's awareness serves as the governing principle for high school vocational education programs. This awareness then becomes the driving force behind the progression of the educational process. Career education activities for students are the relationships between the objectives, contents, methods and forms of organization of educational activities that are directly and constantly influenced by the educational environment. Student career education activities are the relationships between these aspects of educational activities as determined by research into the programs, textbooks, systematization and theoretical analysis of these activities. This investigation focuses on the following areas: (1) Developing preschool and high school teachers in the province of Dong Thap to meet the criteria of the new educational program (2) Developing models of applying local educational material for students in the province of Dong Thap. Both of these initiatives are part of the Dong Thap Educational Development Project. Findings: Assess the current state of activities for students in the province of Dong Thap that are related to vocational education between 2018- 2021. Develop a model for carrying out activities for students participating in vocational education in the province of Dong Thap to fulfill educational innovation requirements

Copula-based agricultural conditional value-at-risk modelling for geographical diversifications in wheat farming portfolio management

An agricultural producer's crop yield and 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 variabilities in crop price and yield, associated with climate variations. There has been limited research performed on the effectiveness of this strategy. The paper proposed 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 Australian agro-ecological zones. A suite of popular and statistically robust tools applied in finance based on well-established statistical theories, comprised of the Conditional Value-at-Risk (CVaR) and the joint copula model were employed to evaluate the effectiveness geographical diversification. CVaR is utilised to benchmark the loss (i.e., downside risk), while the copula function is employed to model 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., targeting 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

General equilibrium impact evaluation of food top-up induced by households’ renewable power self-supply in 141 regions

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

DOLABRANE-TYPE DITERPENOID AND LIGNAN CONSTITUENTS FROM THE STEM BARKS OF CERIOPS DECANDRA (GRIFF.) W. THEOB.

Three dolabrane-type diterpenoids (1‒3) and a lignan derivative (4) were isolated from a methanolic extract of C. decandra stem barks using various chromatographic separations. Their structures were elucidated to be tagalsine X (1), tagalsin P (2), ent-5α,2-oxodolabr-3-ene-3,15,16-triol (3), and (+)-pinoresinol (4) by detailed analysis via spectroscopic techniques (1D, 2D NMR, and ESI-MS data) as well as comparison with those reported. This is the first report of compound 4 from the Ceriops genus

Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data

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

New records of bats (Mammalia: Chiroptera) from Cu Lao Cham and Ly Son archipelagos, central Vietnam

Cu Lao Cham and Ly Son are two well-known archipelagos of Vietnam for their specular landscapes and varied ecosystems including forest, cave, and agriculture. However, their bat fauna has received little attention. Between July 2017 and August 2018, we conducted a series of mammal surveys with emphasis on bats of the two archipelagos. Bats were captured by mist nets and harp traps. Echolocation calls of microchiropteran species were recorded using the PCTape system then analysed by Selena software. With reference to all available literatures and specimens from the recent surveys, we obtained confirmed records of 9 bat species from Cu Lao Cham and 3 species from Ly Son. Of these, Megaderma spasma and Taphozous melanopogon are new to Cu Lao Cham while Rhinolophus macrotis is new to Ly Son. These three species were rarely recorded from other islands of Vietnam and also uncommon within Cu Lao Cham and Ly Son. These new records not only expand the known distributional range, but also provide worthwhile notes on a narrow geographical variation in morphology and echolocation of each species

Global disparities in agricultural climate index-based insurance research

Agricultural climate index-based-insurance (IBI) compensates farmers for losses from adverse climatic conditions. Using a systemic review, we show that research related to agricultural climate index-based-insurance efficacy and application is lacking in many climate and food security vulnerable countries. We concluded that there are countries with high climate and food insecurity risk based on several climate and food security indicators that lack agricultural climate index-based-insurance research that could help farmers in these countries. Research to date has also largely focused on cereal crops and drought, which only represent a fraction of the crops and climate risks that agricultural climate index-based-insurance could be beneficial in managing. Our paper provides evidence-based recommendations for countries that should be focused on to redress the current disparities in agricultural climate index-based-insurance research