Smart economics: evaluation of Australian aid support for women’s economic empowerment

This report evaluates how effective Australia has been in achieving gender equality outcomes in economic development programs. Executive summary Promoting women’s economic empowerment is ‘smart economics’. When women are fully involved in economic development, countries become more equitable and prosperous. Worldwide, there has been steady progress for women and girls in the key sectors of health and education. However, Australia—like most donors—has had limited success in achieving gender equality outcomes in economic development programs. Last financial year, approximately 25 per cent of the Australian aid budget was invested in the economic sectors, ranging from primary industry through to the production of goods and provision of services. Australian aid support for economic development is mainly concentrated on agriculture, rural development and transport. Smaller, but still significant, amounts of aid focus on energy, trade, and business and banking. Less than a third of this economic sector investment can demonstrate an explicit focus on gender equality. This is a concern, especially given Australia’s longstanding policy of ‘mainstreaming’ gender equality in its aid program

Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand. EMBARGOED until 30 September 2023.

Embargoed until 30 September 2023There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research

The importance of implementing good aquaculture practices based on the analysis of Qur’anic verses

The objectives of the papers include presenting the current conditions of aquaculture practices, understanding the importance of good aquaculture practices and most importantly reviewing the Quranic verses in accordance with the importance to conduct good aquaculture practices. The aquaculture itself is in charge of around 47 per cent of the fish consumption in the world. Its value is estimated to be around 125 billion USD and it is responsible for 13% of the world’s animal-source protein (eggs and dairy excluded). Around 24 million people rely on the sustainability of aquaculture to ensure they have a proper living. The Quranic verses state about environmental corruption, prohibition of wastefulness and the importance in ensuring the environment to be well protected. It is crucial that humans start to carefully monitor the environment as what happens to it is derived from their actions, as stated in Quránic verse 30:41: “Mischief has appeared on the land and sea because of (the meed) that the hands of men have earned.” The paper also discusses the importance of productivity and quality as aspects to achieve success. The method used is the library research which includes the observation from Islamic perspectives. This is in line with achieving the highest objectives of Shariah, which protect the farmers, the environment, the aquaculture and the consumers. The output of the study may serve MyGAP and MS 1500:2009

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

The evolution of business analytics : based on case study research

While business analytics is becoming more significant and widely used by companies from increasing industries, for many the concept remains a complex illusion. The field of business analytics is considerably generic and fragmented, leaving managers confused and ultimately inhibited to make valuable decisions. This paper presents an evolutionary depiction of business analytics, using real-world case studies to illustrate a distinct overview that describes where the phenomenon was derived from, where it currently stands, and where it is heading towards. This paper provides eight case studies, representing three different eras: yesterday (1950s to 1990s), today (2000s to 2020s), and tomorrow (2030s to 2050s). Through cross-case analysis we have identified concluding patterns that lay as foundation for the discussion on future development within business analytics. We argue based on our findings that automatization of business processes will most likely continue to increase. AI is expanding in numerous areas, each specializing in a complex task, previously reserved by professionals. However, patterns show that new occupations linked to artificial intelligence will most probably be created. For the training of intelligent systems, data will most likely be requested more than ever. The increasing data will likely cause complications in current data infrastructures, causing the need for stronger networks and systems. The systems will need to process, store, and manage the great amount of various data types in real-time, while maintaining high security. Furthermore, data privacy concerns have become more significant in recent years, although, the case study research indicates that it has not limited corporations access to data. On the contrary, corporations, people, and devices will most likely become even more connected than ever before.nhhma

Big data analytics tools for improving the decision-making process in agrifood supply chain

Introduzione: Nell'interesse di garantire una sicurezza alimentare a lungo termine di fronte a circostanze mutevoli, è necessario comprendere e considerare gli aspetti ambientali, sociali ed economici del processo di produzione. Inoltre, a causa della globalizzazione, sono stati sollevati i problemi delle lunghe filiere agroalimentari, l'asimmetria informativa, la contraffazione, la difficoltà di tracciare e rintracciare l'origine dei prodotti e le numerose questioni correlate quali il benessere dei consumatori e i costi sanitari. Le tecnologie emergenti guidano verso il raggiungimento di nuovi approcci socioeconomici in quanto consentono al governo e ai singoli produttori agricoli di raccogliere ed analizzare una quantità sempre crescente di dati ambientali, agronomici, logistici e danno la possibilità ai consumatori ed alle autorità di controllo della qualità di accedere a tutte le informazioni necessarie in breve tempo e facilmente. Obiettivo: L'oggetto della ricerca riguarda lo studio delle modalità di miglioramento del processo produttivo attraverso la riduzione dell'asimmetria informativa, rendendola disponibile alle parti interessate in un tempo ragionevole, analizzando i dati sui processi produttivi, considerando l'impatto ambientale della produzione in termini di ecologia, economia, sicurezza alimentare e qualità di cibo, costruendo delle opportunità per le parti interessate nel prendere decisioni informate, oltre che semplificare il controllo della qualità, della contraffazione e delle frodi. Pertanto, l'obiettivo di questo lavoro è quello di studiare le attuali catene di approvvigionamento, identificare le loro debolezze e necessità, analizzare le tecnologie emergenti, le loro caratteristiche e gli impatti sulle catene di approvvigionamento e fornire utili raccomandazioni all'industria, ai governi e ai policy maker.Introduction: In the interest of ensuring long-term food security and safety in the face of changing circumstances, it is interesting and necessary to understand and to take into consideration the environmental, social and economic aspects of food and beverage production in relation to the consumers’ demand. Besides, due to the globalization, the problems of long supply chains, information asymmetry, counterfeiting, difficulty for tracing and tracking back the origin of the products and numerous related issues have been raised such as consumers’ well-being and healthcare costs. Emerging technologies drive to achieve new socio-economic approaches as they enable government and individual agricultural producers to collect and analyze an ever-increasing amount of environmental, agronomic, logistic data, and they give the possibility to the consumers and quality control authorities to get access to all necessary information in a short notice and easily. Aim: The object of the research essentially concerns the study of the ways for improving the production process through reducing the information asymmetry, making it available for interested parties in a reasonable time, analyzing the data about production processes considering the environmental impact of production in terms of ecology, economy, food safety and food quality and build the opportunity for stakeholders to make informed decisions, as well as simplifying the control of the quality, counterfeiting and fraud. Therefore, the aim of this work is to study current supply chains, to identify their weaknesses and necessities, to investigate the emerging technologies, their characteristics and the impacts on supply chains, and to provide with the useful recommendations the industry, governments and policymakers

DIGITISING AGRIFOOD Pathways and Challenges. November 2019

As climate change increasingly poses an existential risk for the Earth, scientists and policymakers turn to agriculture and food as areas for urgent and bold action, which need to return within acceptable Planet Boundaries. The links between agriculture, biodiversity and climate change have become so evident that scientists propose a Great Food Transformation towards a healthy diet by 2050 as a major way to save the planet. Achieving these milestones, however, is not easy, both based on current indicators and on the gloomy state of global dialogue in this domain. This is why digital technologies such as wireless connectivity, the Internet of Things, Arti cial Intelligence and blockchain can and should come to the rescue. This report looks at the many ways in which digital solutions can be implemented on the ground to help the agrifood chain transform itself to achieve more sustainability. Together with the solution, we identify obstacles, challenges, gaps and possible policy recommendations. Action items are addressed at the European Union both as an actor of change at home, and in global governance, and are spread across ten areas, from boosting connectivity and data governance to actions aimed at empowering small farmers and end users

Harnessing Technology: analysis of emerging trends affecting the use of technology in education – October 2009

Research to support the delivery and development of Harnessing Technology: Next Generation Learning 2008–1