Fed Up: Now's the Time to Invest in Agro-Ecology

As trends in investment in agriculture in poorer countries edge up, the combined effects of climate change, energy scarcity and water paucity now demand that we radically rethink our agricultural systems.Business as usual will not do. An unprecedented combination of pressures is emerging to threatenthe health of existing social and ecological systems. Population and income growth, urbanization,changing consumption patterns, stagnant yields, demand for land, feed, and biofuels, and theimpact of climate change, biodiversity loss and environmental degradation are driving limited resources of food, energy, water and materials towards critical thresholds.The combined effects of climate change, land degradation, cropland losses, water scarcity and species infestations may cause projected yields to be 5-25% short of demand by 2050, and 600 million additional people could be affected by malnutrition as a direct result of climate change by 2080.The current food system is failing to feed the world adequately, and widespread poverty and inequality mean that many are too poor to access the food that is available. Despite there being enough food for everyone, an estimated 925 million people are hungry and another billion suffer from 'hidden hunger' and micro-nutrient deficiency, while 1.5 billion people are overweight and obese, and a third of all food for human consumption is lost, spoiled, or wasted.Productivity gains from the Green Revolution have not always been sustainable over time and often came at a high social and environmental cost, including the depletion of soils, pollution of groundwater, biodiversity loss, high household debts, and increased inequality among farmers.With case study evidences from Bangladesh, Cambodia, Indonesia and Pakistan, and citing global studies and surveys, this report argues that agro-ecology -- or ecological agriculture -- offers tools that can help the poorest communities to develop new, affordable, dynamic, low-carbon and locally-adaptable models of agricultural development to meet these multiple challenges. Recent research shows that agro-ecology is highly productive and holds great promise for the roughly 500 million food-insecure households around the world.Agro-ecology is the application of ecological science to the study, design, and management of sustainable agriculture, and it is based on practices such as recycling biomass, improving soils through green manures, mulches and bio-fertilisers, minimising water, nutrient and solar radiationlosses, intercropping, mixed farming with a variety of crops and farm animals, and minimising the use of chemical fertilisers, herbicides and pesticides

A business planning framework for WiMAX applications

Mobile networking refers to wireless technologies which provide communications between devices. Applications for mobile networking have a broad scope as they can be applied to many situations in either industrial or commercial sectors. The challenge for firms is to better match market-induced variability to the organizational issues and systems necessary for technological innovation. This chapter develops a business planning framework for mobile networking applications. This framework recognises the fluidity of the situation when trying to anticipate and model emerging wireless applications. The business planning framework outlined in this chapter is a generic model which can be used by companies to assess the business case for applications utilizing mobile networking technologies

Delphi Austria - An Example of Tailoring Foresight to the Needs of a Small Country

The world-wide diffusion and recognition of Technology Foresight suggests that it is of value for quite diverse types of economies and societies. Its merit as an important tool of strategic intelligence for policy-making also in small countries and transition economies depends on a careful tailoring to specific needs. Practice of Foresight is rather diverse also among small countries, but approaches tend to be more selective in scope, have more specific goals, and put greater emphasis on demand aspects than in bigger countries. Austria’s first systematic Foresight programme (completed in 1998) is an example of an innovative approach adapted to the needs of a small country. This contribution shows how Delphi Austria was tailored to a small economy which had undergone a successful catch-up process and how the Foresight process as well as its results have been utilised.Technology Foresight, Delphi method, small country, Austria, innovation, technology policy, implementation

Agricultural climate change mitigation : Carbon calculators as a guide for decision making

This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Agricultural Sustainability on 9 November 2017, available online: https://doi.org/10.1080/14735903.2017.1398628. Under embargo. Embargo end date: 9 November 2018.The dairy industry is receiving considerable attention in relation to both its significant greenhouse gas (GHG) emissions, and it’s potential for reducing those emissions, contributing towards meeting national targets and driving the industry towards sustainable intensification. However, the extent to which improvements can be made is dependent on the decision making processes of individual producers, so there has been a proliferation of carbon accounting tools seeking to influence those processes. This paper evaluates the suitability of such tools for driving environmental change by influencing on-farm management decisions. Seven tools suitable for the European dairy industry were identified, their characteristics evaluated, and used to process data relating to six scenario farms, emulating process undertaken in real farm management situations. As a result of the range of approaches taken by the tools, there was limited agreement between them as to GHG emissions magnitude, and no consistent pattern as to which tools resulted in the highest/lowest results. Despite this it is argued, that as there was agreement as to the farm activities responsible for the greatest emissions, the more complex tools were still capable of performing a ‘decision support’ role, and guiding management decisions, whilst others could merely focus attention on key issues.Peer reviewe

'Smart' design: greening the Total Product System

About the book: Since the Rio summit in 1992, the paradigm of corporate environmental responsibility has gradually and consistently extended beyond complying with increasingly stringent environmental regulation and taking up the proactive initiatives of a few world-class companies. Research indicates that the business and financial performance of companies may depend directly on socially and environmentally responsible business practices. Many world-class companies now realize that customers and other stakeholders do not distinguish between a company and its suppliers. As a result, greening the supply chain is an innovative idea which is fast gaining attention in the industry. Greening the Supply Chain is a compilation of important chapters written by a diverse set of international authors which incorporates a broad variety of perspectives. Note: Smart car refers to Smart City coupe and Fortwo, and all terms are registered trademarks of MCC (micro compact car)

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

Endogenous learning for green hydrogen in a sector-coupled energy model for Europe

Many studies have shown that hydrogen could play a large role in the energy transition for hard-to-electrify sectors, but previous modelling has not included the necessary features to assess its role. They have either left out important sectors of hydrogen demand, ignored the temporal variability in the system or neglected the dynamics of learning effects. We address these limitations and consider learning-by-doing for the full green hydrogen production chain with different climate targets in a detailed European sector-coupled model. Here, we show that in the next 10 years a faster scale-up of electrolysis and renewable capacities than envisaged by the EU in the REPowerEU Plan can be cost-optimal to reach the strictest +1.5oC target. This reduces the costs for hydrogen production to 1.26 €/kg by 2050. Hydrogen production switches from grey to green hydrogen, omitting the option of blue hydrogen. If electrolysis costs are modelled without dynamic learning-by-doing, then the electrolysis scale-up is significantly delayed, while total system costs are overestimated by up to 13% and the levelised cost of hydrogen is overestimated by 67%