Innovative approaches For enhancing the flow of funds into the Benefit Sharing Fund of the International Treaty on Plant Genetic Resources for Food and Agriculture: an evaluation of options

This report assesses the implications and revenue-generating potential of options for reform of the International Treaty on Plant Genetic Resources for Food and Agriculture in the context of the structure of the global seed industry and the emerging landscape of plant variety innovation for different crops. The implementation of these options would require modifications of Treaty and provisions of the Standard Material Transfer Agreements to alter the nature of payment obligations related to different categories of products, the payment rates under different options and the coverage of crops in Annex-I to the Treaty.

Real-time factor model forecasting and the effects of instability

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

Information content and forecasting ability of sentiment indicators: case of real estate market

We evaluate a number of real estate sentiment indices to ascertain current and forward-looking information content that may be useful for forecasting demand and supply activities. Analyzing the dynamic relationships within a Vector Auto-Regression (VAR) framework and using the quarterly US data over 1988-2010, we test the efficacy of several sentiment measures by comparing them with other coincident economic indicators. Overall, our analysis suggests that the sentiment in real estate convey valuable information that can help predict changes in real estate returns. These findings have important implications for investment decisions, from consumers' as well as institutional investors' perspectives.

Satellite-supported flood forecasting in river networks: a real case study

Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of stand-alone EO-based operational flood forecasting.

An evidence-based review on the likely economic and environmental impact of genetically modified cereals and oilseeds for UK agriculture

An evidence-based review of the potential impact that the introduction of genetically-modified (GM) cereal and oilseed crops could have for the UK was carried out. The inter-disciplinary research project addressed the key research questions using scenarios for the uptake, or not, of GM technologies. This was followed by an extensive literature review, stakeholder consultation and financial modelling. The world area of canola, oilseed rape (OSR) low in both erucic acid in the oil and glucosinolates in the meal, was 34M ha in 2012 of which 27% was GM; Canada is the lead producer but it is also grown in the USA, Australia and Chile. Farm level effects of adopting GM OSR include: lower production costs; higher yields and profits; and ease of farm management. Growing GM OSR instead of conventional OSR reduces both herbicide usage and environmental impact. Some 170M ha of maize was grown in the world in 2011 of which 28% was GM; the main producers are the USA, China and Brazil. Spain is the main EU producer of GM maize although it is also grown widely in Portugal. Insect resistant (IR) and herbicide tolerant (HT) are the GM maize traits currently available commercially. Farm level benefits of adopting GM maize are lower costs of production through reduced use of pesticides and higher profits. GM maize adoption results in less pesticide usage than on conventional counterpart crops leading to less residues in food and animal feed and allowing increasing diversity of bees and other pollinators. In the EU, well-tried coexistence measures for growing GM crops in the proximity of conventional crops have avoided gene flow issues. Scientific evidence so far seems to indicate that there has been no environmental damage from growing GM crops. They may possibly even be beneficial to the environment as they result in less pesticides and herbicides being applied and improved carbon sequestration from less tillage. A review of work on GM cereals relevant for the UK found input trait work on: herbicide and pathogen tolerance; abiotic stress such as from drought or salinity; and yield traits under different field conditions. For output traits, work has mainly focussed on modifying the nutritional components of cereals and in connection with various enzymes, diagnostics and vaccines. Scrutiny of applications submitted for field trial testing of GM cereals found around 9000 applications in the USA, 15 in Australia and 10 in the EU since 1996. There have also been many patent applications and granted patents for GM cereals in the USA for both input and output traits;an indication of the scale of such work is the fact that in a 6 week period in the spring of 2013, 12 patents were granted relating to GM cereals. A dynamic financial model has enabled us to better understand and examine the likely performance of Bt maize and HT OSR for the south of the UK, if cultivation is permitted in the future. It was found that for continuous growing of Bt maize and HT OSR, unless there was pest pressure for the former and weed pressure for the latter, the seed premia and likely coexistence costs for a buffer zone between other crops would reduce the financial returns for the GM crops compared with their conventional counterparts. When modelling HT OSR in a four crop rotation, it was found that gross margins increased significantly at the higher levels of such pest or weed pressure, particularly for farm businesses with larger fields where coexistence costs would be scaled down. The impact of the supply of UK-produced GM crops on the wider supply chain was examined through an extensive literature review and widespread stakeholder consultation with the feed supply chain. The animal feed sector would benefit from cheaper supplies of raw materials if GM crops were grown and, in the future, they might also benefit from crops with enhanced nutritional profile (such as having higher protein levels) becoming available. This would also be beneficial to livestock producers enabling lower production costs and higher margins. Whilst coexistence measures would result in increased costs, it is unlikely that these would cause substantial changes in the feed chain structure. Retailers were not concerned about a future increase in the amount of animal feed coming from GM crops. To conclude, we (the project team) feel that the adoption of currently available and appropriate GM crops in the UK in the years ahead would benefit farmers, consumers and the feed chain without causing environmental damage. Furthermore, unless British farmers are allowed to grow GM crops in the future, the competitiveness of farming in the UK is likely to decline relative to that globally.

Electronic commerce in agriculture and agribusiness: the case of Emilia-Romagna (Italy )

Despite the expectations of the benefits of this tool, the adoption of Electronic Commerce (EC) by small and medium firms of the agro-food sector in Italy is still not frequent, however, the understanding of opportunities it could create and how they can be exploited remains a relevant issue. This study, carried out in the Emilia -Romagna region during 2002, illustrates the results of a survey of 208 firms at all stages of the agro-food chain aimed at understanding the use of the Internet and the strategies adopted for EC implementation. The results show a low level of implementation of the instrument and a limited variety of adoption strategies. Agro-food firms actually invest very little in EC focusing their efforts on the Internet as promotion tool, while web-based direct selling is confined to market niches. The view that the Internet would reverse the disadvantages of small firms appears by now non realistic, even if interesting opportunities for further development are still present.

Researching farmer behaviour in climate change adaptation and sustainable agriculture: lessons learned from five case studies

Understanding farmer behaviour is needed for local agricultural systems to produce food sustainably while facing multiple pressures. We synthesize existing literature to identify three fundamental questions that correspond to three distinct areas of knowledge necessary to understand farmer behaviour: 1) decision-making model; 2) cross-scale and cross-level pressures; and 3) temporal dynamics. We use this framework to compare five interdisciplinary case studies of agricultural systems in distinct geographical contexts across the globe. We find that these three areas of knowledge are important to understanding farmer behaviour, and can be used to guide the interdisciplinary design and interpretation of studies in the future. Most importantly, we find that these three areas need to be addressed simultaneously in order to understand farmer behaviour. We also identify three methodological challenges hindering this understanding: the suitability of theoretical frameworks, the trade-offs among methods and the limited timeframe of typical research projects. We propose that a triangulation research strategy that makes use of mixed methods, or collaborations between researchers across mixed disciplines, can be used to successfully address all three areas simultaneously and show how this has been achieved in the case studies. The framework facilitates interdisciplinary research on farmer behaviour by opening up spaces of structured dialogue on assumptions, research questions and methods employed in investigation.

Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models

The Monte Carlo Independent Column Approximation (McICA) is a flexible method for representing subgrid-scale cloud inhomogeneity in radiative transfer schemes. It does, however, introduce conditional random errors but these have been shown to have little effect on climate simulations, where spatial and temporal scales of interest are large enough for effects of noise to be averaged out. This article considers the effect of McICA noise on a numerical weather prediction (NWP) model, where the time and spatial scales of interest are much closer to those at which the errors manifest themselves; this, as we show, means that noise is more significant. We suggest methods for efficiently reducing the magnitude of McICA noise and test these methods in a global NWP version of the UK Met Office Unified Model (MetUM). The resultant errors are put into context by comparison with errors due to the widely used assumption of maximum-random-overlap of plane-parallel homogeneous cloud. For a simple implementation of the McICA scheme, forecasts of near-surface temperature are found to be worse than those obtained using the plane-parallel, maximum-random-overlap representation of clouds. However, by applying the methods suggested in this article, we can reduce noise enough to give forecasts of near-surface temperature that are an improvement on the plane-parallel maximum-random-overlap forecasts. We conclude that the McICA scheme can be used to improve the representation of clouds in NWP models, with the provision that the associated noise is sufficiently small.

Adaptive social protection: mapping the evidence and policy context in the agriculture sector in south Asia

An aim of government and the international community is to respond to global processes and crises through a range of policy and practical approaches that help limit damage from shocks and stresses. Three approaches to vulnerability reduction that have become particularly prominent in recent years are social protection (SP), disaster risk reduction (DRR) and climate change adaptation (CCA). Although these approaches have much in common, they have developed separately over the last two decades. However, given the increasingly complex and interlinked array of risks that poor and vulnerable people face, it is likely that they will not be sufficient in the long run if they continue to be applied in isolation from one another. In recognition of this challenge, the concept of Adaptive Social Protection (ASP) has been developed. ASP refers to a series of measures which aims to build resilience of the poorest and most vulnerable people to climate change by combining elements of SP, DRR and CCA in programmes and projects. The aim of this paper is to provide an initial assessment of the ways in which these elements are being brought together in development policy and practice. It does this by conducting a meta-analysis of 124 agricultural programmes implemented in five countries in south Asia. These are Afghanistan, Bangladesh, India, Nepal and Pakistan. The findings show that full integration of SP, DRR and CCA is relatively limited in south Asia, although there has been significant progress in combining SP and DRR in the last ten years. Projects that combine elements of SP, DRR and CCA tend to emphasise broad poverty and vulnerability reduction goals relative to those that do not. Such approaches can provide valuable lessons and insights for the promotion of climate resilient livelihoods amongst policymakers and practitioners.

The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images

A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant.

Intensive agriculture reduces soil biodiversity across Europe

Soil biodiversity plays a key role in regulating the processes that underpin the delivery of ecosystem goods and services in terrestrial ecosystems. Agricultural intensification is known to change the diversity of individual groups of soil biota, but less is known about how intensification affects biodiversity of the soil food web as a whole, and whether or not these effects may be generalized across regions. We examined biodiversity in soil food webs from grasslands, extensive, and intensive rotations in four agricultural regions across Europe: in Sweden, the UK, the Czech Republic and Greece. Effects of land-use intensity were quantified based on structure and diversity among functional groups in the soil food web, as well as on community-weighted mean body mass of soil fauna. We also elucidate land-use intensity effects on diversity of taxonomic units within taxonomic groups of soil fauna. We found that between regions soil food web diversity measures were variable, but that increasing land-use intensity caused highly consistent responses. In particular, land-use intensification reduced the complexity in the soil food webs, as well as the community-weighted mean body mass of soil fauna. In all regions across Europe, species richness of earthworms, Collembolans, and oribatid mites was negatively affected by increased land-use intensity. The taxonomic distinctness, which is a measure of taxonomic relatedness of species in a community that is independent of species richness, was also reduced by land-use intensification. We conclude that intensive agriculture reduces soil biodiversity, making soil food webs less diverse and composed of smaller bodied organisms. Land-use intensification results in fewer functional groups of soil biota with fewer and taxonomically more closely related species. We discuss how these changes in soil biodiversity due to land-use intensification may threaten the functioning of soil in agricultural production systems.

Improving the health forecasting alert system for cold weather and heat-waves in England: a proof-of-concept using temperature-mortality relationships

Objectives In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. Method The prototype health forecasting alert system introduces an “impact vs likelihood matrix” for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. Conclusions The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.

Knowledge and perception of Ghanaian cocoa farmers on mirid control and their willingness to use forecasting systems

Annual losses of cocoa in Ghana to mirids are significant. Therefore, accurate timing of insecticide application is critical to enhance yields. However, cocoa farmers often lack information on the expected mirid population for each season to enable them to optimise pesticide use. This study assessed farmers’ knowledge and perceptions of mirid control and their willingness to use forecasting systems informing them of expected mirid peaks and time of application of pesticides. A total of 280 farmers were interviewed in the Eastern and Ashanti regions of Ghana with a structured open and closed ended questionnaire. Most farmers (87%) considered mirids as the most important insect pest on cocoa with 47% of them attributing 30-40% annual crop loss to mirid damage. There was wide variation in the timing of insecticide application as a result of farmers using different sources of information to guide the start of application. The majority of farmers (56%) do not have access to information on the type, frequency and timing of insecticides to use. However, respondents who are members of farmer groups had better access to such information. Extension officers were the preferred channel for information transfer to farmers with 72% of farmers preferring them to other available methods of communication. Almost all the respondents (99%) saw the need for a comprehensive forecasting system to help farmers manage cocoa mirids. The importance of accurate timing for mirid control based on forecasted information to farmer groups and extension officers was discussed.