Since 1990, the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed database of the ca. 2,100 cited experiments and simulations in the five ARs was analyzed with respect to impacts on yields via crop type, region, and whether adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline, but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom‐up and top‐down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and nonproduction aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical, and imminent issues in climate change, have been dealt with an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of not only agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modeling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and nonproduction aspects of food security in future roles for integrated assessment models.
Soil and its ecosystem functions play a societal role in securing sustainable food production while safeguarding natural resources. A functional land management framework has been proposed to optimize the agro-environmental outputs from the land and specifically the supply and demand of soil functions such as (a) primary productivity, (b) carbon sequestration, (c) water purification and regulation, (d) biodiversity and (e) nutrient cycling, for which soil knowledge is essential. From the outset, the LANDMARK multi-actor research project integrates harvested knowledge from local, national and European stakeholders to develop such guidelines, creating a sense of ownership, trust and reciprocity of the outcomes. About 470 stakeholders from five European countries participated in 32 structured workshops covering multiple land uses in six climatic zones. The harmonized results include stakeholders' priorities and concerns, perceptions on soil quality and functions, implementation of tools, management techniques, indicators and monitoring, activities and policies, knowledge gaps and ideas. Multi-criteria decision analysis was used for data analysis. Two qualitative models were developed using Decision EXpert methodology to evaluate "knowledge" and "needs". Soil quality perceptions differed across workshops, depending on the stakeholder level and regionally established terminologies. Stakeholders had good inherent knowledge about soil functioning, but several gaps were identified. In terms of critical requirements, stakeholders defined high technical, activity and policy needs in (a) financial incentives, (b) credible information on improving more sustainable management practices, (c) locally relevant advice, (d) farmers' discussion groups, (e) training programmes, (f) funding for applied research and monitoring, and (g) strengthening soil science in education. K E Y W O R D S DEX model, farmers and multi-stakeholders, locally relevant advice, participatory research, soil quality This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Procedures are presented for computerised image analysis of biocrystallogram images, originating from biocrystallization investigations of agricultural products. The biocrystallization method is based on the crystallographic phenomenon that when adding biological substances, such as plant extracts, to aqueous solutions of dihydrate CuCl 2 , biocrystallograms with reproducible dendritic crystal structures are formed during crystallisation. The morphological features found in the structures are traditionally applied for visual ranking or classification, e.g. in comparative studies of the effects of farming systems on crop quality. The circular structures contain predominantly a single centre from where ramifications expand in a zonal structure. In previous studies primarily texture analysis was applied, and the images analysed and classified by means of a circular region-of-interest (ROI), i.e. the region specified for analysis. In the present study the objective was to examine how the discriminative information relevant for classification purposes is distributed over the zonal structure, and how the information is affected by the varying location of the crystallisation centre. The texture analysis procedures were applied to a so-called degradation series of 33 images, including seven groups representing discrete 'treatment levels'. The biocrystallograms were produced over seven consecutive days, on the basis of a single carrot extract degrading while stored at 6°C. This degradation is known to induce systematic changes in morpholog-* Corresponding author. Tel.: +45-35-283520; fax: +45-35-282175; e-mail: joa@kvl.dk. (1999) 51-69 52 ical features over a number of successive days. The biocrystallograms were scanned at 600 dpi, with 256 grey levels. Eight first-order statistical parameters were calculated for four resolution scales, and 15 second-order parameters for five scales, giving a total of 107 observations for each image. Classification of an individual image was performed by means of stepwise discriminant analysis. Four main types, and several subtypes and sizes of ROI were examined. The 33 images as well as a subset of 21 images were examined. When imposing a restriction on the centre location in the subset, thereby reducing the within-group variance, the scores were markedly improved. Classifications of the total set and the subset showed scores up to 84.8 and 100%, respectively. A number of parameters showed a monotonic relationship with degradation day number. Multiple linear regressions based on up to eight parameters indicated strong relationships, with R 2 up to 0.98. It is concluded that the procedures were able to discriminate the seven groups of images, and are applicable for biocrystallization investigations of agricultural products. Perspectives for the application of image analysis are briefly mentioned.
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