INFLUENCE OF DRYING AND GENEBANK STORAGE ON INITIAL GROWTH, PEROXIDASE ACTIVITY AND REGENERATION CAPACITY OF WHEAT SEEDS

Изследвани са семена от три сорта обикновена пшеница: Садово1, Садово772 и Катя и три сорта твърда пшеница: Прогрес, Възход и Белослава. Образците са поставени за обезводняване до ниско водно съдържание (водна активност aw = 0,116) чрез сорбционно сушене. Установени са несъществени промени в началния растеж на пониците. При изследване на пероксидазните изоензимни спектри от поници не са установени съществени изменения в спектъра на пероксидазите като резултат от сушене до ниско влагосъдържание и последвало съхранение при -18оС. Няма доказана тенденция за отрицателно влияние върху репродуктивната способност на пшеничените семена претърпели сушене до нисък предел (водна активност aw = 0,116) и студено съхранение (-18оС).Seeds of three winter wheat bread cultivars (Triticum aestivum L.): Sadovo 1, Sadovo 772 and Katya, and three durum wheat cultivars (Triticum durum Desf): Progress, Vazhod and Beloslava were used. The accessions have been submitted to sorbtion drying to low moisture content (water activity aw = 0,116). No essential changes were observed in the initial seedling growth. In evaluation of peroxidase isozyme spectra of seedlings no significant changes were detected as a result of sorption drying to low seed moisture and subsequent storage at -18оС. Field evaluation of the main structural plant yield elements uncovered no statistically significant unfavorable effects on the regeneration capacity of wheat seeds as a result of drying (water activity aw = 0,116) and cool storage (-18oC)

Betriebliche Bewirtschaftungsindikatoren für Biodiversität im Ökologischen Landbau und in extensiven Anbausystemen in Europa

Farming practices are the key to maintaining and restoring farmland biodiversity. Selected farm management indicators, regarded as scientifically sound, practicable and attractive to stakeholders, were tested against species indicators in various farm types in 12 case studies across Europe. A set of eight farm management indicators is recommended, reflecting the pressure on biodiversity by farm management via energy and nutrient input, mechanical operations, pesticide use and livestock

Indicators for the on-farm assessment of crop cultivar and livestock breed diversity: a survey-based participatory approach

International audienceAgrobiodiversity plays a fundamental role in guaranteeing food security. However, still little is known about the diversity within crop and livestock species: the genetic diversity. In this paper we present a set of indicators of crop accession and breed diversity for different farm types at farm-level, which may potentially supply a useful tool to assess and monitor farming system agrobiodiversity in a feasible and relatively affordable way. A generic questionnaire was developed to capture the information on crops and livestock in 12 European case study regions and in Uganda by 203 on-farm interviews. Through a participatory approach, which involved a number of stakeholders, eight potential indicators were selected and tested. Five of them are recommended as potentially useful indicators for agrobiodiversity monitoring per farm: (1) crop-species richness (up to 16 crop species), (2) crop-cultivar diversity (up to 15 crop cultivars, 1-2 on average), (3) type of crop accessions (landraces accounted for 3 % of all crop cultivars in Europe, 31 % in Uganda), (4) livestock-species diversity (up to 5 livestock species), and (5) breed diversity (up to five cattle and eight sheep breeds, on average 1-2).We demonstrated that the selected indicators are able to detect differences between farms, regions and dominant farm types. Given the present rate of agrobiodiversity loss and the dramatic effects that this may have on food production and food security, extensive monitoring is urgent. A consistent survey of crop cultivars and livestock breeds on-farm will detect losses and help to improve strategies for the management and conservation of on-farm genetic resources

Indikatoren zur Erfassung genetischer Vielfalt in biologischen und nicht-biologischen Landwirtschaftssystemen

Genetic variability is the fundament of life. Large genetic variability within species is the basis for adaptation to changing environmental conditions. Farmers and breeders have developed a multitude of crop cultivars and animal breeds to stabilize and increase quality and productivity. This study evaluated genetic diversity within different organic and non-organic farming systems using crop-cultivar and livestock-breed information as simple indicators. Data was collected using on-farm surveys in 15 case study regions in Europe and beyond. Selected indicators revealed strong differences of cultivar diversity between different countries and farming systems across Europe. No or only small differences were detectable between organic and non-organic farming systems. Landraces, as on-farm genetic resources, were under-represented in European case study regions

BIOBIO – Indikatoren für Biodiversität in ökologischen und ex-tensiven Anbausystemen

Organic and low-input farming systems provide habitats for wildlife on farmland. The EU FP7 project BIOBIO has identified a core set of 23 indicators relating to the diversity of habitats, of species, of crops and of livestock. Management indicators capturing the pressure on biodiversity are also proposed. The indicators were identified in an iterative process between scientists and stake-holders to make sure that they are not only scientifically sound but also practicable and attractive. They were tested in 12 case study regions on four major farm types. Allocating 0.25 % of the CAP budget to a farm scale biodiversity monitoring would allow to measure and analyse the indicators on 50,000 farms across Europe

How much would it cost to monitor farmland biodiversity in Europe?

International audienceTo evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets. Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled. Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy (CAP) budget (2014-2020) to determine the budgetallocation required for the proposed farmland biodiversity monitoring. Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 001%-074% of the total CAP budget and to 004%-248% of the CAP budget specifically allocated to environmental targets.Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts. Editor's Choic

Indicateurs de biodiversité dans les exploitations agricoles biologiques et conventionnelles des Vallées et Coteaux de Gascogne, cas d’étude français du projet européen BIOBIO

Dans le cadre du projet européen BIOBIO, nous avons comparé entre pays les richesses en habitats et richesses spécifiques cumulées de quatre groupes taxonomiques (plantes, abeilles sauvages, araignées, vers de terre), de 169 exploitations biologiques ou conventionnelles appartenant à 10 pays. Pour le cas d’étude français, Vallées et Coteaux de Gascogne, les corrélations entre indicateurs directs (richesses spécifiques des taxons et habitats) et indirects (pratiques agricoles) de biodiversité, relevés dans 8 exploitations conventionnelles et 8 biologiques, ont été recherchées. Les résultats montrent que le nombre d’habitats cultivés et surtout semi-naturels par exploitation est le principal facteur déterminant le niveau de biodiversité à cette échelle, pour le cas d’étude français comme pour les autres cas d’étude. Ce facteur explique en partie le plus haut niveau de biodiversité observé pour le cas d’étude français. Néanmoins, les pratiques, spécifiques ou non des modes de production biologique et conventionnelle, gouvernent généralement les paramètres de biodiversité à l’échelle de l’habitat. In fine, le projet propose la méthode BIOBIO de suivi de la biodiversité dans les exploitations agricoles

Farmland biodiversity and agricultural management on 237 farms in 13 European and two African regions

Farmland is a major land cover type in Europe and Africa and provides habitat for numerous species. The severe decline in farmland biodiversity of the last decades has been attributed to changes in farming practices, and organic and low-input farming are assumed to mitigate detrimental effects of agricultural intensification on biodiversity. Since the farm enterprise is the primary unit of agricultural decision making, management-related effects at the field scale need to be assessed at the farm level. Therefore, in this study, data were collected on habitat characteristics, vascular plant, earthworm, spider, and bee communities and on the corresponding agricultural management in 237 farms in 13 European and two African regions. In 15 environmental and agricultural homogeneous regions, 6–20 farms with the same farm type (e.g., arable crops, grassland, or specific permanent crops) were selected. If available, an equal number of organic and non-organic farms were randomly selected. Alternatively, farms were sampled along a gradient of management intensity. For all selected farms, the entire farmed area was mapped, which resulted in total in the mapping of 11 338 units attributed to 194 standardized habitat types, provided together with additional descriptors. On each farm, one site per available habitat type was randomly selected for species diversity investigations. Species were sampled on 2115 sites and identified to the species level by expert taxonomists. Species lists and abundance estimates are provided for each site and sampling date (one date for plants and earthworms, three dates for spiders and bees). In addition, farmers provided information about their management practices in face-to-face interviews following a standardized questionnaire. Farm management indicators for each farm are available (e.g., nitrogen input, pesticide applications, or energy input). Analyses revealed a positive effect of unproductive areas and a negative effect of intensive management on biodiversity. Communities of the four taxonomic groups strongly differed in their response to habitat characteristics, agricultural management, and regional circumstances. The data has potential for further insights into interactions of farmland biodiversity and agricultural management at site, farm, and regional scale