Knowledge Management Portal

heterogeneity that is associated with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote sensing can be an effective tool for characterizing and monitoring forest vegetation distribution within these fragmented Mediterranean landscapes. The heterogeneity of Mediterranean vegetation, however, often exceeds the resolution typical of most satellite sensors. Hyper-spectral remote sensing technology demonstrates the capacity for accurate vegetation identification. The objective of this research is to determine to what extent forest types can be discriminated using different image analysis techniques and spectral band combinations of Hyperion satellite imagery. This research mapped forest types using a pixel-based Spectral Angle Mapper (SAM), nearest neighbour and membership function classifiers of the objectoriented classification. Hyperion classification was done after reducing Hyperion data using nine selected band combinations. Results indicate that the selection of band combination while reducing the Hyperion dataset improves classification results for both the overall and the individual forest type accuracy, in particular for the selected optimum Hyperion band combination. One shortcoming is that the performance of the best selected band combination was superior in terms of both overall and individual forest type accuracy when applying the membership classifier of the object-oriented method compared to SAM and nearest neighbour classifiers. However, all techniques seemed to suffer from a number of problems, such as spectral similarity among forest types, overall low energy response of the Hyperion sensor, Hyperion medium spatial resolution and spatiotemporal and spectral heterogeneity of the Mediterranean ecosystem at multiple scales.

Digital elevation models (DEM) can be used to derive a wealth of information about land surface morphology. When analyzed using GIS information about stream channels and watersheds it can determine the flow of water over the Earth’s surface. Manipulation, analysis, and graphic presentation of the flooding risk and hazard data can be done within a GIS system, and their spatial interrelationships can be determined and used for computer based risk assessment models. We used panchromatic 10m SPOT and 30m Landsat 7 images to digitize stream channels, dry washes, and salt flats in the central part of Tunisia. We then employed a DEM with a resolution of 90m to determine watersheds and theoretical surface water flow patterns. Watersheds were determined using a minimum surface area of 65km2. We analyzed the DEM by removing pits then determining cells that received runoff assuming an impervious surface. The areas receiving runoff were then classified into cells receiving high, moderate, and low amounts of runoff precipitation. Areas receiving high amounts were considered “at risk” for flooding. Generated maps can be used by government planners and insurance companies to identify areas prone to flooding and encourage citizens to avoid building on these high risk sites.

The current study aims to examine the hypothesis that the determination of the emergence rate is a possible indicator of the plant potential to tolerate soil salinity under field conditions. This hypothesis is analysed using specific experiments on salt-sensitive (chickpea and broad bean) and salt-tolerant (durum wheat and barley) species. For each of the four crops, two varieties that displayed a clear difference in yield production under a saline environment were subjected to two types of analysis. The first type of analysis was used to classify the salt-tolerance of different varieties using two criteria as follows: (1) the relationship between yield and soil salinity; (2) the water use efficiency. The second type of analysis determined the emergence rate as a function of the irrigation-water salinity. The criteria that were used in the current study for classifying the varietal salt-tolerance of each crop provided similar conclusions. However, the hypothesis that the emergence rate was associated with the varietal potential for salt-tolerance was not confirmed by the results of this analysis. On the contrary, the results demonstrated that some varieties combined poor emergence with a high crop salt-tolerance. These varieties included ILC 3279 of the chickpea and Cham-1 of the durum wheat. The current study does not support the hypothesis that postulated the existence of a correlation between the emergence rate and the potential of a variety to tolerate the soil salinity. Finally, practical applications of these conclusions are discussed. (C) 2011 Elsevier B.V. All rights reserved.

Until now three publications have reported the development of transgenic lentil plants through protocol optimisation using the gusA gene, but there are no reports of the introduction of a gene with agronomic importance. In the present study we report the introduction of the DREB1A gene into lentil to enhance drought and salinity tolerance. Decapitated embryos were immersed in Agrobacterium suspension and then co-cultivated for 4 days. Direct organogenesis was induced from the apical meristems and cotyledonary buds. Subsequently, the explants were subjected to selection in medium containing 10 mg/L phosphinothricin for nine rounds with 2-week intervals. The putative transgenic explants were micro-grafted onto non-transformed rootstocks to establish transgenic plants. The PCR results confirmed the insertion and stable inheritance of the gene of interest and bar marker gene in the plant genome. The Southern blot analysis revealed the integration of a single copy of the transgenes. T-0 plants and progeny up to T-2 generations showed complete resistance to the herbicide Basta. The DREB1A gene driven by the rd29A promoter was induced in transgenic plants by salt stress from sodium chloride solution. The total RNA was extracted and cDNA synthesised. The results showed that DREB1A mRNA was accumulated and thus the DREB1A transgene was expressed in the transgenic plants, whereas no expression was detected in the non-transformed parents.

The response of faba bean (Vicia faba L, variety ILB1814) was evaluated in a factorial salinity-drought experiment, combining three levels of salinity in the irrigation water (EC 1.0, 2.3 and 3.6 dS/m) and two levels of plant water status during two successive cropping seasons. The two levels of plant water status were obtained by supplying irrigation when the pre-dawn leaf water potential of the control treatments attained values of -0.3 and -0.6 MPa. The experiment was designed to analyse the effects of soil salinity, the effects of drought (detected by the level of the plant water status), and the combined effect of salinity and drought on the plant-water relationships, nitrogen balance and crop productivity (for both grain and straw). Soil salinity levels equal toot higher than 6.5 dS m(-1) affected the plants by reducing the grain number but not the straw weight. Drought at flowering, early podding and grain-filling stages reduced both grain and straw yields. Moreover, yield reductions were associated with increasing soil salinity levels, confirming an interaction between the salinity and drought effects on faba bean productivity. Symbiotic nitrogen fixation, as evaluated by the nitrogen balance, was more affected by drought than by salinity, and it may explain the absence of any observed effects of salinity under drought conditions. The comparison of these results with those obtained in similar experiments on wheat and barley revealed that these cereals and faba bean have contrasting behaviours under saline-drought conditions. (C) 2011 Elsevier B.V. All rights reserved.

Bio-economic models can be used to assess the impact of policy and environmental measures through economic and environmental indicators. Focusing on agricultural systems, farmers’ decisions in terms of cropping systems and the associated crop management at field scale are essential in such studies. The objective of this paper is to present a study using a bio-economic model to assess the impact of the Nitrate Directive in the Midi-Pyrenees region (France) by analyzing, at the farm scale, farm income and three environmental indicators: nitrate leaching, erosion and water consumption. Two scenarios, the 2003 CAP reform (baseline scenario) and the Nitrate Directive (policy scenario), with a 2013 time horizon, were developed and compared for three representative arable farm types in the Midi-Pyrenees region. Different types of data characterizing the biophysical context in the region (soil, climate), the current cropping systems (rotation, crop management) and farm resources (irrigated land, labor) were collected to calibrate and run the models. Results showed that the implementation of the Nitrate Directive may not affect farm income. However, significant modifications to cropping systems and crop allocation to soil types were simulated. Contrary to expectations, nitrogen leaching at the farm scale did not change. Overall water consumption increased and soil erosion decreased due mainly to a modification in cropping patterns and management by soil type. This study provides an example of unanticipated effects of policy and trade-offs between environmental issues.

This issue of Caravan describes some of ICARDA's work on climate change. It contains overviews of the key issues, and articles describing research in specific areas. We examine, for example, how genetic resources – landrace varieties, wild relatives, indigenous livestock breeds – can be used to make small-scale agriculture better adapted to a changed climate. We discuss the implications of climate change on the evolution and spread of plant pathogens, on policy options for improving rural livelihoods, and on centuries-old patterns of livestock migration. We describe low-cost technologies to improve water management in the face of growing scarcity. We illustrate how remote sensing and geographic information systems can help identify climate change 'hot spots' and analyze long-term changes in the environment. We discuss soil carbon and carbon sequestration in the context of developing-country agriculture. We identify lessons learnt in dry areas in West Africa, that could be put to use elsewhere.

Agricultural systems continuously evolve and are forced to change as a result of a range of global and local driving forces. Agricultural technologies and agricultural, environmental and rural development policies are increasingly designed to contribute to the sustainability of agricultural systems and to enhance contributions of agricultural systems to sustainable development at large. The effectiveness and efficiency of such policies and technological developments in realizing desired contributions could be greatly enhanced if the quality of their ex-ante assessments were improved. Four key challenges and requirements to make research tools more useful for integrated assessment in the European Union were defined in interactions between scientists and the European Commission (EC), i.e., overcoming the gap between micro–macro level analysis, the bias in integrated assessments towards either economic or environmental issues, the poor re-use of models and hindrances in technical linkage of models. Tools for integrated assessment must have multi-scale capabilities and preferably be generic and flexible such that they can deal with a broad variety of policy questions. At the same time, to be useful for scientists, the framework must facilitate state-of-the-art science both on aspects of the agricultural systems and on integration. This paper presents the rationale, design and illustration of a component-based framework for agricultural systems (SEAMLESS Integrated Framework) to assess, ex-ante, agricultural and agri-environmental policies and technologies across a range of scales, from field–farm to region and European Union, as well as some global interactions. We have opted for a framework to link individual model and data components and a software infrastructure that allows a flexible (re-)use and linkage of components. The paper outlines the software infrastructure, indicators and model and data components. The illustrative example assesses effects of a trade liberalisation proposal on EU’s agriculture and indicates how SEAMLESS addresses the four identified challenges for integrated assessment tools, i.e., linking micro and macro analysis, assessing economic, environmental, social and institutional indicators, (re-)using standalone model components for field, farm and market analysis and their conceptual and technical linkage.