Abstract
Collections of crop genetic resources are a valuable source of new genetic variation for economically important traits, including resistance to crop diseases. New sources of useful crop traits are often identified through evaluation in field trials. The number of relevant accessions in genebank collections available to be evaluated is often substantially larger than the capacity of the evaluation project. The focused identification of germplasm strategy (FIGS) is an approach used to select subsets of germplasm from genetic resource collections in such a way as to maximize the likelihood of capturing a specific trait. This strategy uses a range of methods to link the expression of a specific trait (of a target crop) with the eco-geographic parameters of the original collection site. This study contributes to the development of the approach by which a FIGS subset could be assembled for biotic traits. We have evaluated trait-specific subset selection methods for two fungal crop diseases, namely stem rust (Puccinia graminis Pers.) in wheat (Triticum aestivum L. and Triticum turgidum L.) and net blotch (Pyrenophora teres Drechs.) in barley (Hordeum vulgare L.).
The results indicate that the climate layers from freely available eco-geographic databases are well suited to model and predict the reaction in these crops to biotic stress traits. This result has the potential to improve the effi ciency of field screening trials to find novel sources of economically valuable crop traits.

Abstract
This study presents advances in resource-based poverty mapping. It illustrates how agricultural income distribution maps can be generated at small pixel-level, providing an application of the approach in rural Syria. Census data on agriculture and population are disaggregated based on pixel-level agricultural productivity coefficients derived in a GIS environment. The approach, triangulated with survey results and compared with sub-national poverty maps, shows that the better-income areas of Syria are located in the irrigated and higher-rainfall areas, though lower-income pockets exist due to the presence of ecological and topographic factors or due to high population density. The method can be used for developing high-resolution, low-cost maps for rapid detection of resource-driven poverty in low income countries where agriculture is a major source of rural income, and where poverty mapping is rarely undertaken due to the high costs involved.

Abstract
Remote sensing presents a valuable tool to address the highly complex issue of land degradation in dryland areas. Ten-daily composites of 8km-AVHRR reflectance data, for band 1 (0.58-0.68 μm) and band 2 (0.725-1.1 μm), were merged to form a complete coverage of North Africa, the Horn of Africa, West and Central Asia regions (the ‘CWANA’ region), and transformed into monthly NDVI composites. This temporal NDVI dataset was converted into a land use/land cover classification, using a hierarchical decision-tree, based on the average values of the mean and maximum NDVI. In a second stage the NDVI thresholds for different agroclimatic zones were adjusted to account for weather variability. Using these procedures 17 annual land cover maps were produced for each year of the period and further condensed into ‘change’ maps, using a procedure of identifying hierarchical levels of change patterns.
The analysis of the time series shows that most of the land in the CWANA region has remained stable during the period 1982-1999. Nevertheless, major changes in land cover occurred, especially in the Sahel, the Near East and in North Central Asia. Intensification of agriculture, mainly by the conversion of rainfed into irrigated croplands, retrenchment of agriculture and natural vegetation, which are potential indicators of land degradation, but also identification of natural vegetation, are the main trends throughout CWANA, with significant differences between sub-regions.

Abstract
This chapter presents four case studies conducted at regional (Central and West Asia and North Africa, or CWANA) and national (Syria) levels, focusing on irrigated area mapping from both producer and user perspectives. Together, these studies highlight the diversity of spatial applications in agricultural research that benefit from spatial information on irrigated areas.
The case studies first describe how maps of irrigated areas were derived using satellite imagery from either low-resolution or medium-resolution platforms. These mapping products were then utilized, in conjunction with other thematic datasets, in regional and national studies addressing biodiversity assessment, crop diversification and targeting, water management, and poverty research.
The first case study outlines the creation of a 1-km resolution land use/land cover (LULC) map based on 10-day composites of AVHRR-NDVI data spanning from April 1, 1992, to March 21, 1993. The map was developed using an empirically verified hierarchical decision tree. A detailed description of the CWANA LULC map and the methodology is available in Publication 61 (Celis et al., 2007).
The second case study discusses how the CWANA LULC map was integrated into an agroecological zones (AEZ) framework to produce an AEZ map for the CWANA region. Comprehensive details and the methodology for the CWANA AEZ map are provided in Publication 67 (De Pauw, 2007).
CWANA is characterized by a high proportion of marginal agricultural land, leading to significant ‘agricultural resource poverty.’ The third case study investigates the interplay between the quality of the natural resource base and human poverty in Syria using an index-based approach. This approach quantifies agricultural resource poverty and combines it with agricultural and income statistics to estimate agricultural income at the pixel level and identify poverty hotspots. The resulting disaggregated income distribution clearly reflects precipitation gradients and the distribution of irrigated land. A detailed methodology for this approach is presented in Publication 54b (Szőnyi et al., 2005).
The fourth case study explores opportunities for optimally using supplemental irrigation in combination with existing precipitation patterns to irrigate currently rainfed areas in Syria. The methodology combines a simple model to calculate potential water savings achieved by shifting from spring/summer fully irrigated crops to supplemental-irrigated winter/spring crops, with a water allocation procedure for nearby rainfed areas based on land suitability criteria. Full details and methodology for this approach are available in Publication 70 (De Pauw et al., 2008).

Abstract
This study focused on various approaches to rationalising the selection of test environments using on-farm trial data from 5 lentil (Lens culiniaris Medikus subsp. culinaris) genotypes. It was conducted over 3 years in 30 environments across 16 locations in Syria. There was maximum discrimination in the ratio of between-cluster to within-cluster variances, based on genotype yield responses to the environments. Four clusters represented the test locations, reflecting a gradient in the levels of yield and seasonal rainfall. We observed significant genotypic differences and genotype×environment interactions. Genotype×cluster interaction accounted for a substantial portion of the genotype×environment interaction.
This supported a reduction in the number of test locations to evaluate genotype and environment interaction. Temporal interactions were either low or insignificant. The improved lines produced stable and significantly higher yields than the local cultivar. The structure of the clusters formed indicated the presence of research stations in each cluster. We recommend that locations for future on-farm testing should include one research location and a farmer field in each cluster (or the mega-zone) so formed. Climatic variables or geographical nearness cannot replace the role of genotype response when rationalising test locations.

Abstract
Central Asia is often unjustly portrayed as a vast, barren, or sparsely vegetated plain, bordered to the south by snow-capped mountains and interspersed with large irrigated areas. In reality, the region is far more diverse. Central Asia represents a unique agroecosystem, distinguished by its specific combination of climate, terrain, soils, vegetation, and land use patterns that are unlike any found elsewhere. This complexity is further underscored by the region’s history of significant environmental catastrophes and land and water resource degradation, as well as its pronounced vulnerability to climate change.
This chapter synthesizes Central Asia’s agricultural environments by integrating insights from secondary sources and new map data. It goes beyond describing the region’s general physical geography, offering detailed overviews of its key ecosystems, climatic conditions—including precipitation, temperature, agroclimatic zones, and growing periods—soil types, vegetation, land use patterns, and agroecological zones.
The chapter concludes that the environmental factors influencing land use management vary significantly across the region. Climatic drivers, especially those affecting moisture availability and temperature stress, exhibit considerable spatial and temporal variation. Microclimatic conditions, particularly in mountainous areas, play a crucial role. To address the complexity of these agricultural environments, the use of GIS-based approaches is essential for defining agroecological zones.

Abstract
The Mediterranean region is distinguished by one of the world’s most variable climates, where drought is a persistent and defining feature. Agriculture, a critical and highly sensitive sector of the region’s economy, consumes the majority of available water resources, making water availability the primary constraint for agricultural production. Globally, the Mediterranean stands out not only for its distinctive physical environment but also for the ways its history and environment have influenced population growth, settlement patterns, land ownership, the role of farmers, and market development.
This review focuses on the cropping systems of the Mediterranean’s bordering lands. It examines the region’s climatic characteristics, emphasizing rainfall as the key determinant of plant growth. Attention is given to the quantity and distribution of rainfall, as well as the effects of variable moisture and temperature regimes on the duration and quality of the growing season. The review also briefly addresses land use, agricultural systems, and soil resources, highlighting their influence on productivity, conservation practices, and broader social and economic dynamics. The primary farming and cropping systems are summarized with a focus on crop rotations, including insights from long-term crop sequence trials to evaluate the sustainability of various systems. Topics such as tillage and planting methods, conservation practices, cultural techniques, pest control, and soil fertility (nitrogen, phosphorus, and micronutrients) are explored. The review concludes by identifying future research priorities for the sustainable development of the Mediterranean region’s drylands.

Abstract
In the Syrian Steppe we carried out a field experiment using repeated hyperspectral measurements throughout the 2001 growing season to investigate the separability of vegetation types based on their temporal-spectral signatures. We studied two different perennial shrubs and annual grasses showing differences in the length of their growing period. These differences cause seasonal variations in spectral plant reflectance, also giving hints to functional vegetation characteristics such as palatability, soil stabilization or certain hydrometeorological characteristics. By submitting biophysical and spectral data to statistical analyses we identified the dry season period as the most suitable to discriminate between the studied plants or their functional characteristics respectively. NDVI time series using MODIS or SPOT VEGETATION NDVI bands perform nearly as well as an optimum narrow banded index. For a balanced assessment of the biomass from different functional vegetation groups, our study recommends a narrow banded (9nm width) vegetation index that uses the 630nm band together with the 755nm band. Its particular advantage, compared with indices calculated from MODIS and SPOT VEGETATION bands, is the better correlation with biomass from annual grasses.
Conclusions from the field experiment were tested for their transferability to remote sensing conditions. Using the noise-filtered shapes of NDVI cycles as a primary classifier, we identify the distribution and the fractional cover of species with an extended growing period. Pixels with a dominant cover of species with an extended growing period were classified by correlating a reference NDVI cycle with all image pixels, where the calculated correlation coefficient is the measure for shape similarity, and the corresponding slope value the measure for vegetation cover relative to the reference.

Abstract
The Near East is characterized by a high level of aridity and frequent droughts, which pose significant challenges to the region’s economy, particularly its agriculture sector. As the primary consumer of water resources, agriculture is highly sensitive to variations in precipitation, with droughts leading to substantial losses in grain crop production and livestock. These losses not only impact the livelihoods of livestock producers but also strain national economies, disrupting balance of payments.
Historically, governmental responses to drought in the region have focused on mitigating its immediate effects rather than addressing it as a structural issue requiring long-term policy and management solutions. While institutional reforms are gradually being implemented, governments increasingly recognize the need for a comprehensive dryland management strategy. Such a strategy must integrate traditional drought mitigation measures with broader objectives, including agricultural stabilization, productivity enhancement, and environmental sustainability.
Drought monitoring systems are essential components of this strategy, enabling timely and informed responses. Key strategic considerations include establishing institutional frameworks to facilitate seamless information exchange between monitoring entities and decision-making bodies at various levels. Research priorities in this area include improving drought prediction, mapping drought impacts spatially, and assessing vulnerability from agroecological and livelihood perspectives.