Abstract
Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a major wheat disease that can inflict yield losses of up to 70% on susceptible varieties under favourable environmental conditions. The timely identification of plant genetic resources likely to possess novel resistance to this disease would facilitate the rapid development of resistant wheat varieties. The focused identification of germplasm strategy (FIGS) approach was used to predict stripe rust resistance in a collection of wheat landraces conserved at ICARDA genebank. Long-term climate data for the collection sites, from which these accessions originated and stripe rust evaluation scores for one group of accessions were presented to three different non-linear models to explore the trait×collection site environment interactions. Patterns in the data detected by the models were used to predict stripe rust resistance in a second and different set of accessions. The results of the prediction were then tested against actual evaluation scores of the disease in the field. The study mimics the real scenario where requests are made to plant genetic resources curators to provide accessions that are likely to possess variation for specific traits such as disease resistance.
The models used were able to identify stripe rust-resistant accessions with a high degree of accuracy. Values as high as 0·75 for area under the curve and 0·45 for Kappa statistics, which quantify the agreement between the models’ predictions and the curator’s disease scores, were achieved. This demonstrates a strong environmental component in the geographic distribution of resistance genes and therefore supports the theoretical basis for FIGS.
It is argued that FIGS will improve the rate of gene discovery and efficiency of mining genetic resource collections for adaptive traits by reducing the number of accessions that are normally required for evaluation to identify such variation.

Abstract
Salinization is a common problem for agriculture in dryland environments and it has greatly affected land productivity and even caused cropland abandonment in Central and Southern Iraq. Hence it is of pressing importance to quantify the spatial distribution of salinity and its changing trend in space and time and ascertain the driving forces thereof. This study aims at such a diachronic salinity mapping and analysis using multitemporal remote sensing taking a pilot site, the Dujaila area in Central Iraq, as an example. For this purpose, field survey and soil sampling were conducted in the 2011–2012 period, and a multitemporal remote sensing dataset consisting of satellite imagery dated 1988–1993, 1998–2002, and 2009–2012 was prepared. An innovative processing approach, the multiyear maxima-based modeling approach, was proposed to develop remote sensing salinity models. After evaluation of their suitability, the relevant models were applied to the images for multitemporal salinity mapping, quantification, and change tracking in space and time. The driving causes of salinization in the study area were evaluated. The results reveal that the developed salinity models can reliably predict salinity with an accuracy of 82.57%, indicating that our mapping methodology is relevant and extendable to other similar environments. In addition, salinity has experienced significant changes in the past 30 years in Dujaila, especially, very strongly salinized land got continuously expanded, and all these changes are related to land use practices and management of farmers, which are closely associated with the macroscopic socioeconomic environment of the country.

Abstract
Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs.
Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example.
The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and Lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.

Abstract
The focused identification of germplasm strategy (FIGS) has been validated using predictive computer models in simulation studies to predict a priori known trait soores. This study was designed as a “blind” study where the person calculating the computer model did not know the actual trait scores. This study design provides a more realistic test of the predictive capacity of the FIGS approach compared to previous studies. Furthermore, this study also explored the suitability of FIGS for the identification of resistance in bread wheat {Triticum aestivum L. subsp. aestivum) and durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.] to Ug99—a strain of stem rust {Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and typified to race TTKSK. The predictions were validated against a dataset with the screening of wheat accessions conducted in Yemen in 2008. Only a small training set representing 20% of the trait soreening results was disclosed to the person conducting the data analysis for the calibration of the prediction model. The hit rate for identification of Ug99-resistant accessions was more than two times higher when using the FIGS approach compared to a random selection of accessions. These results suggested that FIGS was well suited for the identification of samples with resistance to fungal pathogens. It is therefore recommended that FIGS approach be used as a complement to expert knowledge and experience when selecting accessions for plant breeding and crop research activities.

Abstract
Expectations have been raised that carbon sequestration in soils could provide a short-term bridge to reduce the impacts of increasing carbon emissions until low-carbon technologies are available.
To assess the role of Central Asia in this regard, the organic carbon in soils of Central Asia and losses in response to land use were quantified in a spatially explicit way. Based on literature information on soil organic carbon contents and in combination with the FAO-UNESCO Soil Map of the Word, the organic carbon stocks in the upper 30 cm of native soils of Central Asia were estimated to amount to 20,17± 4,03 Pg. The extent of conversion of native land into agricultural land and the degradation of rangelands was assessed by a land use land cover change map of the region. This type of land use (change) was responsible for a reduction of the soil organic carbon by about 828±166 Tg C, or on average 4.1% of the total stocks. To this reduction, degradation of rangeland (observed on 4.9 Mha) with 50 Tg, contributed  only 6%. Most of the losses resulted from past conversion of rangelands into rainfed or irrigated agricultural land in the north of Kazakhstan. Hotspots of high soil organic matter depletion were former wetlands, drained for cultivation during the last decades. Assuming that improved agronomic and grazing management could be put in place and that therewith SOC levels in all of Central Asia’s cropland and degraded rangeland could be brought back to native levels in the next 50 years, each year 16.6 Tg C could be sequestered. This is equal to the sizeable amount of 15.5% of the 2004 annual anthropogenic C-emissions of the five Central Asian countries (107 Tg C yr−1). However, Central Asia contributed only 1.4% of CO2 that is set free worldwide by fossil fuel burning. Therefore, the mitigation effect on rising atmospheric CO2 levels and climate change of such ambitious sequestration plans, if put into practice, would be hardly notable. The central Asian example shows that, unfortunately, the strategy of soil C sequestration as a stand-alone measure is not a viable bridge to a future in which alternative energy source can substitute fossil fuel burning, but can only be part of a set of mitigating measures.

Abstract
Based on climate change projections from the IPCC Fourth Assessment Report, the drylands surrounding the Mediterranean are expected to experience some of the world’s most significant precipitation declines. This chapter examines this vulnerable dryland region, highlighting how the continuity between current climate trends and near-future projections can inform the development of land, water, and crop management practices to address the challenges of climate change.
Analysis of gridded precipitation data from the GPCC Full Data Reanalysis Product (Version 4) for the period 1901–2007 reveals a clear and often highly significant negative precipitation trend, despite high year-to-year variability. The magnitude of this trend over the past century closely aligns with projections from most General Circulation Models (GCMs) for the Mediterranean region in the coming decades. This alignment suggests that climate change forces have been active in the region for at least a century, with human activities further intensifying an existing trend.
The region’s diverse agricultural systems, adapted to specific agroecological niches and climatic constraints, offer valuable opportunities for adaptation to climate change. A detailed evaluation of the coping mechanisms employed in these systems can inform the refinement of established dryland management principles, ultimately helping to “climate-proof” agricultural systems across the Mediterranean.