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Climate change (CC) may pose a challenge to agriculture and rural livelihoods in Central Asia, but in-depth studies are lacking. To address the issue, crop growth and yield of 14 wheat varieties grown on 18 sites in key agroecological zones of Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan in response to CC were assessed. Three future periods affected by the two projections on CC (SRES A1B and A2) were considered and compared against historic ((1961-1990) figures. The impact on wheat was simulated with the CropSyst model distinguishing three levels of agronomic management. Averaged across the two emission scenarios, three future periods and management scenarios, wheat yields increased by 12% in response to the projected CC on 14 of the 18 sites. However, wheat response to CC varied between sites, soils, varieties, agronomic management and futures, highlighting the need to consider all these factors in CC impact studies. The increase in temperature in response to CC was the most important factor that led to earlier and faster crop growth, and higher biomass accumulation and yield. The moderate projected increase in precipitation had only an insignificant positive effect on crop yields under rainfed conditions, because of the increasing evaporative demand of the crop under future higher temperatures.However, in combination with improved transpiration use efficiency in response to elevated atmospheric CO 2 concentrations, irrigation water requirements of wheat did not increase. Simulations show that in areas under rainfed spring wheat in the north and for some irrigated winter wheat areas in the south of Central Asia, CC will involve hotter temperatures during flowering and thus an increased risk of flower sterility and reduction in grain yield. Shallow groundwater and saline soils already nowadays influence crop production in many irrigated areas of Central Asia, and could offset productivity gains in response to more beneficial winter and spring temperatures under CC.Adaptive changes in sowing dates, cultivar traits and inputs, on the other hand, might lead to further yield increasesi.

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Woody biomass production is a critical indicator to evaluate 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) imagery. Among a set of vegetation indices, the Normalized Difference Vegetation Index (NDVI) provided the best correlation with CC (R 2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to MODIS (Moderate Resolution Imaging Spectroradiometer) 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 FAO 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 the results from other authors obtained by field measurements or by 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.