Biomass potentials of miscanthus, willow and poplar: results and policy implications for Eastern Europe, Northern and Central Asia

Abstract Over the past 20 years, the term agro-ecological zones methodology (AEZ) has become widely used for global regional and national assessments of agricultural potentials. The AEZ methodologies and procedures have recently been extended and newly implemented to make use of the latest digital geographical databases. At the same time a companion model of AEZ has been developed that enables assessments of potential productivity of forest tree species. AEZ follows an environmental approach; provides a standardized framework for the characterization of climate, soil and terrain conditions relevant to crop and forest species production; uses environmental matching procedures to identify limitations of prevailing climate, soil and terrain for assumed management objectives. The AEZ model includes an inventory of ecological adaptability characteristics as well as an inventory of specific ecological and environmental requirements for crop and forest tree species. The natural resources inventory is based on an up-to-date GIS database of climate, soil, terrain and vegetation covering China, Europe, Mongolia and the former Soviet Union. Results of potential productivity for miscanthus, willow and poplars in countries of Eastern Europe and Northern and Central Asia are presented for (i) all suitable areas, (ii) all suitable areas but excluding forests, urban areas and land that is potentially highly suitable for cereal production. The results show a large variation in potentials for bio-energy in the various countries. In a few countries—Lithuania, Latvia, Romania, Georgia, Belarus and Azerbaijan, the potential for producing energy from miscanthus, poplar and willow alone is more than one-third of the current commercial energy use in these countries, even when forests and land potentially highly suitable for cereals are excluded from the assessment.

[1]  Incorporating Natural Vegetation into the LUC Project Framework , 1996 .

[2]  P. Jones,et al.  Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .

[3]  P. Sa European energy to 2020: A scenario approach : European Commission: Directorate General for Energy (DGXVII), Office for Official Publications of the European Communities (Luxembourg), 1996 , 1997 .

[4]  Smith Martin,et al.  Cropwat : a computer program for irrigation planning and management , 1992 .

[5]  Robert Lupton,et al.  Statistics in Theory and Practice , 2020 .

[6]  G. Fischer,et al.  The IIASA/LUC Project Georeferenced Database for the former USSR. Volume 6: Agricultural Regionalization , 1997 .

[7]  Assessment of Potential Productivity of Tree Species in China, Mongolia and the Former Soviet Union: Methodology and Results , 2001 .

[8]  Jan Berdowski Overview of national programmes to reduce greenhouse gas emission , 1999 .

[9]  V. Stolbovoi FAO Land and Water Digital Media Series No. 7. Soil and Physiographic Database for North and Central EURASIA at 1:5 Million Scale , 1998 .

[10]  Guenther Fischer,et al.  Global Agro-ecological Assessment for Agriculture in the 21st Century , 2002 .

[11]  J. Granat,et al.  AEZWIN An Interactive Multiple-Criteria Analysis Tool for Land Resources Appraisal , 1998 .

[12]  Guenther Fischer,et al.  Agro-ecological land resources assessment for agricultural development planning. A case study of Kenya. Resources data base and land productivity. Technical Annex 6. Fuelwood productivity. , 1993 .