Biomass yield and energy value of some fast-growing multipurpose trees in Nigeria

1997 ◽  
Vol 12 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Joseph A. Fuwape ◽  
Shadrach O. Akindele
Keyword(s):  
Biomass Yield ◽  
Multipurpose Trees ◽  
Energy Value ◽  
Fast Growing

scholarly journals Energy Aspects of Exploitation of Pruning Residues from Apple Production

2015 ◽  
Vol 15 (2) ◽  
pp. 139
Author(s):  
Miloš Pajić ◽  
Milan Dražić ◽  
Vesna Pajić ◽  
Dušan Radojičić ◽  
Kosta Gligorević ◽  
...  
Keyword(s):  
Biomass Yield ◽  
Heating Value ◽  
Energy Value ◽  
Apple Varieties ◽  
Apple Production ◽  
Pruning Residues ◽  
Energy Aspects

Large quantities of biomass result from fruit pruning, which is a re­quired pomotechnical operation. Long-standing practice of destroying and burning the pruning residues is hard to change, although energy and envi­ronmental indicators point to the importance of the proper biomass exploitation. The aim of this study is to show the quantity and energy value of pruning residues from apple production, as well as ways and means of their proper exploitation. In Republicof Serbia, apple is grown at 40,000 ha, with the usual biomass yield (3 t ha-1 pruning residues). Upper heating value of pruning residues for two apple varieties (Idared and Jonagold), with 42.36% moisture, was 18.14 MJ kg-1 average.

scholarly journals Comparison Of Energy Sources Grown On Agricultural Land

2015 ◽  
Vol 12 (2) ◽  
pp. 38-43 ◽  
Author(s):  
Zuzana Jureková ◽  
Dušan Húska ◽  
Marián Kotrla ◽  
Martin Prčík ◽  
Martin Hauptvogl
Keyword(s):  
Biomass Production ◽  
Aboveground Biomass ◽  
Agricultural Land ◽  
Biomass Yield ◽  
Biomass Energy ◽  
Statistical Dependence ◽  
Individual Species ◽  
Miscanthus Sinensis ◽  
Energy Value ◽  
Growing Period

Abstract The aim of the paper is to compare biomass production of energy plants and selected crops grown on arable land in the south-western Slovakia in 2007–2014, its energy value and the influence of decisive climatic factors on the size of the production. The data on yields of dominant crops grown in the agricultural farm were obtained from the statistical data of the farm. Aboveground biomass of willows and poplars was harvested at the end of the harvest cycle. Aboveground biomass of Miscanthus sinensis was harvested in 2010–2014, always in early spring period of the following year. Winter wheat, spring barley and maize grown for silage during the period 2007–2014 provided the lowest yields in 2010 and the highest in 2011 and 2014. The highest energy value was obtained from maize in 2014 (400.66 GJ ha−1). The short rotation coppice poplars of Italian provenance yielded biomass with energy value of 951.68 GJ ha−1 year−1 at the end of the first three-year harvest cycle in 2012. The analysis of variance confirmed that there are highly significant statistical differences in the poplar biomass yield among the varieties and individual experimental years. The fast growing willows of Swedish provenance provided aboveground biomass energy value of 868.88 GJ ha−1 year−1 at the end of the first four-year harvest cycle in 2011. The biomass production of the perennial grass Miscanthus sinensis, depending on the growing period, can be expressed by a polynomial trend function. The highest biomass production was obtained in the third growing period (2012). At the end of the fifth growing period (2014), the yield amounted to 28.60 t ha−1 of the dry aboveground biomass. The energy value of the aboveground biomass of Miscanthus reached 486.20 GJ ha−1 in 2014. Differences in the biomass yield of the Miscanthus genotypes are statistically highly significant in each of the monitored growing periods. The growth and production process of the selected energy species reflect the specificities of soil and climatic conditions of the individual growing periods, as well as the ability of individual species and varieties to provide biomass production in the given conditions. Regression analysis of the produced aboveground biomass of the crops grown in the Kolíňany cadastre has not confirmed a statistical dependence in selected climatic parameterss.

Biomass yield and biochemical profile of fourteen species of fast-growing green algae from eutrophic bloomed freshwaters of Kerala, South India

2018 ◽  
Vol 119 ◽  
pp. 155-165 ◽  
Author(s):  
Prasanthkumar Santhakumaran ◽  
Santhosh Kumar Kookal ◽  
Joseph George Ray
Keyword(s):  
Green Algae ◽  
South India ◽  
Biomass Yield ◽  
Biochemical Profile ◽  
Fast Growing

Fast-Growing China's Hydropower Systems and Operation Challenges

2011 ◽  
Author(s):  
Chun-Tian Cheng ◽  
K. W. Chau ◽  
Xin-Yu Wu ◽  
Jian-Jian Shen
Keyword(s):  
Fast Growing

Modern university as an economic growth driver: Models & missions

2017 ◽  
pp. 58-76 ◽  
Author(s):  
A. Karpov
Keyword(s):  
Economic Growth ◽  
Education Research ◽  
Advanced Technology ◽  
Global Competitiveness ◽  
Technology Markets ◽  
Fast Growing ◽  
Business Ecosystem ◽  
Cost Efficient ◽  
National Economies ◽  
The University

The paper considers the modern university as an economic growth driver within the University 3.0 concept (education, research, and commercialization of knowledge). It demonstrates how the University 3.0 is becoming the basis for global competitiveness of national economies and international alliances, and how its business ecosystem generates new fast-growing industries, advanced technology markets and cost-efficient administrative territories.

Characteristics, Features of Development, Regional and Sectoral Determinants of High-tech Business in Russia

2017 ◽  
pp. 142-154 ◽  
Author(s):  
A. Yusupova ◽  
S. Khalimova
Keyword(s):  
Regression Analysis ◽  
Performance Indicators ◽  
Sustainability Assessment ◽  
Business Development ◽  
High Tech ◽  
Assessment Data ◽  
High Tech Sector ◽  
Fast Growing ◽  
High Level

The paper deals with the research devoted to characteristics of high tech business development in Russia. Companies’ performance indicators have been analyzed with the help of regression analysis and author’s scheme of leadership stability and sustainability assessment. Data provided by Russia’s Fast Growing High-Tech Companies’ National Rating (TechUp) during 2012-2016 were used. The results have revealed that the high tech sector is characterized by high level of uncertainty. Limited number of regions and sectors which form the basis for high tech business have been defined. Relationship between innovation activity’s indicators and export potential is determined.

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