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Energy flow in orchards can be used to determine first the best management practices and second the possibility of using those which have best environmental advantages. Conventional and organic peach and kiwi orchards were selected in order to (a) determine energy flow of the farming systems, and (b) reveal the importance of energy inputs in crop and farming system replacement. Fifteen farms (four conventional and three organic kiwi orchards; four conventional and four organic peach orchards) were selected with proportional stratified random sampling during the years 2010–2013. The Hierarchical Cluster Analysis (HCA) method was applied using nine production coefficients' variables (fertilizers, fungicides, insecticides, weed control, diesel, labor, irrigation, branches shoring, and machinery) and revealed three groups of the studied orchards. The highest contributors in cluster formation were weed control, branches shoring, labor, and machinery. The effect of the production coefficients on the grouping of the studied orchards reveals their importance for these crops and farming systems. Most of the production coefficients showed their lowest values in organic kiwi orchards (Group 3), so it could be said that they can play a key role in the replacement of the peaches, and conventional kiwi orchards. It seems that production coefficients can be used as a “tool” for decision makers who are seeking for crops and farming systems with low energy inputs and best environmental advantages in order to use them in crop replacement in agro-ecosystems. [ABSTRACT FROM AUTHOR]

Organic farming is applied in olive groves in Lesvos Island the last 20 years. “Kolovi” and “Adramitiani”, two dominant varieties are cultivated. Since there is limited research for energy inputs in olive groves, 62 conventional and 26 organic farms were selected during 2011–2013 in order to (a) determine the differences in energy flow among farming systems and varieties, (b) group olive groves based on energy flow indicators, (c) compare the CO 2 -equivalent emissions among farming systems and varieties. A combination of univariate and multivariate statistical methods was applied. Hierarchical Cluster Analysis (HCA) revealed three farm groups, all consisted of conventional and/or organic olive groves and included both varieties. Group 1 had the lowest energy inputs, while Group 3 the highest. Fuels and transportation, as energy inputs, had the highest contribution in farms’ grouping. A large number of external variables was studied, most of which (fruit production, olive oil production, pomace production, shoot production, olive oil energy production, pomace energy production, shoot energy production, total energy inputs, total energy outputs, intensity, energy efficiency, and energy productivity) had statistically significant differences among the three Groups. Management practices along with geographical location could be a reasonable explanation for the differences between the groups of studied olive groves. Group 3 had the highest non-renewable energy inputs (14,683.5 MJ ha −1 ) and consumption (2.4 MJ kg −1 ) and gas emissions (1.27 Mg ha −1 CO 2 , 0.17 kg ha −1 CH 4 , and 10.31 g ha −1 N 2 O). Group 2 had the highest renewable energy inputs (7065.8 MJ ha −1 ) and consumption (0.9 MJ kg −1 ), and low CO 2 -equivalent per fruit production (0.12 kg kg −1 ). The above mentioned results show that best management farming practices introduce the use of renewable energy inputs and lead to lower gas emissions. [ABSTRACT FROM AUTHOR]

Abstract: Recently, the increased cost of fossil fuels and greenhouse gas emissions has attracted the interest of many scientists to study the energy use in agricultural systems. Energy analysis in white asparagus farms is useful to decide best management strategies. The objectives of this study were to evaluate a) the energy flow among conventional, integrated and organic farming systems and b) the effect of farming system to greenhouse gas emissions. Eleven farms (three conventional, five integrated, three organic) at nine locations in Greece were selected during 2007–2008. Multidimensional data analyses were used to detect a) clusters of farming systems and b) associations between field operations and production coefficients variables. Three clusters of farming systems and four groups of variables were revealed. Non-parametric tests concerning external variables (outputs, energy efficiency, spear production, CO2, CH4, and N2O) showed that the three clusters of farming systems were not statistically different relative to output and energy efficiency variables but spear productions and gas emissions were lower for the organic farming cluster. The results showed that organic farming could reduce inputs and gas emissions. [Copyright &y& Elsevier]