Growing industrial crops on marginal lands has been proposed as a strategy to minimize competition for arable land and food production. In the present study, eight experimental sites in three different climatic zones in Europe (Mediterranean, Atlantic and Continental), seven advanced industrial crop species [giant reed (two clones), miscanthus (M. × giganteus and two new seed‐based hybrids), saccharum (one clones), switchgrass (one variety), tall wheatgrass (one variety), industrial hemp (three varieties) and willow (eleven clones)], and six marginality factors alone or in combination (dryness, unfavorable texture, stoniness, shallow soil, topsoil acidity, heavy metal and metalloid contamination) were investigated. At each site, biophysical constraints and low‐input management practices were combined with prevailing climatic conditions. The relative yield of a site‐specific low‐input system compared with the site‐specific control was from small to large (i.e. from −99% in industrial hemp in the Mediterranean to +210% in willow in the Continental zone), due to the genotype‐by‐management interaction along with climatic variation between growing seasons. Genotype selection and improved knowledge on crop response to changing environmental, site‐specific biophysical constraint and input application has been detected as key to profitably grow industrial crops on marginal areas. This study may act to provide hints on how to scale up investigated cropping systems, through low‐input practices, under similar environmental and soil conditions tested at each site. However, further attention to detail on the agronomy of early plant development and management in larger multi‐year and multi‐location field studies with commercially scalable agronomies are needed to validate yield performances, and thereby to inform on the best industrial crop options.
Objectives: To define and develop risk -and more specifically market access riskas a framework towards understanding and evaluating stability in market access systems at an individual country level. MethOds: We created a combination model of rating quantitative and qualitative variables which affect a country's ability and willingness to pay for new drugs. The criterion for selection of variables is based on relevance, availability and uniformity in our model. We included a total of 42 variables categorised under three verticals -quantitative, qualitative and measures of stability. In order to derive a non-recursive model of ratings, we fit the regression equation for quantitative and qualitative variables as: Y(1) = α i + ∑β i *X i + ε (Equation 1.1) Y(2) = α j + ∑β j *X j + ε (Equation 1.2) where Y(1) and Y(2) are the market access risk ratings for quantitative and qualitative variables, X i and X j are vectors of independent quantitative and qualitative variables, and ε is the error term. The final score was derived by taking the geometric mean of the two ratings together with ratings for the measures of stability and is described as below: Total Risk Score = √Y(1)^2* Weight of Y(1) + Y(2)^2*Weight of Y(2) + Risk Rating (Measures of Stability)^2*Weight of (Measures of Stability). Results: We decided to aggregate risk scores from different countries into defined clusters -such as BRICS
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