Sowing density and harvest time affect fibre content in hemp (Cannabis sativa) through their effects on stem weight

Sowing density and harvest time are considered important crop management factors influencing fibre quantity and quality in hemp (Cannabis sativa). We investigated whether the effects of these factors are essentially different or that both factors affect stem weight and thereby total and long‐fibre content. The effects of all combinations of three sowing densities and three harvest times were studied for six different stem parts. Almost 500 samples consisting of stem parts from 50 plants and with a length of 50 cm were tested. Fibres were extracted by a controlled warm‐water retting procedure, followed by breaking and scutching. The initial sample weight was fractionated into retting losses, wood, tow and long fibre. In both Italy and the Netherlands, crops were successfully established with different stem densities (99–283 m^?2), plant heights (146–211 cm) and stem diameters (4.5–8.4 mm) at harvest. Stem dry matter yields (6.8–11.7 Mg ha^?1) increased with a delay in harvest time but were not affected by sowing density. Retting loss percentages were lower in lower stem parts and decreased with later harvest because maturation was associated with increasing amounts of fibre and wood. Within a certain stem part, however, the absolute retting losses were constant with harvest time. Multiple linear regression analyses showed that the amount of fibre in a hemp stem is almost completely determined by the weight and the position of that stem part. When the plant grows, the increase in dry matter is split up into fibres and wood in a fixed way. This total fibre/wood ratio was highest in the middle part of the stem and lower towards both bottom and top. Sowing density and harvest time effects were indirect through stem weight. The long‐fibre weight per stem increased with the total fibre weight and hence with stem weight. Stem weight increased with harvest time; as harvest time did not affect plant density, the highest long‐fibre yields were obtained at the last harvest time. The long fibre/total fibre ratio was lowest in the bottom 5 cm of the stems but similar for all other parts. Sowing density and harvest time effects again were indirect. Fibre percentages in retted hemp decreased with increasing stem weights towards a level that is presumably a variety characteristic. The dry matter increase between harvests, however, is much more important with respect to total and long‐fibre yield.     

Postponed sowing does not alter the fibre/wood ratio or fibre extractability of fibre hemp (Cannabis sativa)

Because hemp is a short-day plant, postponing the sowing date might be a suitable strategy to obtain shorter and smaller plants around flowering, when primary fibres are 'ripe' enough to be harvested. Smaller plants can be processed on existing flax scutching and hackling lines and might have fibre characteristics that are desirable for producing high-quality 'long fibres' for yarn spinning.
It was investigated whether sowing beyond the normal sowing period in the Netherlands affects the ratio in which fibres and wood are produced, and what proportion of these fibres are long fibres, suitable for long fibre spinning. About 400 stem samples were fractioned into retting losses, wood, tow, and long fibre, and the ratios between fractions were analysed using multiple linear regression analyses.
A normal sowing date at the end of April was compared with a postponed sowing date at the end of May. The total fibre/wood ratio was not affected. More than 95% of the variance in total fibre was accounted for by the wood weight per stem (55.5%), the variety (+33.3%) and the stem part (+6.5%). The amount of long fibre per stem mainly depended on the amount of the total fibre per stem (95.4% variance was accounted for) and the stem part (+2.0%).
For economic reasons, it could be interesting to grow two successive high-quality hemp crops in one growing season. Therefore, in an additional experiment with one variety, the effect of sowing fibre hemp up to 12 weeks later than normal on the quantity and quality of the fibres was studied. Postponing the sowing date up to 12 weeks had no important effects on retting losses, the total fibre/wood ratio, and the long fibre/total fibre ratio. It is therefore technically possible to grow two successive hemp crops. Whether this fits well in farming systems and a hemp production chain remains to be studied.     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

Population-based nutrikinetic modeling of polyphenol exposure

The beneficial health effects of fruits and vegetables have been attributed to their polyphenol content. These compounds undergo many bioconversions in the body. Modeling polyphenol exposure of humans upon intake is a prerequisite for understanding the modulating effect of the food matrix and the colonic microbiome. This modeling is not a trivial task and requires a careful integration of measuring techniques, modeling methods and experimental design. Moreover, both at the population level as well as the individual level polyphenol exposure has to be quantified and assessed. We developed a strategy to quantify polyphenol exposure based on the concept of nutrikinetics in combination with population-based modeling. The key idea of the strategy is to derive nutrikinetic model parameters that summarize all information of the polyphenol exposure at both individual and population level. This is illustrated by a placebo-controlled crossover study in which an extract of wine/grapes and black tea solids was administered to twenty subjects. We show that urinary and plasma nutrikinetic time-response curves can be used for phenotyping the gut microbial bioconversion capacity of individuals. Each individual harbours an intrinsic microbiota composition converting similar polyphenols from both test products in the same manner and stable over time. We demonstrate that this is a novel approach for associating the production of two gut-mediated γ-valerolactones to specific gut phylotypes. The large inter-individual variation in nutrikinetics and γ-valerolactones production indicated that gut microbial metabolism is an essential factor in polyphenol exposure and related potential health benefits.     

Simplivariate models: ideas and first examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework 'simplivariate models' which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of 'divide and conquer', we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli.     

Statistical validation of megavariate effects in ASCA

Background  

Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.