LED Illumination for High-Quality High-Yield Crop Growth in Protected Cropping Environments.

Vegetables and herbs play a central role in the human diet due to their low fat and calory content and essential antioxidant, phytochemicals, and fiber. It is well known that the manipulation of light wavelengths illuminating the crops can enhance their growth rate and nutrient contents. To date, it has not been easy to generalize the effects of LED illumination because of the differences in the plant species investigated, the measured traits, the way wavelengths have been manipulated, and the plants' growing environments. In order to address this gap, we undertook a quantitative review of LED manipulation in relation to plant traits, focusing on vegetables and herbs. Here, we use standardized measurements of biomass, antioxidant, and other quantitative characteristics together with the whole range of the photosynthetic photon flux density (PPFD). Overall, our review revealed support for the claims that the red and blue LED illumination is more reliable and efficient than full spectrum illumination and increases the plant's biomass and nutritional value by enhancing the photosynthetic activity, antioxidant properties, phenolic, and flavonoids contents. Although LED illumination provides an efficient way to improve yield and modify plant properties, this study also highlights the broad range of responses among species, varieties traits, and the age of plant material.

Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering.

Pedigree and sibship reconstruction are important methods in quantifying relationships and fitness of individuals in natural populations. Current methods employ a Markov chain-based algorithm to explore plausible possible pedigrees iteratively. This provides accurate results, but is time-consuming. Here, we develop a method to infer sibship and paternity relationships from half-sibling arrays of known maternity using hierarchical clustering. Given 50 or more unlinked SNP markers and empirically derived error rates, the method performs as well as the widely used package Colony, but is faster by two orders of magnitude. Using simulations, we show that the method performs well across contrasting mating scenarios, even when samples are large. We then apply the method to open-pollinated arrays of the snapdragon Antirrhinum majus and find evidence for a high degree of multiple mating. Although we focus on diploid SNP data, the method does not depend on marker type and as such has broad applications in nonmodel systems.