Why does low intensity, long-day lighting promote growth in Petunia, Impatiens, and tomato?

Numerous reports demonstrate that low intensity, long-day (LD) lighting treatments can promote growth. However, there are conflicting suggestions as to the mechanisms involved. This study examines the responses of Petunia, Impatiens, and tomato to LD lighting treatments and concludes that no single mechanism can explain the growth promotion observed in each case. Petunia showed the most dramatic response to photoperiod; up to a doubling in dry weight (DW) as a result of increasing daylength from 8 h d–1 to 16 h d–1.This could be explained by an increase in specific leaf area (SLA) comparable to that seen with shading. At low photosynthetic photon flux densities (PPFD), the increased leaf area more than compensated for any loss in photosynthetic capacity per unit leaf area. In Petunia, the response may, in part, have also been due to changes in growth habit. Impatiens and tomato showed less dramatic increases in DW as a result of LD lighting, but no consistent effects on SLA or growth habit were observed. In tomato, increased growth was accompanied by increased chlorophyll content, but this had no significant effect on photosynthesis. In both species, increased growth may have been due to a direct effect of LD lighting on photosynthesis. This is contrary to the generally held view that light of approx. 3 – 4 μmol m–2 s–1 is unlikely to have any significant impact on net photosynthesis. Nevertheless, we show that the relationship between PPFD and net photosynthesis is non-linear at low light levels, and therefore low intensity LD lighting can offset respiration very efficiently. Furthermore, a small increase in photosynthesis will have a greater impact when ambient light levels are low

The effects of day and night temperature on Chrysanthemum morifolium: investigating the safe limits for temperature integration

The impact of day and night temperatures on pot chrysanthemum (cultivars ‘Covington’ and ‘Irvine’) was assessed by exposing cuttings, stuck in weeks 39, 44, and 49, to different temperature regimes in short-days. Glasshouse heating setpoints of 12°, 15°, 18°, and 21°C, were used during the day, with venting at 2°C above these set-points. Night temperatures were then automatically manipulated to ensure that all of the treatments achieved similar mean diurnal temperatures. Plants were grown according to commercial practice and the experiment was repeated over 2 years. Increasing the day temperature from approx. 19°C to 21°C, and compensating by reducing the night temperature, did not have a significant impact on flowering time, although plant height was increased.This suggests that a temperature integration strategy which involves higher vent temperatures, and exploiting solar gain to give higher than normal day temperatures, should have minimal impact on crop scheduling. However, lowering the day-time temperature to approx. 16°C, and compensating with a warmer night, delayed flowering by up to 2 weeks. Therefore, a strategy whereby, in Winter, more heat is added at night under a thermally-efficient blackout screen may result in flowering delays.Transfers between the temperature regimes showed that the flowering delays were proportional to the amount of time spent in a low day-time temperature regime. Plants flowered at the same time, irrespective of whether they were transferred on a 1-, 2-, or 4-week cycle

Simultaneous Multicolor Detection of Faint Galaxies in the Hubble Deep Field

We present a novel way to detect objects when multiband images are available. Typically, object detection is performed in one of the available bands or on a somewhat arbitrarily co-added image. Our technique provides an almost optimal way to use all the color information available. We build up a composite image of the N passbands where each pixel value corresponds to the probability that the given pixel is just sky. By knowing the probability distribution of sky pixels (a chi-square distribution with N degrees of freedom), the data can be used to derive the distribution of pixels dominated by object flux. From the two distributions an optimal segmentation threshold can be determined. Clipping the probability image at this threshold yields a mask, where pixels unlikely to be sky are tagged. After using a standard connected-pixel criterion, the regions of this mask define the detected objects. Applying this technique to the Hubble Deep Field data, we find that we can extend the detection limit of the data below that possible using linearly co-added images. We also discuss possible ways of enhancing object detection probabilities for certain well defined classes of objects by using various optimized linear combinations of the pixel fluxes (optimal subspace filtering).Comment: 8 pages, 5 figures (4 postscript, 1 JPEG). To be published in A

The effects of long-day lighting and removal of young leaves on tomato yield

While low intensity long-day (LD) lighting has been shown to enhance the growth of young plants under low light levels, its effect on the yield of a long-season glasshouse tomato crop has not been previously examined. LD were provided by the use of tungsten lamps (2.8 μmol m-2 s-1 at approx. 0.5 m from the ground) between 04.00 h to sunrise and from sunset until 20.00 h (GMT). LD lighting increased leaf chlorophyll contents, and the numbers of flowers and fruits set per truss when the plants were young. However, this treatment did not affect the total yield of tomatoes. Different leaf removal treatments were applied within each glasshouse compartment. A previous experiment had shown that reducing the leaf area index (LAI) from 5.2 to 2.6, by removing old leaves, did not affect yield. It was also thought that removal of young leaves reduced the total vegetative sink-strength and favoured assimilate partitioning into the fruit. Therefore, removal of young leaves could increase fruit yield. In the present experiments, one-third of the leaves were removed in March (those immediately below each truss) and, subsequently, every third leaf was removed at an early stage of its development. This reduced the LAI from 4.1 to 2.9 and resulted in a loss of yield from 3 – 4 weeks after leaf removal until the end of the experiment, at which point there was an 8% loss of cumulative yield due to a reduction in the average number of fruits set per truss and in mean fruit weight. We postulate that the light which would have been intercepted by young photosynthetically-efficient leaves at the top of the canopy was intercepted instead by older leaves which were less efficient, reducing overall net canopy photosynthesis

Measuring Progress on the Control of Porcine Reproductive and Respiratory Syndrome (PRRS) at a Regional Level: The Minnesota N212 Regional Control Project (Rcp) as a Working Example.

Due to the highly transmissible nature of porcine reproductive and respiratory syndrome (PRRS), implementation of regional programs to control the disease may be critical. Because PRRS is not reported in the US, numerous voluntary regional control projects (RCPs) have been established. However, the effect of RCPs on PRRS control has not been assessed yet. This study aims to quantify the extent to which RCPs contribute to PRRS control by proposing a methodological framework to evaluate the progress of RCPs. Information collected between July 2012 and June 2015 from the Minnesota Voluntary Regional PRRS Elimination Project (RCP-N212) was used. Demography of premises (e.g. composition of farms with sows = SS and without sows = NSS) was assessed by a repeated analysis of variance. By using general linear mixed-effects models, active participation of farms enrolled in the RCP-N212, defined as the decision to share (or not to share) PRRS status, was evaluated and used as a predictor, along with other variables, to assess the PRRS trend over time. Additionally, spatial and temporal patterns of farmers' participation and the disease dynamics were investigated. The number of farms enrolled in RCP-N212 and its geographical coverage increased, but the proportion of SS and NSS did not vary significantly over time. A significant increasing (p<0.001) trend in farmers' decision to share PRRS status was observed, but with NSS producers less willing to report and a large variability between counties. The incidence of PRRS significantly (p<0.001) decreased, showing a negative correlation between degree of participation and occurrence of PRRS (p<0.001) and a positive correlation with farm density at the county level (p = 0.02). Despite a noted decrease in PRRS, significant spatio-temporal patterns of incidence of the disease over 3-weeks and 3-kms during the entire study period were identified. This study established a systematic approach to quantify the effect of RCPs on PRRS control. Despite an increase in number of farms enrolled in the RCP-N212, active participation is not ensured. By evaluating the effect of participation on the occurrence of PRRS, the value of sharing information among producers may be demonstrated, in turn justifying the existence of RCPs

The Economics of Reducing Package Size: Consumer response and returns to manufacturers.

Marketing, Production Economics,

Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available

Influences of thermal environment on fish growth

Indexación: Scopus.Thermoregulation in ectothermic animals is influenced by the ability to effectively respond to thermal variations. While it is known that ectotherms are affected by thermal changes, it remains unknown whether physiological and/or metabolic traits are impacted by modifications to the thermal environment. Our research provides key evidence that fish ectotherms are highly influenced by thermal variability during development, which leads to important modifications at several metabolic levels (e.g., growth trajectories, microstructural alterations, muscle injuries, and molecular mechanisms). In Atlantic salmon (Salmo salar), a wide thermal range (ΔT 6.4°C) during development (posthatch larvae to juveniles) was associated with increases in key thermal performance measures for survival and growth trajectory. Other metabolic traits were also significantly influenced, such as size, muscle cellularity, and molecular growth regulators possibly affected by adaptive processes. In contrast, a restricted thermal range (ΔT 1.4°C) was detrimental to growth, survival, and cellular microstructure as muscle growth could not keep pace with increased metabolic demands. These findings provide a possible basic explanation for the effects of thermal environment during growth. In conclusion, our results highlight the key role of thermal range amplitude on survival and on interactions with major metabolism-regulating processes that have positive adaptive effects for organisms.http://onlinelibrary.wiley.com/doi/10.1002/ece3.3239/ful

ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction

International audienceLinear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. Here, we apply Isomap, one such technique, to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures, comparing observational data with simulations from a number of current coupled atmosphere-ocean general circulation models. We use Isomap to examine El Niño variability in the different datasets and assess the suitability of the Isomap approach for climate data analysis. We conclude that, for the application presented here, analysis using Isomap does not provide additional information beyond that already provided by principal component analysis