Modellering ruimtelijke lichtverdeling in gewassen: Opbouw en toepassing van een 3D model voor kas en gewas

Een 3D model voor lichtverdeling in kasgewassen is ontwikkeld om de meest efficiënte plaatsing van lampen (SONT, LED) te berekenen om hiermee op groeilicht en energie te kunnen besparen. Het onderzoek, in het kader van het programma Kas als Energiebron en gefinancierd door Productschap Tuinbouw en Ministerie van ELI, behelsde de bouw en test van het model, dat rekening houdt met lampposities en -eigenschappen, bladstanden en rijstructuur. De lichtabsorptie en gewasfotosynthese voor een ingevoerd lichtplan wordt gesimuleerd. In het rapport zijn een reeks kansrijke belichtingsscenario’s doorgerekend voor een representatieve gewasstructuur voor tomaat en roos. Het resultaat bleek sterk afhankelijk van padbreedte en aantal bladeren, maar minder van bladvorm en bladhoek. De belichting wordt efficiënter bij gerichtere plantbelichting door aanpassing van de lampreflector, gebruik van tussenbelichting en schermreflectie. Het lichtverlies naar vloer en kasdek worden hiermee gereduceerd. Voor vragen uit de sector is het 3D model nu op verzoek inzetbaar

Ingested water equilibrates isotopically with the body water pool of a shorebird with unrivaled water fluxes

We investigated the applicability of H-2 to measure the amount of body water (TBW) and water fluxes in relation to diet type and level of food intake in a mollusk-eating shorebird, the Red Knot (Calidris canutus). Six birds were exposed to eight experimental indoor conditions. Average fractional H-2 turnover rates ranged between 0.182 day(-1) (SD = 0.0219) for fasting birds and 7.759 day(-1) (SD = 0.4535) for birds feeding on cockles (Cerastoderma edule). Average TBW estimates obtained with the plateau method were within the narrow range of 75.9-85.4 g (or between 64.6 and 70.1% of the body mass). Those obtained with the extrapolation method showed strong day-to-day variations (range 55.7-83.7 g, or between 49.7 and 65.5%). Average difference between the two calculation methods ranged between 0.6% and 36.3%, and this difference was strongly negatively correlated with water flux rate. Average water influx rates ranged between 15.5 g/day (fasting) and 624.5 g/day (feeding on cockles). The latter value is at 26.6 times the allometrically predicted value and is the highest reported to date. Differences in H-2 concentrations between the blood and feces (i.e., biological fractionation) were small but significant (-3.4% when fed a pellet diet, and -1.1% for all the other diets), and did not relate to the rate of water flux (chi (2)(1) = 0.058, P &lt;0.81). We conclude that the ingested water equilibrated rapidly with the body water pool even in an avian species that shows record water flux rates when living on ingested marine bivalves.</p

Carrying large fuel loads during sustained bird flight is cheaper than expected

Birds on migration alternate between consuming fuel stores during flights and accumulating fuel stores during stopovers. The optimal timing and length of flights and stopovers for successful migration depend heavily on the extra metabolic power input (fuel use) required to carry the fuel stores during flight(1,2). The effect of large fuel loads on metabolic power input has never been empirically determined. We measured the total metabolic power input of a long-distance migrant, the red knot (Calidris canutus), flying for 6 to 10 h in a wind tunnel, using the doubly labelled water technique(3). Here we show that total metabolic power input increased with fuel load, but proportionally less than the predicted mechanical power output from the flight muscles. The most likely explanation is that the efficiency with which metabolic power input is converted into mechanical output by the flight muscles increases with fuel load. This will influence current models of bird flight and bird migration. It may also help to explain why some shorebirds, despite the high metabolic power input required to fly, routinely make nonstop flights of 4,000 km longer(4).</p

Water and heat balance during flight in the Rose coloured starling (Sturnus roseus, Linneus).

Water imbalance during flight is considered to be a potentially limiting factor for flight ranges in migrating birds, but empirical data are scarce. We studied flights under controlled ambient conditions with rose-colored starlings in a wind tunnel. In one experiment, we measured water fluxes with stable isotopes at a range of flight speeds (9-14 m s(-1)) at constant temperature (15 degrees C). In a second experiment, we measured evaporation rates at variable ambient temperatures (T-a = 5 degrees-27 degrees C) but constant speed (12 m s(-1)). During all flights, the birds experienced a net water loss. On average, water influx was 0.98 g h(-1) (SD = 0.16; n = 8), and water efflux was 1.29 g h(-1) (SD = 0.16; n = 8) irrespective of flight speed. Evaporation was related to temperature in a biphasic pattern. At temperatures below 18.2 degrees C, net evaporation was constant at 0.36 g h(-1) (SD = 0.14; n = 8)), rising at higher temperatures with a slope of 0.11 per degree to about 1.5 g h(-1) at 27 degrees C. We calculated the relative proportion of dry and evaporative heat loss during flight. Evaporative heat loss at T-a < 18.2 degrees C was 14% of total a heat production during flight, and dry heat loss accounted for 84%. At higher temperatures, evaporative heat loss increased linearly with T-a to about 25% at 27 degrees C. Our data suggest that for prolonged flights, rose-colored starlings should adopt behavioral water-saving strategies and that they cannot complete their annual migration without stopovers to replenish their water reserves

Determining the spike–wave index using automated detection software

Purpose: The spike–wave index (SWI) is a key feature in thediagnosis of electrical status epilepticus during slow-wave sleep.Estimating the SWI manually is time-consuming and is subject tointerrater and intrarater variability. Use of automated detectionsoftware would save time. Thereby, this software willconsistently detect a certain EEG phenomenon as epileptiformand is not influenced by human factors. To determinenoninferiority in calculating the SWI, we compared theperformance of a commercially available spike detectionalgorithm (P13 software, Persyst Development Corporation,San Diego, CA) with human expert consensus.Methods: The authors identified all prolonged EEG recordingsfor the diagnosis or follow-up of electrical status epilepticusduring slow-wave sleep carried out from January to December2018 at an epilepsy tertiary referral center. The SWI during thefirst 10 minutes of sleep was estimated by consensus of twohuman experts. This was compared with the SWI calculated bythe automated spike detection algorithm using the threeavailable sensitivity settings: “low,” “medium,” and “high.” In thesoftware, these sensitivity settings are denoted as perceptionvalues.Results: Forty-eight EEG recordings from 44 individuals wereanalyzed. The SWIs estimated by human experts did not differfrom the SWIs calculated by the automated spike detectionalgorithm in the “low” perception mode (P ¼ 0.67). The SWIscalculated in the “medium” and “high” perception settings were,however, significantly higher than the human expert estimatedSWIs (both P , 0.001).Conclusions: Automated spike detection (P13) is a useful tool indetermining SWI, especially when using the “low” sensitivitysetting. Using such automated detection tools may save time,especially when reviewing larger epochs.</p