Plant photosynthesis and biomass production are associated with the amount of intercepted light, especially the light distribution inside the canopy. Three virtual canopies (n = 80, 3.25 plants/m2) were constructed based on average leaf size of the digitized plant structures: ‘small leaf’ (98.1 cm2), ‘medium leaf’ (163.0 cm2) and ‘big leaf’ (241.6 cm2). The ratios of diffuse light were set in three gradients (27.8%, 48.7%, 89.6%). The simulations of light interception were conducted under different ratios of diffuse light, before and after the normalization of incident radiation. With 226.1% more diffuse light, the result of light interception could increase by 34.4%. However, the 56.8% of reduced radiation caused by the increased proportion of diffuse light inhibited the advantage of diffuse light in terms of a 26.8% reduction in light interception. The big-leaf canopy had more mutual shading effects, but its larger leaf area intercepted 56.2% more light than the small-leaf canopy under the same light conditions. The small-leaf canopy showed higher efficiency in light penetration and higher light interception per unit of leaf area. The study implied the 3D structural model, an effective tool for quantitative analysis of the interaction between light and plant canopy structure.
MULTIFILE
Rationale: A higher protein intake is suggested to preserve muscle mass during aging, and may therefore reduce the risk for sarcopenia. We explored whether the amount, type (animal/vegetable) and essential amino acid (EAA) composition of protein intake were associated with 5-year change in mid-thigh muscle cross-sectional area (CSA) in older adults.Methods: Protein intake was assessed at year 2 by a Block food frequency questionnaire in 2,597 participants of the Health ABC study, aged 70–79 y. At year 1 and year 6 mid-thigh muscle CSA (cm2) was measured by computed tomography. Multiple linear regression analysis was used to examine the association between energy adjusted protein residuals (total, animal and vegetable protein) and muscle CSA at year 6, adjusted for muscle CSA at year 1 and potential confounders including prevalent health conditions, physical activity and 5-year change in fat mass. EAAintake was expressed as percentage of total protein intake.Results: Mean protein intake was 0.90 (SD 0.36) g/kg/d and mean 5-year change in muscle CSA was −9.8 (17.0) cm2 (n = 1,561). No association was observed between energy adjusted total (β = −0.00 cm2 ; SE = 0.03; P = 0.98), animal (β = −0.00 cm2; SE = 0.03; P = 0.92), and plant (β = +0.07 cm2; SE = 0.07; P = 0.291) protein intake and muscle CSA at year 6, adjusted for baseline mid-thigh muscle area and potential confounders. No associations were observed for the EAAs.Conclusion: A higher total, animal or vegetable protein intake was not associated with 5 year change in mid-thigh cross sectional area in older adults. This conclusion contradicts some, but not all previous research, therefore optimal protein intake for older adults is currently not known.
Background: A higher protein intake is suggested to preserve muscle mass during aging and may therefore reduce the risk of sarcopenia.Objectives: We explored whether the amount and type (animal or vegetable) of protein intake were associated with 5-y change in mid-thigh muscle cross-sectional area (CSA) in older adults (n = 1561).Methods: Protein intake was assessed at year 2 by a Block foodfrequency questionnaire in participants (aged 70–79 y) of the Health, Aging, and Body Composition (Health ABC) Study, a prospective cohort study. At year 1 and year 6 mid-thigh muscle CSA in square centimeters was measured by computed tomography. Multiple linearregression analysis was used to examine the association between energy-adjusted protein residuals in grams per day (total, animal, and vegetable protein) and muscle CSA at year 6, adjusted for muscle CSA at year 1 and potential confounders including prevalent health conditions, physical activity, and 5-y change in fat mass.Results: Mean (95% CI) protein intake was 0.90 (0.88, 0.92) g ·kg–1 · d–1 and mean (95% CI) 5-y change in muscle CSA was −9.8 (−10.6, −8.9) cm2. No association was observed between energyadjusted total (β = −0.00; 95% CI: −0.06, 0.06 cm2; P = 0.982), animal (β = −0.00; 95% CI: −0.06, 0.05 cm2; P = 0.923), or plant(β = +0.07; 95% CI: −0.06, 0.21 cm2; P = 0.276) protein intake and muscle CSA at year 6, adjusted for baseline mid-thigh muscle CSA and potential confounders.Conclusions: This study suggests that a higher total, animal, or vegetable protein intake is not associated with 5-y change in midthigh muscle CSA in older adults. This conclusion contradicts some, but not all, previous research. This trial was registered at www.trialregister.nl as NTR6930.
Nederlandse glastuinbouwbedrijven, onderzoekers en technologie spelen een grote rol in de voedselvoorziening wereldwijd. De productiviteit ligt hier door de kennis en kunde hoog, met een kleine footprint in vergelijking met producenten in andere landen. Met de huidige bevolkingsgroei en druk op veilige en duurzame voedselvoorziening in het achterhoofd, leveren onderzoekers en ondernemers een versterking van de glastuinbouwsector. De inzet van sensoren, data en data-analyse is gewenst om groei en opbrengst beter te monitoren, ziektes beter te bestrijden, en de footprint verder te verkleinen. Nederlandse telers zijn proeftuinen voor deze innovaties: zij experimenteren als eerste, om technologieën of methoden toe te kunnen passen en tegen lagere kosten meer te produceren. Innovatieagenda’s van betrokken topsectoren dragen sterk bij aan deze ontwikkelingen. Dit project stelt data over de plant centraal. Nu heeft een teler data over zijn klimaat, hij of zij ziet zelf iets met de plant gebeuren en past dan klimaat aan. Dit project zorgt voor meer data over de plant zelf, zodat de telers de teelt directer kunnen aansturen, met betere opbrengst en lagere kosten tot gevolg. In dit project wil het consortium van onderzoekers en ondernemers een grote stap zetten naar grootschalige toepassing van sensortechnologie voor het volgen van gewasgroei. Daarvoor moeten te ontwikkelen sensoren zowel low-cost als nauwkeurig zijn. Daarnaast is draadloos en contactloos werken van groot belang. De belangrijkste te meten parameters zijn de kopdikte van het gewas en de Leaf Area Index. Beide parameters samen zeggen iets over de sapstroom en de sapstroom is de belangrijkste parameter voor de groei van het gewas. Dit project is een vliegwiel voor technologieontwikkeling. Resultaten van het onderzoek en de ontwikkeling, met toeleveranciers, kwekers en veredelaars samen, kunnen na dit proeftuin-stadium de technologie verder brengen, vooral naar het buitenland, waar de vraag naar Nederlandse kennis en expertise alsmaar groter wordt.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
