Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2021
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.
Sammendrag
One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.
Forfattere
Xinbing Wang Yuxin Miao Rui Dong Hainie Zha Tingting Xia Zhichao Chen Krzysztof Kusnierek Guohua Mi Hong Sun Minzan LiSammendrag
Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.
Forfattere
Stefano Zanotto Anna Palmé Áslaug Helgadóttir Kristin Daugstad Mika Isolahti Linda Öhlund Petter Marum Maria Ahlin Moen Merja Veteläinen Odd Arne Rognli Åshild ErgonSammendrag
Red clover (Trifolium pratense L.) is the most important forage legume in the Nordic region, but its utilization is limited by poor persistency. The improvement of cultivated red clover can potentially take advantage of the numerous wild populations and landraces conserved in gene banks; however, there is often limited information available on the phenotypic and genetic characteristics of this material. We characterized 48 populations conserved at NordGen for a number of traits and compared them to commercial cultivars. The material was evaluated in field trials at four locations over two years and in an experiment under controlled conditions. Considerable variation was identified, with stem length, growth type and flowering date having the highest broad sense heritabilities. Traits related to plant size were strongly associated with late flowering and upright growth and differed between landraces/cultivars on the one hand and wild populations on the other. There was a large genotype by environment interaction on winter survival, which only partially correlated with freezing tolerance under controlled conditions. A majority of gene bank accessions exceeded the commercial cultivars in winter survival and freezing tolerance and can therefore be a genetic resource for future improvement of these traits. The phenotypic variation among accessions was associated with two main axes of climatic variation at the collection site. Petiole length of young plants under controlled conditions as well as plant size in the field increased with increasing summer temperature and decreasing summer precipitation, while number of leaves and an apparent vernalization requirement, recorded under controlled conditions, increased with decreasing annual and winter temperature. We discuss the implications these results have for collection, conservation and utilization of red clover genetic resources in the Nordic region.
Sammendrag
Seaweeds are increasingly used in European cuisine. Until the recent use of molecular techniques, species identification was solely based on morphology which cannot easily discriminate morphologically simple but phenotypically plastic taxa such as the green algal genus Ulva. For example, current taxonomic protocol effectively reassigned the previously known European ʻUlva lactuca L.’ under the name Ulva fenestrata Postels & Ruprecht. Also, the presumptive Ulva lactuca approved by the Institute for Reference Materials and Measurements (IRMM, Joint Research Center, European Commission) as Certified Reference Material (CRM) for analytical quality assurance was genetically identified as U. rigida C.Agardh. It is very likely that different Ulva species under various names have been consumed as food not only in Europe, but also worldwide. In this regard, when chemical composition and nutritional quality of different seaweed species meet a set of food standard criteria, and food safety hazards are mitigated, they should be endorsed for consumption. In the case of Ulva, we propose that different bladed and tubular species should generally be accepted for food consumption in Europe.
Sammendrag
Macrocystis pyrifera is a major habitat forming kelp in coastal ecosystems of temperate regions of the northern and southern hemispheres. We investigated the seasonal occurrence of adult sporophytes, morphological characteristics, and reproductive phenology at two sites within a wave-protected harbour and two wave-exposed sites in southern New Zealand every 3–4 months between 2012 and 2013. Seasonality in reproduction was assessed via the number of sporophylls, the occurrence of sori on sporophylls, and non-sporophyllous laminae (fertile pneumatocyst-bearing blades and fertile apical scimitars), meiospore release, and germination. We found that M. pyrifera was present and reproductive year-round in three of the four sites, and patterns were similar for the wave-exposure conditions. Sori were found on pneumatocyst-bearing blades and apical scimitars in addition to the sporophylls, and viable meiospores were released from all three types of laminae. Morphological variations between sites with different wave exposure indicate that sporophytes from wave-protected sites have bigger blades and holdfasts and are longer than those from wave-exposed sites. We discuss the implications of these biological variables for the ecology of M. pyrifera inhabiting different wave exposure environments in southern New Zealand.
Sammendrag
Kelps are important foundation species in coastal ecosystems currently experiencing pronounced shifts in their distribution patterns caused by ocean warming. While some populations found at species’ warm distribution edges have been recently observed to decline, expansions of some species have been recorded at their cold distribution edges. Reduced population resilience can contribute to kelp habitat loss, hence, understanding intraspecific variations in physiological responses across a species’ latitudinal distribution is crucial for its conservation. To investigate potential local responses of the broadly distributed kelp Saccharina latissima to marine heatwaves in summer, we collected sporophytes from five locations in Europe (Spitsbergen, Bodø, Bergen, Helgoland, Locmariaquer), including populations exposed to the coldest and warmest local temperature regimes. Meristematic tissue from sporophytes was subjected to increasing temperatures of 1+2, 1+4 and 1+6 ◦C above the respective mean summer temperatures (control, 1±0 ◦C) characteristic for each site. Survival and corresponding physiological and biochemical traits were analyzed. Vitality (optimum quantum yield, Fv/Fm) and growth were monitored over time and biochemical responses were measured at the end of the experiment. Growth was highest in northern and lowest in southern populations. Overall, northern populations from Spitsbergen, Bodø and Bergen were largely unaffected by increasing summer temperatures up to 1+6 ◦C. Conversely, sporophytes from Helgoland and Locmariaquer were markedly stressed at 1+6 ◦C: occurrence of tissue necrosis, reduced Fv/Fm, and a significantly elevated de-epoxidation state of the xanthophyll cycle (DPS). The variations in phlorotannins, mannitol and tissue C and N contents were independent of temperature treatments and latitudinal distribution pattern. Pronounced site-specific variability in response to increasing temperatures implies that exceeding a threshold above the mean summer temperature exclusively affect rear-edge (southernmost) populations.
Forfattere
Ingunn M. VågenSammendrag
Det er ikke registrert sammendrag
Forfattere
Ingunn M. VågenSammendrag
Det er ikke registrert sammendrag