Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
Abstract
Grassland farmers face ever increasing demands on their production systems and the quality of their grassland yields. Estimating pasture quality using traditional field methods is limited as it is time consuming and costly, and requires some destructive sampling. The field of remote sensing offers alternative tools and techniques to overcome some of the limitations and thereby help farmers to receive spatial continuous and near real-time information about grassland quality parameters. This review gives an overview about recent developments in the remote sensing-based estimation of three aspects of grassland quality: feed quality, biological nitrogen fixation by legumes, and the identification of unwanted plant species.
Abstract
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.
Abstract
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.
Authors
Xinbing Wang Yuxin Miao Rui Dong Hainie Zha Tingting Xia Zhichao Chen Krzysztof Kusnierek Guohua Mi Hong Sun Minzan LiAbstract
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.
Abstract
No abstract has been registered
Abstract
This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central Norway. All images were subject to radiometric and geometric corrections before being processed to ortho-images, carrying canopy reflectance information. The data (n = 707) was split in two, using half the data for model calibration and the remaining half for validation. Several powered partial least squares regression (PPLSR) models were fitted to the reflectance data to estimate fresh (FM) and dry matter (DM) yields, as well as crude protein (CP), dry matter digestibility (DMD), neutral detergent fibre (NDF), and indigestible neutral detergent fibre (iNDF) content. Prediction performance of these models was compared with the prediction performance of simple linear regression (SLR) models, which were based on selected vegetation indices and plant height. The highest prediction accuracies for general models, based on the pooled data, were achieved by means of PPLSR, with relative root-mean-square errors of validation of 14.2% (2550 kg FM ha−1), 15.2% (555 kg DM ha−1), 11.7% (1.32 g CP 100 g−1 DM), 2.4% (1.71 g DMD 100 g−1 DM), 4.8% (2.72 g NDF 100 g−1 DM), and 12.8% (1.32 g iNDF 100 g−1 DM) for the prediction of FM, DM, CP, DMD, NDF, and iNDF content, respectively. None of the tested SLR models achieved acceptable prediction accuracies.
Abstract
Horticultural production systems are under pressure to find environmentally friendly growing media. Peat is currently the most popular substrate for fresh potted herbs production; however, this raw material is not sustainable due to the large amount of greenhouse gases released during its harvesting. Therefore, the goal of the study was to test the performance of various commercial wood fiber products and compare them with peat and coir in an ebb-and-flow production system with basil (Ocimum basilicum L. 'Marian'). Basil plants were grown in three different pot sizes (6, 9 and 12 cm in diameter) and under various fertigation regimes (EC 1, 2 and 3). Height and biomass of the plants were recorded when the best performing plants reached the commercial stage. The tallest plants and greatest biomass were produced in peat and coir, however, the results confirm that wood fiber can be a promising substrate alternative. Further research is needed to study, among others topics, how to modify some properties of wood fibers to fulfil their potential as a replacement for non-sustainable growing media in production of herbs in pots.
Abstract
No abstract has been registered
Abstract
This paper describes a tool that enables farmers to time harvests and target nitrogen (N) inputs in their forage production, according to the prevailing yield potential. Based on an existing grass growth model for forage yield estimation, a more detailed process-based model was developed, including a new nitrogen module. The model was tested using data from an experiment conducted in a grassland-rich region in central Norway and showed promising accuracy with estimated root mean square error (RMSE) of 50 and 130 g m-2 for dry matter yield in the trial. Three parameters were detected as highly sensitive to model output: initial value of organic N in the soil, fraction of humus in the initial organic N in the soil, and fraction of decomposed N mineralized. By varying these parameters within a range from 0.5 to 1.5 of their respective initial value, most of the within-field variation was captured. In a future step, remotely sensed information on model output will be included, and in-season model correction will be performed through re-calibration of the highly sensitive parameters.
Authors
Tomasz Leszek Woznicki Krzysztof Kusnierek Unni Myrheim Roos Sofie Andersen Katrin Zimmer Anita SønstebyAbstract
The use of peat as a growing media in horticulture is supposed to be reduced due to negative effects of its production on the environment. Interest in development of alternative growing media is therefore increasing and is enhanced by both political pressure and industry demands. Therefore, the influence of 33 growing media on the performance and productivity of two strawberry cultivars were examined in a polytunnel under Nordic conditions (60.7 N). Alternative substrates including fibers of spruce, birch and flax and coffee grounds were tested standalone or in mixes. Peat and coir were included as controls. Additionally, impregnation of the wood fibers with organic and inorganic substances was examined. All investigated growing media received identical fertigation strategies (EC 1.5). The highest average biomass production was observed for plants grown in bare peat; however, the best yield performance was noted for peat mixed with perlite and for coarse spruce fiber. Strawberries grown in these two best performing substrates showed comparable overall productivity, with 272 and 268 g of berries per plant, respectively. Both peat/perlite mix and the coarse spruce fiber had also a similar weight of berries larger than 25 mm, with 210 and 198 g plant-1, respectively. Moreover, improvement of the substrate structure by adding perlite or wood chips may have had a pronounced effect on fruiting performance. When compared to peat with added perlite (which gave the highest berry yield in the experiment; 272 g plant-1), strawberries grown in pure peat produced only 187 g plant-1. Furthermore, impregnation of spruce fiber with humic acid enhanced fruiting performance by increasing the total yield and number of large berries (≥25 mm). Future prospects for this study include establishment of an optimal structure of spruce fiber substrate suitable for strawberry production and development of the fertigation strategy optimized for the new growing media.