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.
2023
Authors
Rui Dong Yuxin Miao Pete Berry Xinbing Wang Fei Yuan Krzysztof Kusnierek Chris Baker Mark SterlingAbstract
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications.
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Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.
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The resilience of global food security is a critical concern. Facing limited access to land and potential disruption of the food markets, alternative, scalable, and efficient production systems are needed as a complementary buffer for maintenance of food production integrity. The purpose of this study was to introduce an alternative hydroponic potato growing system where potatoes are grown in bare wood fiber as a growing medium. A system utilizing drip irrigation and plastic bags as containers was tested for three different types of wood fiber, two cultivars and two fertigation strategies. Implementation of the system resulted in ~300% higher tuber production when compared to the local conventional farming. Mineral composition of the tubers obtained from hydroponic system was similar to the composition of tubers grown in the field and revealed potential for biofortification. In addition, a fertigation strategy where the two application points were separated across the root zone resulted in tubers with dry matter content comparable to the potatoes grown in soil. The recyclability, reusability, and simplicity of this solution may encourage its application for improving security of food production in selected areas of the world as well as its utilization in urban agriculture.
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Wood modification using polyesterification of sorbitol and citric acid is a novel environmentally friendly strategy for wood protection improving its dimensional stability and acts against fungal deterioration. Inelastic Raman scattering is sensitive to the molecules of high polarizability and both lignocellulose and aliphatic esters formed during the treatment are polar. Therefore, in the present study, the quality control of the treatment using a handheld Raman spectrometer equipped with 830 nm laser is suggested as a rapid and reliable approach. Raman spectra from six wood modification levels (resulting in different weight percent gain, WPG) of three different wood species (Silver birch, Scots pine and Norway spruce) as well as three sample preparation strategies (intact, sanded and milled wood samples) were collected, and further analyzed using a chemometric method. Best performing models based on Powered Partial Least Squares Regression predicted the WPG level at R2 = 0.85, 0.95 and 0.98 for birch, pine and spruce, respectively. In addition, a clear separation between hard and soft wood species was also captured. Especially for softwood species, the sample preparation method affected the model accuracy, revealing the best performance in milled material. It is concluded that by using handheld Raman spectrometer it is possible to perform accurate quality control of wood modified by polyesterification of citric acid and sorbitol.
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A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.
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There is an increased interest in the hydroponic production of strawberries in protected cultivation systems, and it is, therefore, urgent to develop new, more sustainable growing media alternatives. This study investigated the physical properties of wood fiber produced from Norway spruce (Picea abies (L.) H. Karst.) and peat:wood fiber substrate blends as well as the performance of the wood fiber in comparison to the industry standards, i.e., peat and coconut coir in the cultivation of hydroponic strawberry. Tray plants of the June-bearing strawberry (Fragaria × ananassa Duch.) cultivar ‘Malling Centenary’ were transplanted into five different growing media: a peat (80%) and perlite (20%) mixture, stand-alone (100%) coconut coir and three stand-alone (100%) Norway spruce wood fiber substrates (including coarse textured fibers with compact and loose packing density and compacted fine-textured fibers). Ripe strawberries were harvested and registered throughout the production season. The overall marketable yield was comparable across all the tested growing media; however, after 4 weeks of harvest, both coarse wood fiber and fine wood fiber showed better fruiting performance than the peat-perlite mixture. A trend for earlier berry maturation was observed for all wood fiber-based substrates. Plant parameters recorded after the end of production showed that plant height, number of leaves, and biomass production were higher in coarse wood fiber than in the peat-perlite mixture. Moreover, plants grown in wood fiber-based substrates had less unripe berries and flowers not harvested in comparison to both the peat and coir treatments.