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
Potato contributes highly to the global food security. It is predicted that the production of this crop will be negatively affected by future climatic changes. Application of hydroponics for table potato production can mitigate crop loss in highly vulnerable regions. A preliminary small-scale case-study was performed to test theoretical perspectives of hydroponic production of table potatoes in wood fiber by comparing different fiber types and fertigation strategies. Potatoes were also grown in the field to obtain a reference control. Hydroponic production of potato in a stand-alone wood fiber resulted in ca. 200% higher yield, when compared to standard soil cultivation. However, the quality of the tubers was slightly reduced (lower dry matter content). Productivity of table potatoes was affected by the growing medium and fertigation strategy. Production of potatoes in wood fiber is possible and, in the future, might complement the conventional production systems, or even become an important alternative in locations where in-field cultivation is not possible. Nevertheless, the effect of wood fiber properties and the applied fertigation strategy on yield potential and tuber quality should be further studied. Optimization of these factors will be a topic of a following full-scale research.
Abstract
No abstract has been registered
Abstract
Hydroponic production of strawberry (Fragaria × ananassa Duch.) in protected cultivation systems using substrates (growing media) is gaining popularity worldwide. Therefore, it is necessary to develop more sustainable growing media alternatives. This study focused on growth performance of strawberry plants grown in wood fibre from Norway spruce (Picea abies (L.) H. Karst.), in comparison to two industry standards (peat and coco fibres). Plug (tray) plants of the June-bearing strawberry cultivar 'Malling Centenary' and bare root (WBH) plants of cultivar 'Sonata' were transplanted into three different growing media: peat (80%) and perlite (20%) mixture, coconut coir (100%) and Norway spruce wood fibre (100%). The plants received four fertigation strategies (various potassium and nitrogen concentrations) from flowering onwards. Throughout the production season ripe berries were harvested and frozen for later analyses of chemical composition. Plant architecture was also recorded after termination of the experiment. The results revealed that the most significant differences among the majority of the fruit and plant parameters were due to cultivar traits. Strawberries grown in wood fibre produced slightly smaller berries with elevated °Brix and dry matter compared to berries from plants grown in peat and coir. This was most likely caused by the common fertigation strategy applied to all substrates. Nevertheless, among the tested fertigation strategies, application of solutions with elevated potassium resulted in the highest sugar accumulation in berries grown in wood fibre substrate. In general, the experiment revealed relatively negligible differences between the growing media, and we therefore conclude that wood fibre from Norway spruce may be a viable alternative as a growing media in hydroponic strawberry production when the fertigation strategy is precisely adjusted.
2020
Authors
Jakob GeipelAbstract
No abstract has been registered
Authors
Junjun Lu Yuxin Miao Wei Shi Jingxin Li Xiaoyi Hu Zhichao Chen Xinbing Wang Krzysztof KusnierekAbstract
RapidSCAN is a portable active canopy sensor with red, red-edge, and near infrared spectral bands. The objective of this study is to develop and evaluate a RapidSCAN sensor-based precision nitrogen (N) management (PNM) strategy for high-yielding rice in Northeast China. Six rice N rate experiments were conducted from 2014 to 2016 at Jiansanjiang Experiment Station of China Agricultural University in Northeast China. The results indicated that the sensor performed well for estimating rice yield potential (YP0) and yield response to additional N application (RIHarvest) at the stem elongation stage using normalized difference vegetation index (NDVI) (R2 = 0.60–0.77 and relative error (REr) = 6.2–8.0%) and at the heading stage using normalized difference red edge (NDRE) (R2 = 0.70–0.82 and REr = 7.3–8.7%). A new RapidSCAN sensor-based PNM strategy was developed that would make N recommendations at both stem elongation and heading growth stages, in contrast to previously developed strategy making N recommendation only at the stem elongation stage. This new PNM strategy could save 24% N fertilizers, and increase N use efficiencies by 29–35% as compared to Farmer N Management, without significantly affecting the rice grain yield and economic returns. Compared with regional optimum N management, the new PNM strategy increased 4% grain yield, 3–10% N use efficiencies and 148 $ ha−1 economic returns across years and varieties. It is concluded that the new RapidSCAN sensor-based PNM strategy with two in-season N recommendations using NDVI and NDRE is suitable for guiding in-season N management in high-yield rice management systems. Future studies are needed to evaluate this RapidSCAN sensor-based PNM strategy under diverse on-farm conditions, as well as to integrate it into high-yield rice management systems for food security and sustainable development.
Abstract
The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.
Authors
Hainie Zha Yuxin Miao Tiantian Wang Yue Li Jing Zhang Weichao Sun Zhengqi Feng Krzysztof KusnierekAbstract
Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
Authors
Jiangsan Zhao Dmitry Kechasov Boris Rewald Gernot Bodner Michel Verheul Nicholas Clarke Jihong Liu ClarkeAbstract
Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
2019
Authors
Jakob GeipelAbstract
No abstract has been registered
Authors
Jakob GeipelAbstract
No abstract has been registered