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.
2024
Forfattere
Habtamu AlemSammendrag
No abstract has been registered
Forfattere
Habtamu AlemSammendrag
No abstract has been registered
Sammendrag
Smallholder farmers are reverting to traditional production methods due to the high opportunity costs and unintended consequences of new technologies. This study focuses on row planting technology, which is labor-intensive and slow without mechanized operations. Data from 224 nonadopters and 237 adopters of row planting were analyzed using ordinary least squares and instrumental variable techniques. The results show varying effects of row planting on yield and income across different agroecological zones, with labor use being endogenously determined. Comparing labor requirements, we found that row planting demands an additional 14.79 man-hours per acre over broadcasting seeds. The current dissemination approach of sustainable intensification technologies such as row planting prioritizes yield-enhancing technologies over mechanization, hindering adoption. We propose integrating small-scale mechanization, such as small-scale planters and harvesters, to reduce labor hours and promote sustainable intensification technology adoption.
Forfattere
Habtamu AlemSammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Sammendrag
Increasing planting densities and nitrogen (N) application rates are two practices commonly used in high-yield maize (Zea mays L.) production systems to increase crop yield, but have resulted in lower N use efficiency, increased lodging, and negative environmental problems. Crop sensing-based precision N management (PNM) strategies have been developed to optimize maize yield, N use efficiency, and reduce environmental footprints, however, PNM strategies to balance grain yield and lodging risks are still very limited. The objectives of this study were to: (1) propose a N nutrition index (NNI)-based algorithm for in-season estimation of maize N demand; and (2) develop a sensor-based PNM strategy to balance grain yield and lodging risk for maize. Field experiments were conducted in Northeast China from 2017 to 2019, using a split-plot design with three planting densities (5.5, 7.0 and 8.5 plants m−2) as main plots and six N rates (0–300 kg ha−1) as subplots. Based on previous studies, a leaf fluorescence sensor Dualex 4 good for estimating plant N concentration and a canopy reflectance sensor Crop Circle ACS 430 good for estimating plant aboveground biomass were used to estimate maize NNI and predict lodging risk. Total N rates to achieve low lodging risk were determined based on wind velocity causing maize stalk lodging and historical actual natural wind speed, as well as the response of a lodging risk indicator (stem failure moment, Bs) to N supply. In-season side-dress N rates were determined based on theoretical amount of preplant N fertilizer estimated using NNI and a target total N rate. The final recommended sidedress N rates were adjusted based on the sensor-predicted lodging risk. The results indicated that NNI could be used for estimating the theoretical amount of preplant N fertilizer required to reach the current N status. It’s feasible to estimate maize side-dress N demand based on the difference of a target total N rate (to achieve an optimal grain yield or low lodging risk) and the current theoretical N supply. Total N rate to ensure low lodging risk was suggested to be adopted under low and medium planting densities. Medium planting density of 70,000 plants ha−1 matched with the corresponding optimal N rate would be recommended for the study area to balance economic return and lodging risk. In general, high planting density is not recommended because it has high lodging risk. More studies are needed to further improve the developed crop sensing-based PNM strategy with more site-years of data and multi-source data fusion using machine learning models for practical on-farm applications.
Sammendrag
Context Traditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions. Objectives This study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality. Methods Five years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models. Results The new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions. Conclusions In-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion. Implications The active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.
Forfattere
Lingwei Dong Yuxin Miao Xinbing Wang Krzysztof Kusnierek Hainie Zha Min Pan William D. BatchelorSammendrag
Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.
Forfattere
Ingunn Øvsthus Mitja Martelanc Alen Albreht Tatjana Radovanović Vukajlović Urban Česnik Branka Mozetič VodopivecSammendrag
Our investigation delves into the previously uncharted territory of cider composition from Norway. This study aimed to obtain an overview of the qualitative and quantitative compositions of general chemical parameters, polyphenols (individual and total expressed as gallic acids equivalents), selected esters, and selected C6-alcohols in ciders with the PDO label Cider from Hardanger. In total, 45 juice and cider samples from the fermentation process were collected from 10 cider producers in Hardanger in 2019, 2020, and 2021. Individual sugars, acids, ethanol, and 13 individual phenols were quantified using HPLC-UV/RI. Seven ethyl esters of fatty acids, four ethyl esters of branched fatty acids, ten acetate esters, two ethyl esters of hydroxycinnamic acids, and four C6-alcohols were quantified using HS-SPME-GC-MS. For samples of single cultivars (‘Aroma’, ‘Discovery’, ‘Gravenstein’, and ‘Summerred’), the sum of the measured individual polyphenols in the samples ranges, on average, from 79 to 289 mg L−1 (the lowest for ‘Summerred’ and highest for ‘Discovery’ and ‘Gravenstein’). Chlorogenic acid was the most abundant polyphenol in all samples. Ethyl butyrate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, ethyl isobutyrate, ethyl 2-methylbutyrate, isoamyl acetate, and hexanol were present at concentrations above the odour threshold and contributed to the fruity flavour of the Cider from Hardanger.
Forfattere
Ingunn Øvsthus M. Martelanc T. Radovanović Vukajlović M. Lesica L. Butinar B. Mozetič Vodopivec G. AntalickSammendrag
Norwegian apple ciders have recently gained attention at the levels of international competitiveness. Accordingly, a comparative study on the chemical composition of selected Norwegian and French apple ciders was conducted to gain knowledge on what ubiquitous chemical parameters make the Norwegian ciders different from ciders from well-established producing regions. A total of 43 ciders, 24 Norwegian and 19 French, in the category of acidic dominant ciders, were included in the study. Ethanol, individual sugars and organic acids, pH, total phenols, aroma compounds including esters, C6-alcohols, volatile phenols and terpenoids, were analysed. Norwegian ciders showed higher contents in ethanol, malic and citric acids, whereas total phenols, pH, glucose, and fructose were higher in French counterparts. Regarding the aromatic profile, no significant differences were observed for C6-alcohols. In contrast, differences were more expressed in the case of esters and volatile phenols. Norwegian ciders were characterised by higher average concentration for all the groups of esters, with the most important differences measured for higher alcohol acetates. Norwegian ciders also displayed higher contents of 4-vinylphenol and 4-vinylguaiacol while French ciders contained substantially higher levels of 4-ethylphenol and 4-ethylguaiacol. These results are in mutual correlation with the empirical observation reporting Norwegian apple ciders as more acidic, alcoholic and with lighter body but fruitier profile. Whereas French ciders are often perceived with more structure and animalistic profile.