Ten barley cultivars, including covered and naked types, varying in their content of total starch, amylose, protein and β‐glucan, were grown in different years, at various locations and nitrogen fertilization rates. The barley cultivars showed a large variation in yield (3250–6690 kg ha−1), thousand kernel weight (40–50 g), bulk weight (660–815 g litre−1), starch (51–67% of DM), protein (8–15% of DM), β‐glucan (3·5–5·9% of DM) and ash (1·9–2·5% of DM). The naked cultivar SW 8775 had the highest starch content and the high amylopectin cultivar (SW 7142‐92, waxy) had the lowest. The waxy starch barleys had the highest protein content, while the normal starch barleys (Golf and Lina) had the lowest protein content at each N‐rate. Higher content of β‐glucan was found in the waxy cultivars (5·0–5·9% of DM) and high amylose cultivars (5·6–5·9% of DM) than in the other barley cultivars (3·5–4·8% of DM). The results were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA), and both confirmed that nitrogen fertilization rate was of great importance for the protein content. PCA revealed that protein and β‐glucan were negatively correlated with starch and yield. The ANOVA showed that the cultivar factor was significant for all variables, but especially for the β‐glucan content, which responded differently to the nitrogen fertilization rate during the 2 years. In this study, environmental effects were generally of less importance than the barley cultivars for the different variables. © 1998 Society of Chemical Industry.

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Key words: NIR, nitrogen mineralisation, precision agriculture AbstractAdjusting fertiliser applications to within-field variations in nitrogen (N) mineralisation during the growing season can increase yields, improve crop quality, reduce costs and decrease nutrient losses to the environment. Predicting such variations at a reasonable cost is therefore important. In a three-year study, Near Infrared Reflectance (NIR) spectroscopy was compared with soil organic matter (SOM) and clay content as predictors of plant N uptake using cross-validated PLS (Partial Least Squares) regression models. Plant N uptake was measured as total nitrogen in aboveground plant parts at harvest, in plots without N fertilisation within three different fields in southern Sweden. NIR spectroscopy and combined clay and SOM content resulted in equally good estimations of plant N uptake in fields with large variation in SOM content. Cross-validated NIR calibrations for plant N uptake within fields for separate years resulted in r 2 values of 0.75-0.85 and average cross-validation errors of 11-16 kg N ha -1 for two fields (one year excluded at one field because of farmyard manure application). No significant improvements were seen when NIR-spectra, clay and SOM were included in the same model, suggesting that the additional predictive capacity of NIR over SOM relates to soil texture variations. NIR calibrations also performed poorly in one field where plant N uptake could not be explained by SOM or clay content. Predictions within fields between years produced r 2 values of 0.54-0.92 and prediction errors of 8-20 kg N ha -1 for one field. These results confirm that N uptake prediction accuracy can be improved by using NIR spectroscopy in fields with large SOM variations. However, good estimations could not be made between fields, indicating difficulties in creating more general calibration models for large geographical areas. IntroductionConsiderable amounts of N can be mineralised during the growing season and the variation between and within fields can be significant (Börjesson et al. 1999;Delin and Lindén 2002). Adjusting fertiliser applications with respect to this variation can therefore both reduce costs and decrease nutrient losses to the environment. In precision agriculture, global positioning system (GPS) technology and the development of geographical information systems (GIS) together with variable rate technology (VRT) have made it possible for farmers to adjust crop inputs as they drive through the fields. This has increased the demand for information at a high spatial resolution. However, increasing the amount of conventional methods of laboratory analyses to meet these demands fast becomes too costly and time-consuming.

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