A rapid and sensitive method for the speciation and quantification of glucosinolates in rapeseed is described. The method combines liquid chromatography (LC) with ion trap mass spectrometry (ITMS) detection. Electrospray ionization (ESI) has been chosen as the ionization technique for the on-line coupling of LC with ITMS. Glucosinolates are extracted from different rapeseeds with MeOH and the extracts are cleaned-up by solid phase extraction with Florisil cartridges. Aqueous extracts are injected into LC system coupled to an ITMS, leading to accurately quantify eight of the most important glucosinolates in rapeseed, by MS2 mode and confirming their structure by MS3 acquisition. All the glucosinolates found in rapeseeds provide good signals corresponding to the deprotonated precursor ion [M-H]-. The method is reliable and reproducible, and detection limits range from 0.5 nmol g(-1) to 3.7 nmol g(-1) when 200 mg of dried seeds of certified reference material are analyzed. Within-day and between-day RSD percentages range between 2.4-14.1% and 3.9-16.9%, respectively. The LC-ESI-ITMS-MS method described here allows for a rapid assessment of these metabolites in rapeseed without a desulfatation step. The overall process has been successfully applied to identify and quantify glucosinolates in rapeseed samples.

Minimum NNI (Nitrogen Nutrition Index) values have been developed for each key growing stage of wheat (Triticum aestivum) to achieve high grain yields and grain protein content (GPC). However, the determination of NNI is time-consuming. This study aimed to (i) determine if the NNI can be predicted using the proximal sensing tools RapidScan CS-45 (NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge)) and Yara N-TesterTM and if a single model for several growing stages could be used to predict the NNI (or if growing stage-specific models would be necessary); (ii) to determine if yield and GPC can be predicted using both tools; and (iii) to determine if the predictions are improved using normalized values rather than absolute values. Field trials were established for three consecutive growing seasons where different N fertilization doses were applied. The tools were applied during stem elongation, leaf-flag emergence, and mid-flowering. In the same stages, the plant biomass was sampled, N was analyzed, and the NNI was calculated. The NDVI was able to estimate the NNI with a single model for all growing stages (R2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values (R2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.

It is difficult to predict the crop-available nitrogen (N) from farmyard manures applied to soil. The aim of this study was to assess the usefulness of the proximal sensors, Yara N-TesterTM and RapidScan CS-45, for diagnosing the N nutritional status of wheat after the application of manures at sowing. Three annual field trials were established (2014–2015, 2015–2016 and 2016–2017) with three types of fertilizer treatments: dairy slurry (40 t ha−1 before sowing), sheep manure (40 t ha−1 before sowing) and conventional treatment (40 kg N ha−1 at tillering). For each treatment, five different mineral N fertilization doses were applied at stem elongation: 0, 40, 80, 120, and 160 kg N ha−1. The proximal sensing tools were used at stem elongation before the application of mineral N. Normalized values of the proximal sensing look promising for adjusting mineral N application rates at stem elongation. For dairy slurry, when either proximal sensor readings were 60–65% of the reference plants with non-limiting N, the optimum N rate for maximizing yield was 118–128 kg N ha−1. When the readings were 85–90%, the optimum N rate dropped to 100–110 kg N ha−1 for both dairy slurry and conventional treatments. It was difficult to find a clear relationship between sensor readings and yield for sheep manure treatments. Measurements taken with RapidScan C-45 were less time consuming and better represent the spatial variation, as they are taken on the plant canopy. Routine measurements throughout the growing season are particularly needed in climates with variable rainfall. The application of 40 kg N ha−1 at the end of winter is necessary to ensure an optimal N status from the beginning of wheat crop development. These research findings could be used in applicator-mounted sensors to make variable-rate N applications.

No abstract