1-mm particle sizes, and subjected to NIRS to measure quality parameters. Near-infrared spectroscopy can rap-Improving maize (Zea mays L.) forage yield and quality is a major idly measure multiple traits in food and agricultural goal for corn breeders in northern Europe. The objective of this research was to measure maize forage dry matter (DM) content and rope GmbH, Res. & Product Dev.,

Improving maize (Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate fi eld grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, signifi cantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specifi c calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation (SECV) and coeffi cient of determination (R 2 ) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction (SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R 2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R 2 and low biases among them, hence there was no need to apply specifi c standardisation algorithms. Moisture NIR spectroscopy determinations were signifi cantly more precise than those obtained using the reference method. Analysis of variance revealed low least signifi cant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

Oil yield and agronomic properties are the most important targets for canola breeders ( Brassica napus L.) in northern Europe. In order to enhance Pioneer's canola oil yield breeding efforts, oil content was measured with near infrared (NIR) spectroscopy directly on a harvester (NIR spectroscopy on-combine) and, in addition, moisture, protein and glucosinolate content were determined. NIR spectroscopy, coupled with rapid harvesting, can significantly improve the quality and speed of oil yield determinations so they occur within a very narrow harvest time window. Moisture, oil, protein and glucosinolate calibrations were developed with 449 samples from the 2004 to 2005 harvests, comprising spectra from four diode array NIR spectrometers mounted on-combine. Applying these calibrations to an independent dataset from the 2006 harvest resulted in standard errors of prediction ( SEP) and coefficients of determination ( r2) of 0.41% and 0.93 for moisture, 0.7% and 0.84 for oil, 0.61% and 0.81 for protein, 4.0% and 0.22 for glucosinolates, respectively. Combining calibrations generated from the four instruments gave optimal predictions. Omitting data from any instrument decreased accuracy and precision, although dropping each instrument had a different effect on the measured values of the constituents. Each instrument produced very similar moisture and constituent predictions with a common sample set as indicated by high r2 and, thus, very similar ranking properties. Analysis of variance with the on-combine determinations led to lower residual variance for oil and similar variance for protein compared to those obtained with classical methods of sampling and laboratory NIR analyses. In summary, the results demonstrate that NIR spectroscopy on-combine is very promising to enhance breeding canola for higher oil yield.