Weighing in on a method to discriminate maize haploid from hybrid seed

The doubled haploid breeding method can produce maize inbred lines faster than traditional methods, but there are challenges associated with it. Sorting haploid from hybrid seed based on visual colour markers is time consuming and can be difficult due to colour inhibitors that obscure pigmentation needed to distinguish between haploid, hybrid and outcrossed seed. In this study, weight was evaluated as a method to sort haploid from hybrid seed. A first experiment utilized two families for analysis in a preliminary study. Eleven haploid and hybrid kernels from both families were weighed for a total of 44 experimental units. A second experiment was carried out using six families, using the same format as the previous, for 132 experimental units. Hybrid seed weighed significantly more than haploid seed in both experiments. However, the interaction between line and kernel type was significant in the second experiment. In conclusion, efficacy of sorting haploid from hybrid kernels based on weight depends on the genotypes involved

Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field

Quantification of Oil Content in Intact Sugar Beet Seed by Near-Infrared Spectroscopy

Sugar beet seed oil reserves play an important role in successful germination and seedling development. The purpose of this study was to establish a non-destructive near-infrared (NIR) methodology with good predictive accuracy to quantify stored seed oil in sugar beet seed. Reflectance NIR spectra were acquired from viable monogerm seeds. Calibration equations were developed using partial least squares. The optimized calibration model reached a Pearson correlation of 0.946; an independent prediction test reached a correlation of 0.919 and a Root Mean Square Error of Prediction of 0.388. The possible role of the outer pericarp in the prediction of oil content was additionally considered. The results indicate that the model is suitable for a rapid and accurate determination of the oil content in both polished and unpolished sugar beet seeds. This NIR application might help to understand the role of seed energy reservoirs in sugar beet germination and further plant growth

Wuerschum_et_al_2011_rawData

Marker names with genetic map positions, P values for all markers for the seven tested biometrical models, shown for all six traits