Optimising cutting method and timing for the control of Abutilon theophrasti seed production

Abutilon theophrasti (velvetleaf) is a widespread and problematic annual weed. Greenhouse and field experiments were conducted to investigate the effects of different cutting methods on the viability of A. theophrasti seeds. Three cutting methods were assessed: (1) Entire plant cut and dried (EPD)—plants were cut at soil level and dried with capsules attached on the greenhouse bench or soil surface for 4 weeks; (2) capsules detached and dried (CD)—capsules were removed from plants and dried for 4 weeks; and (3) capsules detached and tested while fresh (CF)—a control treatment. Before drying, the developmental stage (stage one, dark green; stage two, light green; stage three, yellowish-green; or stage four, black with the slightly open capsule) and age (days after flowering, DAF) of each capsule was recorded. Seed viability was measured immediately in the CF treatment and after the 4-week drying period in the EPD and CD treatments. No seeds in the EPD and CD treatments were viable when harvested at the first developmental stage (1–8 DAF) in either experiment, but 100% of seeds in the CF treatment in the field were viable when harvested at 8 DAF. In both greenhouse and field experiments, seeds attained full viability at earlier harvest ages in CF than in EPD or CD treatments, suggesting that seeds might become viable relatively early in development but lose viability if allowed to dry. These findings could be applied to optimise late-season mechanical control of A. theophrasti.     

Chromosomal location of genes for resistance to powdery mildew in common wheat (Triticum aestivum L. em Thell.). 9. Gene MlZec1 from the Triticum dicoccoides-derived wheat line Zecoi-1

The Triticum dicoccoides-derived wheat line Zecoi-1 provides effective protection against powdery mildew. F₃ segregation analysis of Chinese Spring × Zecoi-1 hybrids showed that resistance in line Zecoi-1 is controlled by a single dominant gene. Amplified fragment length polymorphism (AFLP) analysis of bulked segregants from F₃s showing the homozygous resistant and susceptible phenotypes identified eight markers, of which four were associated with the resistance allele in repulsion phase. Following the assignment of these four repulsion phase AFLP markers to wheat chromosome 2B with the aid of Chinese Spring nulli-tetrasomic lines, they were physically mapped in the terminal breakpoint interval 0.89 (2BL-6)–1.00 (telomere) of chromosome 2BL. Genetic and physical mapping of simple sequence repeat markers from the distal half of chromosome 2BL located the wild emmer-derived powdery mildew resistance gene distal of breakpoint 0.89 in deletion line 2BL-6. Based on disease response patterns, genomic origin and chromosomal location the resistance gene in Zecoi-1 is temporarily designated MlZec1.     

Error assessment in decision-tree models applied to vegetation analysis

Methods were developed to evaluate the performance of a decision-tree model used to predict landscape-level patterns of potential forest vegetation in central New York State. The model integrated environmental databases and knowledge on distribution of vegetation. Soil and terrain decision-tree variables were derived by processing state-wide soil geographic databases and digital terrain data. Variables used as model inputs were soil parent material, soil drainage, soil acidity, slope position, slope gradient, and slope azimuth. Landscapescale maps of potential vegetation were derived through sequential map overlay operations using a geographic information system (GIS). A verification sample of 276 field plots was analyzed to determine: (1) agreement between GIS-derived estimates of decision-tree variables and direct field measurements, (2) agreement between vegetation distributions predicted using GIS-derived estimates and using field observations, (3) effect of misclassification costs on prediction agreement, (4) influence of particular environmental variables on model predictions, and (5) misclassification rates of the decision-tree model. Results indicate that the prediction model was most sensitive to drainage and slope gradient, and that the imprecision of the input data led to a high frequency of incorrect predictions of vegetation. However, in many cases of misclassification the predicted vegetation was similar to that of the field plots so that the cost of errors was less than expected from the misclassification rate alone. Moreover, since common vegetation types were more accurately predicted than rare types, the model appears to be reasonably good at predicting vegetation for a randomly selected plot in the landscape. The error assessment methodology developed for this study provides a useful approach for determining the accuracy and sensitivity of landscape-scale environmental models, and indicates the need to develop appropriate field sampling procedures for verifying the predictions of such models.