Root rot diseases of sugarbeet (Beta vulgaris L) as affected by defloliation intensity

The aim of this work was to study the effect of sugar beet re-growth after water stress defoliation on root rots of three cultivars (Europa, Rival Corsica), which were spring sown in Thessaly, central Greece, for two growing seasons (2003-04). At the beginning of July, sugar beets were subjected to water deficit with irrigation withholding. A month later, three defoliation levels (control - C, moderate - MD, severe - SD) and irrigation were applied. Thus, sugar beets were forced to re-grow and three harvests (15, 30 and 40 days after defoliation - DAD) were conducted. Rotted roots per hectare were counted and pathogens were identified. Data were analyzed as a four-factor randomized complete block design with years, defoliation levels, sampling times and cultivars as main factors. The number of rotted roots was increased with the defoliation level and was significantly higher for SD sugar beets (3748 roots ha–1). No significant differences were found between C and MD treatments (1543 and 2116 roots ha–1, respectively). Rival was the most susceptible cultivar to root rots. Sugar beets were more susceptible to rotting 15 and 40 DAD (2778 and 2998 roots ha–1). The causal agents of root rots were the fungi, Fusarium spp., Rhizopus stolonifer, Macrophomina phaseolina and Rhizoctonia solani

A simple quantitative model to predict leaf area index in sorghum

Leaf area index (LAI) is a widely used physiological parameter to quantify the vegetative canopy structure of crops. Over the years, several models to estimate LAI have been developed with various degrees of complexity and inherent shortcomings. The LAI simulation models proposed so far for sorghum [Sorghum bicolor (L.) Moench] either lack details of the leaf area dynamics of expanding leaves or demand exhaustive measurements. The objective of this study was to develop a simple quantitative model to predict the LAI of sorghum by introducing a new method for simulation of the leaf area of expanding leaves. The proposed model relates LAI to thermal time. It calculates LAI from an algorithm considering the total number of mature leaves, the area of mature leaves, the area of expanding leaves, and plant density. The performance of the model was tested using LAI data collected using a nondestructive method under field conditions. The slope of the regression of modeled LAI on observed LAI varied for photoperiod-sensitive and -insensitive genotypes in 2010. The coefficients of determination (R²) between modeled and observed LAI were 0.96 in 2009 and 0.99 (photoperiod insensitive) and 0.95 (photoperiod sensitive) in 2010. The inclusion of expanding leaves in the model improved its accuracy. The model provides an accurate estimate of LAI at any given day of the vegetative growing season based only on thermal time and making use of default coefficients demonstrated in this research