Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

Effects of Root Characteristics and Deep Tillage on the Development of Sudden Death Syndrome in Soybean

162 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.Current research on sudden death syndrome (SDS), caused by the fungus Fusarium solani f. sp. glycines (FSG) relies on foliar severity ratings as an indication of disease resistance. However, foliar symptom severity does not always correlate well with infection on the root system or with reductions in yield. A combination of root and foliar symptom severity ratings would provide more information on disease resistance. Yet, it is unknown whether physical mechanisms exist by which the root system of the soybean plant resists colonization or overcomes infection by this pathogen. Field experiments, conducted in Urbana, IL were designed to determine if root characteristics (architecture, length, surface area, volume, and average diameter), time and location of colonization, foliar disease severity, and yield varied among 12 soybean cultivars, and whether these parameters changed when plants were inoculated with FSG. Disease severity and yield of the cultivars were related to root characteristics. Significant differences in root characteristics, foliar symptom severity and yield were observed among the cultivars when inoculated with the pathogen. Resistant cultivars had significantly smaller root diameters and lower root volumes than susceptible cultivars. Colonization frequency on the upper taproot and laterals significantly changed throughout the season. However neither root colonization nor architecture was significantly different among cultivars. In addition, the effects of deep tillage on the severity of SDS, as measured by foliar symptoms and yield, were evaluated. Deep tillage did not significantly affect yield, nor did it decrease the amount of SDS observed as foliar disease symptoms. Yield and foliar symptom severity were significantly related, but the correlation was weak. In greenhouse experiments the effects of infection site on SDS disease severity and soybean root characteristics were evaluated. Soybean root length, surface area, and volume were significantly lower when the taproot was the primary site of infection compared to when the lateral roots were the point of infection. Results from field and greenhouse trials showed differences among the root characteristics of 12 cultivars, and that infection by FSG and the site of infection changed these root characteristics. This suggests that the soybean root system may play a role in SDS resistance.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

The use of Vis-NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial

This paper reports the use of visible/near-infrared reflectance spectroscopy (Vis-NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm−3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm−3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm−3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r2 cross-validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross-validation (RMSECV) = 0.60 mg cm−3; with removing outliers, r2 cross-validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm−3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils