Slope correction for LAI estimation from gap fraction measurements

Digital hemispherical photography poses specific problems when deriving leaf area index (LAI) over sloping terrain. This study proposes a method to correct from the slope effect. It is based on simple geometrical considerations to account for the path length variation within the canopy for cameras pointing vertically. Simulations over sloping terrain show that gap fraction increases up-slope while decreasing down-slope. As a consequence of this balance between up- and down-slope effects, effective LAI estimates derived from inversion of the Poisson model are marginally affected for low to medium slopes (<25°) and LAI (LAI < 2). However, for larger slopes and LAI values, estimated LAI values may be strongly underestimated. The proposed correction was evaluated over four forested sites located over sloping terrain. Results indicate that in these conditions (LAI between 0.6 up to 3.0, clumped canopies with relatively erectophile leaf distribution), the effect of the slope (between 25° and 36°) was moderate as compared to other potential sources of problems when deriving LAI from gap fraction measurements, including clumping, leaf angle inclination and spatial sampling.     

A bayesian approach to crop Model calibration under unknown error covariance

Bayesian methods seem well adapted to dynamic system models in general and to crop models in particular, because there is in general prior information about parameter values. The usefulness of a Bayesian approach has often been pointed out, but actual applications are rather rare. A major difficulty is including the elements of the covariance matrix of model errors in the treatment. We treat the specific case of balanced data and an unstructured covariance matrix. In our particular case this is a 3 × 3 matrix. We illustrate two methods for deriving a sample from the joint posterior density for the crop model parameters and the error covariance matrix parameters. The first method is based on importance sampling, the second on Metropolis within Gibbs sampling. We derive an instrumental density for the former and a proposal density for the latter which are adapted to this type of model and data. Both algorithms work well and they give very similar results. The example concerns a model for sunflowers during rapid leaf growth. The ultimate goal is to use the model as a decision aid in predicting disease risk.     

Improved visualization of the lumbar spine nerve roots in dogs using water excitation (ProSet) as opposed to short tau inversion recovery: A retrospective study of two fat suppression MRI sequences

Magnetic resonance imaging fat suppression techniques are commonly used for diagnosis of canine spinal disease, however, studies comparing different techniques are currently lacking. This retrospective, methods comparison study aimed to evaluate water excitation and STIR MRI pulse sequences for visualization of canine lumbar spinal nerve roots. For inclusion, all dogs had to have dorsal planar MRI studies of the lumbar spine using both sequences. Visual grading analysis was used for scoring the following five criteria: degree of fat suppression; nerve root visualization; subjective tissue contrast; presence of noise; and overall better image quality. Scores were independently recorded by three board‐certified veterinary radiologists on two separate occasions, 3‐6 weeks apart. A total of 90 dogs were sampled. A two‐tailed t‐test showed that there were significant differences in all scored parameters (P < 0.00001), with the exception of noise (P = 0.47343), and that the water excitation sequence scored higher in all cases excluding noise. A Gwets AC kappa for intraobserver and interobserver reliability showed “almost perfect” agreement for the nerve roots in both tests (intra: k = 0.88; inter: k = 0.90). Intraobserver agreement was “substantial” for the degree of fat suppression (k = 0.68), subjective tissue contrast (k = 0.75), and overall better image quality (k = 0.76) and it was “fair” for the noise (k = 0.46). Interobserver agreement was “moderate” for the degree of fat suppression (k = 0.53), subjective tissue contrast (k = 0.63), and overall better image quality (k = 0.66) and “slight” for noise (k = 0.25). These findings supported using the water excitation pulse sequence for fat‐suppressed MRI of canine lumbar spinal nerve roots.     

Diagnosis and successful treatment of a caudal lingual abscess in a geriatric dog

A 13-year-old, intact male, golden retriever was presented for glossomegaly. A diagnosis of a caudal lingual abscess was made by fine needle aspiration under general anesthesia. The dog showed marked clinical improvement following abscess drainage. Lingual abscesses should be included as a differential diagnosis in any dog with an unexplained acute glossomegaly.