Agreement Between Measurements of Shrub Cover Using Ground-Based Methods and Very Large Scale Aerial Imagery

Very large scale aerial (VLSA) photography is a remote sensing method, which is collected and analyzed more efficiently than ground-based measurement methods, but agreement with ground-based measurements needs to be quantified. In this study, agreement between ground- and image-measured cover and precision, and accuracy of image locations and scale, were assessed. True image locations were determined by georeferencing images and conducting a ground search. Accuracy and precision of planned, aircraft, and georeferenced locations were evaluated by comparison with true image locations. Shrub cover was measured at true image locations using ground-based line-intercept and on the image using point-intercept. Sagebrush (Artemisia spp. L.), antelope bitterbrush (Purshia tridentata [Pursh] DC.), and spineless horsebrush (Tetradymia canescens DC.) were distinguished in the imagery. Agreement between ground- and image-based measurements was quantified using limit-of- agreement analysis. True ground locations of the VLSA images were within a 41-m radius of the aircraft location at the time of image acquisition, with 95% confidence. Using a panchromatic image from the QuickBird satellite (0.6-m pixel resolution) as a base map, 90% of true ground locations were within a 5-m radius of the location estimated from georeferencing the VLSA image to the base map. VLSA image-measured cover was, in general, unbiased with mean absolute differences between VLSA- and ground-based methods less than 1.3%. The degree of agreement and absence of bias between VLSA image-measured and ground-measured cover is sufficient to recommend using VLSA imagery to measure shrub cover. The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

39 Thoughts on the energetic efficiency of grazing cattle

Abstract Energetic efficiency of cattle is a significant issue in the beef industry. Ruminant nutritionists have made strides in improving production efficiency, but we still see significant variation among individuals. A popular method for evaluating the cattle efficiency is selection based on residual feed intake. This method has some limitations because of mounting evidence that it has a genotype x environment interaction. Individuals deemed most efficient being fed in confinement are not most efficient when their environment is grazing. Hence, cattle efficiencies need to be evaluated in appropriate environments. Because it is impossible to directly measure DMI by grazing cattle, we need to develop proxies to assess the animal’s efficiency. By using experimental methods such as breath cloud analysis to provide individual estimates of total heat of production and known constants for retained energy in BW gain, we can calculate ME energy intake. However, we still lack the ability to measure DMI without the true digestibility of the grazed diet. The greatest energy expenditure is basal metabolism and if we are to decrease it, we will need to examine their body composition and protein-turnover rates. The rates of protein and fat deposition in the carcass affects an animal’s efficiency and ADG chiefly because of the higher energy density of fat. However, with great protein deposition, there also is a higher ME requirement for its maintenance because of the high protein turn-over rate. Energy expensed for protein turnover is approximately 23% of the total energy intake by average cattle. Management that decreases body protein turn-over rate probably would result in a decreased basal metabolism. The research needed to truly improve the energetic efficiency of grazing cattle will be time consuming and expensive, but for progress to be made it will be required.</jats:p

188 Comparison of two software methods of fitting one- and two-compartment age-dependent digesta kinetics models

Abstract Ruminant nutritionists fit digesta kinetics models to better understand processes in the gastrointestinal tract. Several software packages are available to fit one- and two-compartment, age-dependent digesta kinetics models to fecal marker concentration over time after giving a marker dose. We characterized repeatability and agreement for two programs (R and SAS) used to fit these models. We constructed 8100 datasets using a Monte-Carlo approach where errors were added to true marker concentrations for samples from 81 synthetic profiles representing different animal and diet combinations (100 datasets per profile). Datasets contained concentrations at 15 nominal times (from 0 to 120 hours after dosing). The two error sources were sampling time (uniform between -3 and 1 hours) and concentration measurement (normal with mean 0 and SD = 5 + 0.08CT, where CT is true concentration). The resulting datasets were fit to G2 and G2G1 models using both software and model parameters (K0, λ or λ1, K2, and τ) were used to calculate digesta kinetics parameters (particle passage rate, gastrointestinal DM fill, fecal DM output, gastrointestinal mean retention time, and rumen retention time). When fitting G2 models, all converged, but when fitting G2G1 model, 60 did not converge. The parameters were more repeatable from R than from SAS, but the ratios of repeatability to the mean was generally less than 10%. Bias and SD of differences between software packages were also small. The G2G1 models produced smaller bias and SD of differences than the G2 models. Bias and SD for digestion parameters between software were also small, however, G2G1 models had smaller biases and SD of differences than G2 models. Repeatability was better with R than with SAS, but mean differences were small; the G2G1 model was more repeatable than the G2 model, but differences between software were small for G2G1 models.</jats:p

Is it feasible to improve stand persistence of eastern gamagrass (Tripsacum dactyloides L.) through breeding?

Abstract Eastern gamagrass is a warm‐season perennial grass that has excellent forage nutritional quality and a broad range of adaptations. Despite the favorable forage qualities, agronomic issues persist; most important for grazing systems is stand persistence from preferential grazing when growing in mixed species communities. Stand persistence when monoculture stands are grazed has been previously studied, and it has been observed that stands decline under high stocking rates or low grazing height. The objective of our study was to evaluate if an improvement of stand persistence under defoliation to a low‐cut height is feasible through the breeding of improved lines. A persistence trial was conducted using a completely random design and eight replications to evaluate the stand persistence of two commercial cultivars, two experimental lines, and an ecotype selection. Defoliation occurred by grazing and mowing to a height of 8 cm three‐to‐four times over the course of five growing seasons. Stand counts of live plants were taken following the final defoliation of the season. Mixed linear regression modeling indicates significant differences between genotypes developed in years where no grazing occurred. An ecotype selection from Northwest Oklahoma exhibited significantly greater persistence than the cultivar Pete and the other experimental lines. These results suggest that different alleles exist for critical persistence genes, indicating it may be feasible to improve the trait through breeding and selection

Weather Variability and Adaptive Management for Rangeland Restoration

The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform March 202

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Aerial imaging provides a landscape view of crop fields that can be utilized to monitor plant diseases. Phymatotrichopsis root rot (PRR) is a serious root rot disease affecting several dicotyledonous hosts, including the perennial forage crop alfalfa. PRR disease causes stand loss by spreading as circular to irregular diseased areas that increase over time, but disease progression in alfalfa fields is poorly understood. The objectives of this study were to develop a workflow to produce PRR disease maps from sets of high-resolution red, green and blue (RGB) images acquired from two different platforms and to assess the feasibility of using these PRR disease maps to monitor disease progression in alfalfa fields. Aerial RGB images, two from unmanned aircraft systems (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014&ndash;2015 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classification of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform-specific and manned-aircraft platform-specific. Our results showed that the UAS-platform-specific spectral signature was most efficient for classifying images acquired with the UAS, with accuracy ranging from 90 to 96%. In contrast, manned-aircraft-acquired images classified using image-specific spectral signatures yielded 95 to 100% accuracy. The effect of hue, saturation and value color space transformations (HSV and Hrot60SV) on classification accuracy was determined, but the accuracy estimates showed no improvement in their efficiency compared to the RGB color space. Finally, the data showed that the classification of the bare ground increased by 74% during the study period, indicating the extent of alfalfa stand loss caused by PRR disease. Thus, this study showed the utility of high-resolution RGB aerial images for monitoring PRR disease spread in alfalfa