The objective of this research was development and testing of a method for estimating cattle carrying capacities. A series of studies were conducted in developing this method. Range site and vegetation production data were grouped by topographic position and multiple linear regression equations were calculated for predicting vegetation production as a site deviated from the average case of a given range site. Overstory-understory relationships from the literature were adapted into overstory canopy cover classes for predicting understory production and tested on a variety of range sites. Use of these classes produced understory biomass estimates within 13% of measured biomass. Range condition class and understory aspect dominance by forage vs. non-forage species were investigated as estimators of forage value of the understory vegetation. Both were significantly related to amount of forage in the understory. However, understory aspect proved to be a better estimator when individual comparisons were examined. The previous findings, along with Soil Conservation Service range site guides, were used to calculate resource value ratings. Adjustment factors to be applied to the resource value ratings were calculated, using data from the literature, to account for the effects of slope and distance from water on forage utilization by cattle. These resource value ratings and adjustment factors form the basis of the carrying capacity estimation method. Pastures identified as properly utilized were used in testing the method developed. Pastures were mapped for range site, vegetation, slope and water location. Maps were converted to digital form and analyzed using the Map Analysis Package (MAP) computer program (Tomlin, 1975). Construction of a final range site-vegetation-slope-distance from water map, assigning of resource value ratings and adjustment factors, and computation of final carrying capacity estimates were accomplished using MAP. Carrying capacity estimates from the developed method were well correlated to estimates from ocular reconnaissance and area allowable use methods, r = .87 and .97, respectively, and with the actual use (perceived proper use), r = .95. These estimates were accomplished without intensive field sampling. The only information required was range site designation, amount of overstory canopy cover, understory aspect class, percent slope and water location.

Revised publication

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirement for the degree of Master of Science (Environmental Sciences) at the School of Geography, Archaeology & Environmental Studies March 2017 / Accurate estimates and mapping of grass quality is important for effective rangeland management. The purpose of this research was to map different grass species as well as nutrient Phosphorus (P) and Sodium (Na) concentration across grass communities using Sentinel-2 imagery in Telperion game reserve. The main objectives of the study were to: map the most common grass communities at the Telperion game reserve using Sentinel-2 imagery using artificial neural network (ANN) classifier and to evaluate the use of Sentinel-2 (MSI) in quantifying grass phosphorus and sodium concentration across different grass communities. Grass phosphorus and sodium concentrations were estimated using Random Forest (RF) regression algorithm, normalized difference vegetation index (NDVI) and the simple ratios (SR) which were calculated from all two possible band combination of Sentinel-2 data. Results obtained demonstrated woody vegetation as the dominant vegetation and Aristida congesta as the most common grass species. The overall classification accuracy = 81%; kappa =0.78 and error rate=0.18 was achieved using the ANN classifier. Regression model for leaf phosphorus concentration prediction both NDVI and SR data sets yielded similar results (R2 =0.363; RMSE=0.017%) and (R2 =0.36 2; RMSE=0.0174%). Regression model for leaf sodium using NDVI and SR data sets yielded dissimilar results (R2 =0.23; RMSE=16.74 mg/kg) and (R2 =0.15; RMSE =34.08 mg/kg). The overall outcomes of this study demonstrate the capability of Sentinel 2 imagery in mapping vegetation quality (phosphorus and sodium) and quantity. The study recommends the mapping of grass communities and both phosphorus and sodium concentrations across different seasons to fully understand the distribution of different species across the game reserve as well as variations in foliar concentration of the elements. Such information will guide the reserve managers on resource use and conservation strategies to implement within the reserve. Furthermore, the information will enable conservation managers to understand wildlife distribution and feeding patterns. This will allow integration of effective conservation strategies into decisions on stocking capacity. / MT 2017

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The management and sustainable use of Central Australian rangelands for livestock production and conservation requires improved knowledge of the temporal and spatial distribution of primary production in this region. To provide such information, this thesis investigated methods that could rapidly and efficiently estimate regional herbage biomass production in these arid landscapes. Two different approaches were examined, using (1) ground-based or (2) satellite-based data sources. Soil moisture and herbage growth data were collected over several growth seasons and five landscape types in Central Australia, and the data used to develop a model of soil moisture balance and herbage production for the region. The model has few parameters and only requires inputs of rainfall and potential evaporation to predict daily soil moisture and plant growth. Moisture loss in the 0-500 mm soil profile was modelled using a negative exponential function that depends on available soil moisture and is driven by potential evaporation. The growth of herbage, whilst soil moisture is above wilting point, is a linear function of actual evapotranspiration, with the decay of plant material represented by a logistic curve through time. Soil moisture, herbage biomass and species composition assessments made at hectare and square kilometre scales at four locations within Central Australia were examined to determine if a small sample area could be used to accurately describe the soil and plant conditions at a landscape scale. Moisture levels of the 0-200 and 0-500 mm soil profiles from nine samples were analysed for the beginning and conclusion of a growth season, whilst herbage biomass and species composition from 50 samples were compared at the end of the growth season. Results suggest that mean soil moisture levels determined in a 1 ha area are comparable with mean values in the surrounding 1 km2 area. Herbage biomass and species richness for a square kilometre can be assessed at a hectare site for some landscape types, but a larger sampling area (> 1 ha) is recommended for most rangeland assessments. Satellite data (NOAA-11) were examined for their potential application in assessing primary productivity in Central Australia. Several image correction techniques were tested to minimise the adverse effects of atmospheric contamination and illumination. Two measures of atmospheric moisture: (1) radiosonde data and (2) temperature differences between bands 4 and 5 of the NOAA satellite (split-window) were used to explain variations in NOAA-11 normalised difference vegetation index (NDVI) on inert desert sites. The splitwindow approach provided the best single factor relationship (r2=0.63) and, when combined with scattering angle (illumination) effects, up to 81% of the variation in NDVI data could be explained. Field measurements of herbage biomass were correlated with four growth indices derived from NOAA-11 NDVI data. The influence of preflight and sensor degradation calibrations of Bands 1 and 2, and atmospheric correction techniques were also tested. Correlations between temporal sums of NDVI and herbage biomass data were relatively poor (r2<0.42) and unsuitable for herbage assessment in Central Australia. However, correlations between atmospherically corrected and background-adjusted maximum NDVI data and observed herbage biomass were strong (r2=0.91), that will allow primary production in the arid rangelands of Central Australia to be assessed rapidly and efficiently using remotely-sensed information.

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 86-93). Also available on the World Wide Web.

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