Tropical forests are major components of Earth’s carbon stocks, but their diversity and structural complexity pose major challenges for making accurate estimates of their above ground biomass (AGB). Palms, in particular, are prominent and unique components of many tropical forests that have anatomical and physiological differences from dicot trees which affect their height - diameter allometry and consequently, our ability to accurately estimate their AGB. We focused on improving height estimates and AGB models for a highly abundant palm, Prestoea montana, in the Luquillo Forest Dynamics Plot, Puerto Rico. We measured stem height (Hstem), diameter at breast height (DBH) and basal diameter (DB) for 1215 individual palms. Although palms do not develop secondary xylem, we found a strong relationship both between Hstem:DBH and Hstem:DB for P. montana which indicates that its mechanical H:D scaling exhibits similar mechanical constraints of dicotyledonous trees. Additionally, we provide evidence that P. montana’s H:D allometry is mediated by several sources of environmental heterogeneity including slope, elevation, and neighborhood crowding (as a proxy for local competition). We applied our H:D allometric model to hindcast AGB dynamics in the Luquillo Forest Dynamics Plot. Finally, we demonstrated that neighborhood crowding has a negative effect on P. montana’s growth. Our study enables improved  estimates in Puerto Rico and provides novel insight to the growth dynamics of palms in tropical forests.

Herbage and cattle production in semi-arid regions are primarily controlled by climate variation particularly rainfall variability and secondarily by disturbances such as drought, grazing and fire. These factors interact at different spatial and temporal scales in a complex manner difficult to observe or comprehend and, reduce availability and quality of herbage and cattle productivity. Variables for quantifying rangeland productivity are thus rarely available and unreliable yet options for sustainable management are limited. Grazing experiments have provided useful insight about ecological and management factors involved in rangeland functioning, but they have limited scope to deal with high environmental variation. This highlights the need for a systems approach for monitoring rangeland and cattle productivity at the appropriate spatial and temporal scales to enable productivity to be maximised whilst risk to climate variation is minimised. This study explored two broad objectives: to determine the ranch-scale impacts of rainfall variability and drought on herbaceous aboveground biomass (AGB) using optical remote sensing; and to parameterise, evaluate and apply a systems model, the Sustainable Grazing Systems (SGS) whole farm model to complement grazing experiments in assessing the effects of grazing strategies on beef cattle production. To determine rainfall variability impacts, twenty regression models were firstly developed between measured herbaceous AGB and, classical and extended multispectral vegetation indices (MVIs) derived from a Landsat 8 image. End-of-season herbaceous AGB was predicted with high accuracy (r2 range = 0.55 to 0.71; RMSE range = 840 to 1480 kgha-1). The most accurate model was used to construct a regression between rainfall and AGB derived from peak-season Landsat images available between 1992 and 2017. Standardised precipitation index and standardised anomalies of herbaceous AGB production were then used in a convergence of evidence approach to determine the response of AGB to rainfall variability and drought intensity. Total wet season rainfall revealed high variability (33 to 41 % CV) and subsequent herbaceous AGB production were 18 to 35 % more variable. Spatial heterogeneity of AGB production across herbaceous communities were high and deviated from mean AGB by 51 to 69 %. Landscape-level temporal variation of AGB production remained stable despite the increase of climate variability experienced in the region in the past 50 years. Climate inputs and parameter sets for upper-, mid- and foot- slope land types and key grass species, Urochloa mosambicensis and Eragrostis curvula were developed by integrating spatial data with previous soil surveys and extensive reviews of published experiments. A simulation experiment was conducted between 1992 and 2017 for all combinations of land types and grass species to analyse the extent of improvement resulting from parameter adjustments. The SGS model predicted the growth pattern known for grasses native to dry regions of southern Africa. The model represented measured herbaceous biomass moderately well (r2 = 0.57), at low average error (RMSE, 820 kg DM ha-1) despite huge discrepancies in summary statistics for measured (mean, 3877 kg DM ha-1) and simulated (mean, 3071 kg DM ha-1) biomass and residuals. Model predictions were also significantly correlated with remotely sensed AGB (r2 = 0.46) at reasonable overall performance error (RMSE, 981 kg DM ha-1). The integrated workflow developed for parameterising and calibrating the SGS pasture-simulation model can benefit model users in data-constrained environments. Animal growth parameters specific to Brahman weaner steers were defined in the SGS model to enable evaluation of impacts of recommended (10 haLU-1) and other three stocking rates (7, 15 and 20 haLU-1) and multi-paddock grazing systems (2-, 3- and 4- paddocks per herd) on rangeland productivity. Overall, there were no observable differences in herbage production and dry matter intake irrespective of stocking rate and multi-paddock grazing system. But stocking rate effects on animal production were more pronounced compared to multi-paddock grazing systems. To maximise cattle productivity in semi-arid rangelands, management should be emphasised on manipulation of stocking rates over multi-paddock grazing systems. Keywords Rangeland monitoring, climate risk, sustainability, animal productivity, grazing strategies / Thesis (PhD (Animal Production Management))--University of Pretoria, 2020. / National Research Foundation of South Africa / University of Pretoria Department of Research and Innovation Support / Animal and Wildlife Sciences / PhD / Unrestricted

Future climate projections suggest an increase in average temperature as well as a decrease in average winter rainfall across the south-west Australian floristic region (SWAFR). These adverse future climatic conditions will amplify the intensity and frequency of disturbance events such as drought and fire. Mediterranean forests within the SWAFR are prone to drought and fire disturbance and have acquired resilience through the selection of drought and fire tolerable species. However, shifts in the magnitude of these disturbance events could increase the recovery period required for recruitment, causing a shift in forest structure and decreasing the resilience of these ecosystems to future disturbances. In this study, we investigated above-ground biomass (AGB) accumulation of understorey plants at sites within the Northern Jarrah Forest (NJF) that have experienced different degrees of drought and fire intensity. We found that within a disturbance event, sites experiencing either more severe drought and fire intensities on average accumulated substantially more understorey AGB than sites subjected to both low drought and moderate fire intensities. This suggests that understorey species within the SWAFR gain a competitive advantage in high drought and fire severity conditions and are highly tolerant to drought. However, the increase in understorey AGB accumulation also suggests a shift in overall forest structure to more dense, compact, low-ground small stems, which is known to increase fire probability. An increase in fire probability shortens the time period between fire intervals and can detrimentally affect forest recovery, especially in drought conditions. Therefore, these changes may shift ecosystems within the SWAFR to a state of non-equilibrium and reduce resilience to future disturbance events.

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This study assessed the utility of an object-oriented approach to deciduous and coniferous forest volume and above ground biomass estimation, based solely on small-footprint, multiple return lidar data. The study area is located in Appomattox Buckingham State Forest in the Piedmont physiographic province of Virginia, U.S.A, at 78°41’ W, 37°25’ N. Vegetation is composed of various coniferous, deciduous, and mixed forest stands. The eCognition segmentation algorithm was used to derive objects from a lidar-based canopy height model (CHM). New segment selection criteria, based on between- and within-segment CHM variance, and average field plot size, were developed. Horizontal point samples were used to measure in-field volume and biomass, for 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest schemes. Per-segment lidar distributional parameters, e.g., mean, range, and percentiles, were extracted from the lidar data and used as input to volume and biomass regression analysis. Discriminant classification was performed using lidar point height and CHM distributions. There was no evident difference between the two-class and three-class approaches, based on similar adjusted R2 values. Two-class forest definition was preferred due to its simplicity. Two-class adjusted R2 and root mean square error (RMSE) values for deciduous volume (0.59; 51.15 m3/ha) and biomass (0.58; 37.41 Mg/ha) were improvements over those found in another plot-based study for the same study area. Although coniferous RMSE values for volume (38.03 m3/ha) and biomass (17.15 Mg/ha) were comparable to published results, adjusted R2 values (0.66 and 0.59) were lower. This was attributed to more variability and a narrower range (6.94 - 350.93 m3/ha) in measured values. Classification accuracy for discriminant classification based on lidar point height distributions (89.2%) was a significant improvement over CHM-based classification (79%). A lack of modeling and classification differences between average segment sizes was attributed to the hierarchical nature of the segmentation algorithm. However, segment-based modeling was distinctly better than modeling based on existing forest stands, with values of 0.42 and 62.36 m3/ha (volume) and 0.46 and 41.18 Mg/ha (biomass) for adjusted R2 and RMSE, respectively. Modeling results and classification accuracies indicated that an object-oriented approach, based solely on lidar data, has potential for full-scale forest inventory applications. / Ph. D.

Yes / Walking down ramps is a demanding task for transfemoral-amputees and terminating gait on ramps is even more challenging because of the requirement to maintain a stable limb so that it can do the necessary negative mechanical work on the centre-of-mass in order to arrest (dissipate) forward/downward velocity. We determined how the use of a microprocessor-controlled limb system (simultaneous control over hydraulic resistances at ankle and knee) affected the negative mechanical work done by each limb when transfemoral-amputees terminated gait during ramp descent. Methods: Eight transfemoral-amputees completed planned gait terminations (stopping on prosthesis) on a 5-degree ramp from slow and customary walking speeds, with the limb's microprocessor active or inactive. When active the limb operated in its ‘ramp-descent’ mode and when inactive the knee and ankle devices functioned at constant default levels. Negative limb work, determined as the integral of the negative mechanical (external) limb power during the braking phase, was compared across speeds and microprocessor conditions. Findings: Negative work done by each limb increased with speed (p < 0.001), and on the prosthetic limb it was greater when the microprocessor was active compared to inactive (p = 0.004). There was no change in work done across microprocessor conditions on the intact limb (p = 0.35). Interpretation: Greater involvement of the prosthetic limb when the limb system was active indicates its ramp-descent mode effectively altered the hydraulic resistances at the ankle and knee. Findings highlight participants became more assured using their prosthetic limb to arrest centre-of-mass velocity. / ZA is funded by the Higher Committee of Education Development in IRAQ (HCED student number D13 626).

Research has shown that students achieved higher standardized test scores in mathematics and gained more positive attitudes towards mathematics after learning from reform curricula. Because these studies involve actual students and teachers, there are classroom variables that are involved in these findings (Silver and Stein, 1996; Stein et al., 1996). To understand how much these curricula by themselves contribute to higher test scores, I have studied the cognitive demand of tasks in two traditional and two reform curricula. This work required the creation of a scale to categorize tasks based on their level of cognitive demand. This scale relates to those by Stein, Schoenfeld, and Bloom. Based on this task analysis, I have found that more tasks in the reform curricula require higher cognitive demand than tasks in the traditional curricula. These findings confirm other results that posing tasks with higher cognitive demand to students can lead to higher student achievement.