47 Figure 2.2: Regression analysis of the relationship between CVol (natural logarithm conversion). independent variable) and LDM of A. 56 Figure 2.3: Regression analysis of the relationship between CVOL (natural logarithm conversion). independent variable) and LDM of E. Private Game Reserve (MGR), Pongola Game Reserve (PGR)) regression analyzes (Table 2.4) of the relationship between CVol (normal logarithm transformation) (independent variable) and LDM (dependent variable) for (a) A .
83 Figure 3.5: Average browsing production for different heights to the lower leaves of the tree crown. 27 Table 2.1: A comparison of taste, leaf size and wood pulp for the four study tree species.
Light
Temperature and daylength
Water
Growing season
Soil fertility
Tree guild
In general, evergreen trees have the following ecological advantages over deciduous trees: (1) Photosynthesis may occur over a longer period of each year, even into the dry season (Mooney and Dunn 1970). Before leaves are shed (whether evergreen or deciduous), the mineral nutrients (e.g. N, P, K) are incompletely extracted, and the fraction that is not re-displaced must be replaced when new leaves are produced (Chabot and Hicks 1982). This suggests that evergreen trees growing in nutrient-poor soils have an ecological advantage (Givnish 2002). 4) Finally, evergreen leaves are often tougher and thicker than deciduous leaves to withstand drought during the dry season without wilting (Chabot and Hicks 1982).
Since evergreen leaves are often the only leaves exposed to herbivores during the dry season, evergreen trees invest in tough leaves and high levels of chemical defenses to (to some extent) repel high levels of browsing (Coley et al. 1985 ). Deciduous trees maintain higher rates of photosynthesis per unit leaf mass during favorable growing conditions than evergreen plants (Givnish 2002).
Tree age and size
A high fiber concentration, together with the presence of high levels of chemical defenses such as tannins and aromatics, reduces flavor (Bergström 1992, Walker 1980, Harborne 1991). In general, deciduous tree species are thought to be more palatable than evergreen tree species because evergreen leaves have more inherent physical and chemical defenses to reduce browsing (Eamus 1999). The higher concentration of N in the leaves also increases the efficiency of photosynthesis, allowing the plant to compensate for their short lifespan (Eamus 1999).
More N assimilation leads to the formation of more plant metabolic constituents (M) and possibly N-based secondary chemicals, while more carbon. 16 assimilation leads to the formation of more plant structural material and carbon-based secondary chemicals (C) (Bell 1982).
Soil fertility
Temperature
Tree guild
Tree age
Plant defences
Average monthly humidity is relatively high (fluctuating between 65-85%), even in the drier interior parts of the region. Proceedings of the Southern African Grassland Society Condensed tannins inhibit feeding by. ruminant browsing on a South African savanna. Proceedings of the Grassland Society of Southern Africa Seasonal chemical composition of the diet of the Transvaal Lowveld giraffe.
Bulletin of the Grassland Society of Southern Africa Secondary connections and food selection by Colobus monkeys. Proceedings of the Grassland Society of Southern Africa Tree and shrub growth and response to defoliation.
78 Figure 3.1: Stages in the determination of average leaf production (kg/ha/quarter) according to the method of Pellew (1981). Differences in leaf production (kg/ha/quarter) between the different key influencing factors identified by CART® were determined using repeated measures analysis of variance (ANOVA) (Genstat Lawes Agricultural 2006). Leaf production was mainly influenced by the ACVol (m3) of trees (considering 7% of the total variance for the model).
Stratifying tree canopies into height classes (Telfer 1969) or sorting whole canopies into foliage density classes (Mason and Hutchings 1967) reduces variance in browsing estimates of production. Differences in annual tuber production (kg/ha/year) and seasonal tuber production (kg/ha/quarter) between different tree guilds, feeding levels and defoliation treatments were determined by paired sample t-tests, independent sample t-tests, one -way ANOVA and repeated measures ANOVA (Genstat Lawes Agricultural 2006) where appropriate. Standard errors of browsing production estimates are relatively large (up to 80% of the mean in some cases).
Although average browse production was greater in the growing season (by a factor of three) in evergreen trees C. Average annual browse production (kg/ha/year) of different levels of nutrition in the tree canopy varied in D deciduous. Only small differences between average cumulative browse production (kg/ha/quarter) of different levels of leaf nutrition A.
Species Year Average leaf production for each feeding level (kg/ha/year ± SE) F-ratio P-value. 121 Figure 4.5: A comparison of the average leaf production of tagged shoots with and without defoliation (with standard errors), for semi-deciduous trees, for (a) S. 122 Figure 4.6: A comparison of the average leaf production of tagged shoots with and without defoliation ( with standard errors), for evergreen trees (a) C.
Changes in browse yield (kg/ha/quarter) were mainly due to P (% DM) (with 7% of the total variance accounted for by the model) (Figure 5.3). Other significant browse nutrition factors contributing to the variation in quarterly browse production were: CP (7%), Ca (2%) and ADF, with the overall model accounting for 24% of the total variance in the data set. The season affected different concentrations of nutrients in the guilds of the studied tree species.
Do the CP concentrations of the current season's growth of browse meet the daily nutritional needs of roe deer. The resulting browsing production basis functions accounted for 39 % of the total variance in the data set (P=0.00). The resulting browsing production basis functions accounted for 38% of the total variance in the data set (P=0.00).
The resulting browsing production basis functions accounted for 65% of the total variance in the data set (P=0.00). According to Aucamp (1976), browsing capacity is most influenced by the following six factors: (i) density of woody plants, (ii) amount of leaf material within reach of animals, (iii) species composition of woody vegetation, (iv) palatability of woody species, (v) digestibility wooden ones. The resulting browsing production basis functions accounted for 39% of the total variance in the data set (P<0.001) (Table 6.2).
167 Figure 6.1: The relationship between (a) foliage density, (b) stem diameter, (c) available canopy volume and (d) height to lowest leaves and quarterly leaf production in deciduous trees (Acacia nilotica and Dichrostachys cinerea) (Pure ordinal contribution). The resulting browsing production basis functions accounted for 38 % of the total variance in the data set (P<0.001) (Table 6.4). The resulting browsing production basis functions accounted for 65 % of the total variance in the data set (P<0.001) (Table 6.6).
172 Figure 6.3: Relationship between (a) stem diameter, (b) tree height, (c) height to lowest leaf and (d) crown volume and quarterly production in evergreen trees (Carissa bispinosa, Euclea divinorum and Gymnosporia senegalensis) (Pure ordinal contribution). It is therefore intuitive that the best predictors of semi-deciduous browse production are measures of the prevailing climatic conditions.
Leaf dry mass estimation of woody plants in northern Zululand
Determinants and measurement of browse production in northern
The available canopy volume turned out to be the most reliable predictor of leaf production per species and per tree. A conservation system must therefore take into account the browsing component, more specifically the browsing production potential, of the system. This study defined leaf production of trees as the growth of new shoots (kg dry matter) over a specified period (in this case 3 months or a year) ((Pellew 1981).
In South Africa in particular, browsing production (as defined in this study) has yet to be measured. In addition, our study aimed to determine which biotic (measurable dimensions of trees, tree species) and abiotic (climate and soil) factors influence the rate and amount of growth of these species. The following stages in determining the speed of browsing production are schematically shown in Figure 3.1.
Other important factors influencing quarterly leaf production were: sampling period (1 %), height to lowest leaf (m) (12 %), species (2 %) and potassium (mg/L) (0.2 %), with the total model accounting for 32.5 % of the total variance in the data set. The fact that leaf production is affected by sampling period (derived season) is an important result in terms of the management and conservation of brittle. Leaf production during the growing season essentially determines the amount of sap available to mammalian herbivores in the year ahead, as well as the tree's ability to respond to the defoliation (or any other stress for that matter) of the previous season.
93 production at the species level is given in Chapter 4 with the aim of improving the forecasting performance of the developed production management models. Finally, the use of ACVol as a seasonal measure of browsing production (for annual comparison purposes) is supported. Browsing the production of some trees in the Sahel: relationships between maximum foliage biomass and different physical parameters.
Browse production comparisons between evergreen, semi-
Mean leaf production (kg/ha/quarter) differed between sampling periods in semi-deciduous and evergreen trees (Table 4.5).
Relation between browse production and nutritive value in three
Predicting browse production for northern Zululand
Stem diameter (m), HL (m) and HT (m) had secondary, tertiary and quaternary effects on leaf production with variable significance figures of and 29 % respectively. 166 Equation 6.2: Basis functions for the deciduous growing season scroll through production (kg/ha/quart) MARS® model. The resulting response of scroll production to ACVol is in the form of one linear regression spline and one basis function (Equation 6.2, BF3).
The resultant response of browsing output to HL is in the form of two linear regression splines and a basis function (Equation 6.2, BF4). The resultant response of the browsing output to the MMinDT is in the form of a linear regression spline and a basis function (Equation 6.3, BF3). Stem diameter (m), HL (m) and HT (m) had secondary, tertiary and quaternary effects on browse production with variable significance figures of and 29 % respectively (Table 6.5).
The resultant response of the browse output to flow D is in the form of two linear regression splines and a basis function (Equation 6.4, BF1). The resultant response of browsing output to HL is in the form of two linear regression splines and a basis function (Equation 6.4, BF3). The resultant response of surf output to ACVol is in the form of two linear regression splines and a basis function (Equation 6.4, BF4).
The use of measurable tree sizes for predicting and measuring canopy production is therefore feasible. Available canopy volume was identified as the most important predictive variable in the MARS® production models for deciduous, semi-deciduous and evergreen leaves. 174 emphasized the importance of this factor in determining leaf production and argued that canopy size (defined by the number of branches) would determine the number of potential growth sites (branches).
Browse quantity and nutritive value in the context of carrying
