Tropical dryland agroforestry on clay soils: : Analysis of systems based on Acacia senegal in the Blue Nile region, Sudan

Acacia senegal, the gum arabic producing tree, is the most important component in traditional dryland agroforestry systems in the Blue Nile region, Sudan. The aim of the present study was to provide new knowledge on the potential use of A. senegal in dryland agroforestry systems on clay soils, as well as information on tree/crop interaction, and on silvicultural and management tools, with consideration on system productivity, nutrient cycling and sustainability. Moreover, the aim was also to clarify the intra-specific variation in the performance of A. senegal and, specifically, the adaptation of trees of different origin to the clay soils of the Blue Nile region. In agroforestry systems established at the beginning of the study, tree and crop growth, water use, gum and crop yields, nutrient cycling and system performance were investigated for a period of four years (1999 to 2002). Trees were grown at 5 x 5 m and 10 x 10 m spacing alone or in mixture with sorghum or sesame; crops were also grown in sole culture. The symbiotic biological N2 fixation by A. senegal was estimated using the 15N natural abundance (δ15N) procedure in eight provenances collected from different environments and soil types of the gum arabic belt and grown in clay soil in the Blue Nile region. Balanites aegyptiaca (a non-legume) was used as a non-N-fixing reference tree species, so as to allow 15N-based estimates of the proportion of the nitrogen in trees derived from the atmosphere. In the planted acacia trees, measurements were made on shoot growth, water-use efficiency (as assessed by the δ13C method) and (starting from the third year) gum production. Carbon isotope ratios were obtained from the leaves and branch wood samples. The agroforestry system design caused no statistically significant variation in water use, but the variation was highly significant between years, and the highest water use occurred in the years with high rainfall. No statistically significant differences were found in sorghum or sesame yields when intercropping and sole crop systems were compared (yield averages were 1.54 and 1.54 ha-1 for sorghum and 0.36 and 0.42 t ha-1 for sesame in the intercropped and mono-crop plots, respectively). Thus, at an early stage of agroforestry system management, A. senegal had no detrimental effect on crop yield, but the pattern of resource capture by trees and crops may change as the system matures. Intercropping resulted in taller trees and larger basal and crown diameters as compared to the development of sole trees. It also resulted in a higher land equivalent ratio. When gum yields were analysed it was found that a significant positive relationship existed between the second gum picking and the total gum yield. The second gum picking seems to be a decisive factor in gum production and could be used as an indicator for the total gum yield in a particular year. In trees, the concentrations of N and P were higher in leaves and roots, whereas the levels of K were higher in stems, branches and roots. Soil organic matter, N, P and K contents were highest in the upper soil stratum. There was some indication that the P content slightly increased in the topsoil as the agroforestry plantations aged. At a stocking of 400 trees ha-1 (5 x 5 m spacing), A. senegal accumulated in the biomass a total of 18, 1.21, 7.8 and 972 kg ha-1of N, P, K and OC, respectively. Trees contributed ca. 217 and 1500 kg ha-1 of K and OC, respectively, to the top 25-cm of soil over the first four years of intercropping. Acacia provenances of clay plain origin showed considerable variation in seed weight. They also had the lowest average seed weight as compared to the sandy soil (western) provenances. At the experimental site in the clay soil region, the clay provenances were distinctly superior to the sand provenances in all traits studied but especially in basal diameter and crown width, thus reflecting their adaptation to the environment. Values of δ13C, indicating water use efficiency, were higher in the sand soil group as compared to the clay one, both in leaves and in branch wood. This suggests that the sand provenances (with an average value of -28.07 ) displayed conservative water use and high drought tolerance. Of the clay provenances, the local one (Bout) displayed a highly negative (-29.31 ) value, which indicates less conservative water use that resulted in high productivity at this particular clay-soil site. Water use thus appeared to correspond to the environmental conditions prevailing at the original locations for these provenances. Results suggest that A. senegal provenances from the clay part of the gum belt are adapted for a faster growth rate and higher biomass and gum productivity as compared to provenances from sand regions. A strong negative relationship was found between the per-tree gum yield and water use efficiency, as indicated by δ13C. The differences in water use and gum production were greater among provenance groups than within them, suggesting that selection among rather than within provenances would result in distinct genetic gain in gum yield. The relative δ15N values ( ) were higher in B. aegyptiaca than in the N2-fixing acacia provenances. The amount of Ndfa increased significantly with age in all provenances, indicating that A. senegal is a potentially efficient nitrogen fixer and has an important role in t agroforestry development. The total above-ground contribution of fixed N to foliage growth in 4-year-old A. senegal trees was highest in the Rahad sand-soil provenance (46.7 kg N ha-1) and lowest in the Mazmoom clay-soil provenance (28.7 kg N ha-1). This study represents the first use of the δ15N method for estimating the N input by A. senegal in the gum belt of Sudan. Key words: Acacia senegal, agroforestry, clay plain, δ13C, δ15N, gum arabic, nutrient cycling, Ndfa, Sorghum bicolor, Sesamum indicum

Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.