Nitrogen dynamics and nitrogen use efficiency of spring cereals under Finnish growing conditions

Nitrogen (N) is one of the main inputs in cereal cultivation and as more than half of the arable land in Finland is used for cereal production, N has contributed substantially to agricultural pollution through fertilizer leaching and runoff. Based on this global phenomenon, the European Community has launched several directives to reduce agricultural emissions to the environment. Trough such measures, and by using economic incentives, it is expected that northern European agricultural practices will, in the future, include reduced N fertilizer application rates. Reduced use of N fertilizer is likely to decrease both production costs and pollution, but could also result in reduced yields and quality if crops experience temporary N deficiency. Therefore, more efficient N use in cereal production, to minimize pollution risks and maximize farmer income, represents a current challenge for agronomic research in the northern growing areas. The main objective of this study was to determine the differences in nitrogen use efficiency (NUE) among spring cereals grown in Finland. Additional aims were to characterize the multiple roles of NUE by analysing the extent of variation in NUE and its component traits among different cultivars, and to understand how other physiological traits, especially radiation use efficiency (RUE) and light interception, affect and interact with the main components of NUE and contribute to differences among cultivars. This study included cultivars of barley (Hordeum vulgare L.), oat (Avena sativa L.) and wheat (Triticum aestivum L.). Field experiments were conducted between 2001 and 2004 at Jokioinen, in Finland. To determine differences in NUE among cultivars and gauge the achievements of plant breeding in NUE, 17-18 cultivars of each of the three cereal species released between 1909 and 2002 were studied. Responses to nitrogen of landraces, old cultivars and modern cultivars of each cereal species were evaluated under two N regimes (0 and 90 kg N ha-1). Results of the study revealed that modern wheat, oat and barley cultivars had similar NUE values under Finnish growing conditions and only results from a wider range of cultivars indicated that wheat cultivars could have lower NUE than the other species. There was a clear relationship between nitrogen uptake efficiency (UPE) and NUE in all species whereas nitrogen utilization efficiency (UTE) had a strong positive relationship with NUE only for oat. UTE was clearly lower in wheat than in other species. Other traits related to N translocation indicated that wheat also had a lower harvest index, nitrogen harvest index and nitrogen remobilisation efficiency and therefore its N translocation efficiency was confirmed to be very low. On the basis of these results there appears to be potential and also a need for improvement in NUE. These results may help understand the underlying physiological differences in NUE and could help to identify alternative production options, such as the different roles that species can play in crop rotations designed to meet the demands of modern agricultural practices.

Simulation and graph mining tools for improving gene mapping efficiency

Gene mapping is a systematic search for genes that affect observable characteristics of an organism. In this thesis we offer computational tools to improve the efficiency of (disease) gene-mapping efforts. In the first part of the thesis we propose an efficient simulation procedure for generating realistic genetical data from isolated populations. Simulated data is useful for evaluating hypothesised gene-mapping study designs and computational analysis tools. As an example of such evaluation, we demonstrate how a population-based study design can be a powerful alternative to traditional family-based designs in association-based gene-mapping projects. In the second part of the thesis we consider a prioritisation of a (typically large) set of putative disease-associated genes acquired from an initial gene-mapping analysis. Prioritisation is necessary to be able to focus on the most promising candidates. We show how to harness the current biomedical knowledge for the prioritisation task by integrating various publicly available biological databases into a weighted biological graph. We then demonstrate how to find and evaluate connections between entities, such as genes and diseases, from this unified schema by graph mining techniques. Finally, in the last part of the thesis, we define the concept of reliable subgraph and the corresponding subgraph extraction problem. Reliable subgraphs concisely describe strong and independent connections between two given vertices in a random graph, and hence they are especially useful for visualising such connections. We propose novel algorithms for extracting reliable subgraphs from large random graphs. The efficiency and scalability of the proposed graph mining methods are backed by extensive experiments on real data. While our application focus is in genetics, the concepts and algorithms can be applied to other domains as well. We demonstrate this generality by considering coauthor graphs in addition to biological graphs in the experiments.

Microfinance, Efficiency and Agricultural Production in Bangladesh

The objectives of this study were to make a detailed and systematic empirical analysis of microfinance borrowers and non-borrowers in Bangladesh and also examine how efficiency measures are influenced by the access to agricultural microfinance. In the empirical analysis, this study used both parametric and non-parametric frontier approaches to investigate differences in efficiency estimates between microfinance borrowers and non-borrowers. This thesis, based on five articles, applied data obtained from a survey of 360 farm households from north-central and north-western regions in Bangladesh. The methods used in this investigation involve stochastic frontier (SFA) and data envelopment analysis (DEA) in addition to sample selectivity and limited dependent variable models. In article I, technical efficiency (TE) estimation and identification of its determinants were performed by applying an extended Cobb-Douglas stochastic frontier production function. The results show that farm households had a mean TE of 83% with lower TE scores for the non-borrowers of agricultural microfinance. Addressing institutional policies regarding the consolidation of individual plots into farm units, ensuring access to microfinance, extension education for the farmers with longer farming experience are suggested to improve the TE of the farmers. In article II, the objective was to assess the effects of access to microfinance on household production and cost efficiency (CE) and to determine the efficiency differences between the microfinance participating and non-participating farms. In addition, a non-discretionary DEA model was applied to capture directly the influence of microfinance on farm households production and CE. The results suggested that under both pooled DEA models and non-discretionary DEA models, farmers with access to microfinance were significantly more efficient than their non-borrowing counterparts. Results also revealed that land fragmentation, family size, household wealth, on farm-training and off farm income share are the main determinants of inefficiency after effectively correcting for sample selection bias. In article III, the TE of traditional variety (TV) and high-yielding-variety (HYV) rice producers were estimated in addition to investigating the determinants of adoption rate of HYV rice. Furthermore, the role of TE as a potential determinant to explain the differences of adoption rate of HYV rice among the farmers was assessed. The results indicated that in spite of its much higher yield potential, HYV rice production was associated with lower TE and had a greater variability in yield. It was also found that TE had a significant positive influence on the adoption rates of HYV rice. In article IV, we estimated profit efficiency (PE) and profit-loss between microfinance borrowers and non-borrowers by a sample selection framework, which provided a general framework for testing and taking into account the sample selection in the stochastic (profit) frontier function analysis. After effectively correcting for selectivity bias, the mean PE of the microfinance borrowers and non-borrowers were estimated at 68% and 52% respectively. This suggested that a considerable share of profits were lost due to profit inefficiencies in rice production. The results also demonstrated that access to microfinance contributes significantly to increasing PE and reducing profit-loss per hectare land. In article V, the effects of credit constraints on TE, allocative efficiency (AE) and CE were assessed while adequately controlling for sample selection bias. The confidence intervals were determined by the bootstrap method for both samples. The results indicated that differences in average efficiency scores of credit constrained and unconstrained farms were not statistically significant although the average efficiencies tended to be higher in the group of unconstrained farms. After effectively correcting for selectivity bias, household experience, number of dependents, off-farm income, farm size, access to on farm training and yearly savings were found to be the main determinants of inefficiencies. In general, the results of the study revealed the existence substantial technical, allocative, economic inefficiencies and also considerable profit inefficiencies. The results of the study suggested the need to streamline agricultural microfinance by the microfinance institutions (MFIs), donor agencies and government at all tiers. Moreover, formulating policies that ensure greater access to agricultural microfinance to the smallholder farmers on a sustainable basis in the study areas to enhance productivity and efficiency has been recommended. Key Words: Technical, allocative, economic efficiency, DEA, Non-discretionary DEA, selection bias, bootstrapping, microfinance, Bangladesh.