Planted tree fallows and their influence on soil fertility and maize production in East Africa

Soil fertility depletion is a main constraint to food production in sub-Saharan Africa. This thesis concerns the potential of N2-fixing trees to increase nitrogen inputs to agroforestry systems and accordingly to improve crop production. The suitability of five tropical tree species (including two N2-fixing species, Leucaena leucocephala and Prosopis chilensis) for tree fallows in Tanzania were evaluated by comparing their leaf chemistry, their effects on soil properties and on maize growth. After five years fallow, the per cent total soil N was higher under Prosopis compared to under other tree species. Maize biomass production was higher on soils from Leucaena or Prosopis compared to grass fallow. Prosopis contributed 11% to the total soil C over a period of 8 years. Field experiments in Kenya were performed to test a low-level 15N-tracer technique to estimate biological nitrogen fixation in Sesbania sesban over an 18-months period, and to compare the effects of short-duration tree fallows on two subsequent maize harvests with natural fallow and continuous cropping. We estimated the N derived from atmosphere by Sesbania after 18 months to between 500 and 600 kg ha-1, depending on which plant parts were used for 15N data and on the choice of reference species. We consider the 15N dilution method to be appropriate for quantifying N2 fixation in improved fallows in studies of young trees with high N2-fixing ability. In an experiment examining the effects of tree fallows on subsequent maize crops approximately 70-90% of the N in Sesbania, and 50-70% in Calliandra calothyrsus, was derived from N2-fixation. The quantity of N added by N2-fixation, 280-360 kg N ha-1 for Sesbania and 120-170 kg N ha-1 for Calliandra, resulted in a positive N balance after two cropping seasons of 170-250 kg N ha-1 and 90-140 kg N ha-1 respectively. Both the content of inorganic N in the topsoil and the quantity of N mineralised during rainy seasons were higher after the Sesbania fallows than after the other treatments. The substantial accumulation of N in planted Sesbania demonstrated its potential to increase the sustainability of crop production on N-limited soils

Colour stability and water-holding capacity of M. longissimus and carcass characteristics in fallow deer (Dama dama) grazed on natural pasture or fed barley

The effects of feeding regimen on carcass characteristics, meat colour and water-holding capacity of M. longissimus were studied in 24 female fallow deer (Dama dama). All animals were farm raised; twelve were grazed on pasture and twelve were fed barley and a small amount of hay prior to slaughter. The animals were slaughtered at two occasions (during the Southern Hemisphere spring); after 19 weeks of feeding (n=12; 6 grazing and 6 barley fed animals; group 1) and after 24 weeks of feeding (n=12; 6 grazing and 6 barley fed animals; group 2). The barley/hay-fed deer had significantly higher body condition scores and carcass weights than the pasture raised group. No difference in meat ultimate pH values between the treatment groups was recorded. The meat from the pasture raised deer had significantly longer colour display life after 2 and 3 weeks of refrigerated storage (+ 2.0 ºC) in vacuum bags. There was no difference in drip loss between the two treatment groups. However, significantly lower drip losses were found in meat from the animals in group 2 compared with the ones in group 1 (P ≤ 0.001). It was concluded that the feeding regimen of the animals is an important factor that contributes to the variation in quality of fresh chilled deer meat (venison), mainly the colour stability and display life of vacuum packaged meat.Abstract in Swedish / Sammanfattning: I denna undersökning ingick 24 dovhjortshindar (Dama dama) för att studera effekterna av olika typer av foder (bete och korn) på slaktkroppskvalitet samt färg och vattenhållande förmåga i köttet (M. longissimus). Alla djur var uppfödda på en hjortfarm, 12 betade gräs och 12 utfodrades med korn och en liten mängd hö före slakt. Djuren slaktades vid två olika tillfällen (under våren på det södra halvklotet); efter 19 veckors utfodring (n=12; 6 betesdjur och 6 kornfodrade djur; grupp 1) och efter 24 veckors utfodring (n=12; 6 betesdjur och 6 kornfodrade djur; grupp 2). De dovhjortar som utfodrats med korn och hö var i bättre kondition och hade högre slaktvikter jämfört med de djur som betat gräs. Ingen skillnad i köttets pH-värde mellan de två utfodringsgrupperna kunde dock påvisas. Köttet från de betande dovhjortarna hade bättre färgstabilitet efter lagring i 2 och 3 veckor (+ 2.0 ºC) i vakuumförpackning. Det fanns ingen skillnad mellan kött från betande och korn/hö-utfodrade djur i vattenhållande förmåga. Däremot hade kött från djur i grupp 2 (slaktade efter 24 veckors utfodring) bättre vattenhållande förmåga jämfört med grupp 1 (P ≤ 0.001). Vi kunde konstatera att de olika fodertyperna påverkade kvaliteten hos färskt kyllagrat kött, framförallt färgstabiliteten hos vakuumförpackat kött

Seasonal dynamics of soil respiration and nitrogen mineralization in chronically warmed and fertilized soils

Although numerous studies have examined the individual effects of increased temperatures and N deposition on soil biogeochemical cycling, few have considered how these disturbances interact to impact soil C and N dynamics. Likewise, many have not assessed season-specific responses to warming and N inputs despite seasonal variability in soil processes. We studied interactions among season, warming, and N additions on soil respiration and N mineralization at the Soil Warming × Nitrogen Addition Study at the Harvard Forest. Of particular interest were wintertime fluxes of C and N typically excluded from investigations of soils and global change. Soils were warmed to 5°C above ambient, and N was applied at a rate of 5 g m−2 y−1. Soil respiration and N mineralization were sampled over two years between 2007 and 2009 and showed strong seasonal patterns that mirrored changes in soil temperature. Winter fluxes of C and N contributed between 2 and 17% to the total annual flux. Net N mineralization increased in response to the experimental manipulations across all seasons, and was 8% higher in fertilized plots and 83% higher in warmed plots over the duration of the study. Soil respiration showed a more season-specific response. Nitrogen additions enhanced soil respiration by 14%, but this increase was significant only in summer and fall. Likewise, warming increased soil respiration by 44% over the whole study period, but the effect of warming was most pronounced in spring and fall. The only interaction between warming × N additions took place in autumn, when N availability likely diminished the positive effect of warming on soil respiration. Our results suggest that winter measurements of C and N are necessary to accurately describe winter biogeochemical processes. In addition, season-specific responses to the experimental treatments suggest that some components of the belowground community may be more susceptible to warming and N additions than others. Seasonal changes in the abiotic environment may have also interacted with the experimental manipulations to evoke biogeochemical responses at certain times of year

Fossil fuels, global warming and democracy: a report from a scene of the collision

What happens to democracy when the fossil fuel industry collides with global warming? Introduction Democracy is caught in a collision between two forces: the need to respond to global warming by cutting carbon emissions, and the demands of the fossil fuel industry to increase carbon use and production. This is a slow motion collision that will take decades to conclude, though its ending seems inevitable: coal, and then oil and natural gas, will be replaced by more sustainable energy sources, but only after great damage to the environment. In this paper I explore the question, What happens to democracy when the fossil fuel industry collides with global warming? This collision is already making its marks on democratic practices. The fossil fuel industry is using every tool it can to preserve its wealth and power by pressuring governments, political parties, universities, regulators, courts, and voters. It is a process of tough, aggressive, and sophisticated politics that ultimately depends on denying the evidence that global warming poses a danger that needs to be urgently confronted. Without a theoretical framework to focus this inquiry, it could easily produce little more than a list of anecdotes about politics and influence. The value of good theory is that it reveals the patterns in the evidence, showing how the disparate pieces are connected to one another, and to larger historical, social, and economic factors. In this paper, I drew theory from (among others) Valerie Bunce, Timothy Mitchell, and most importantly Terry Lynn Karl. I use the work of these scholars to focus on the Canadian province of Alberta. Alberta provides an example of what can happen to democracy in places where fossil fuel production predominates. From time-to-time I link the paper to Australia, which depends even more than Canada on mineral extraction, and which is on the burning edge of global warming. This paper should be read as a warning to people everywhere who are concerned about fossil fuel dependence, global warming, and democracy. Those who value democracy must ask, Can democracy as we know it survive global warming

Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models