Variability of African Farming Systems from Phenological Analysis of NDVI Time Series

Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa

Exploring Connections between Global Climate Indices and African Vegetation Phenology

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the continent in Africa. Agriculturally destructive droughts and floods are monitored from space using satellite remote sensing by organizations seeking to provide quantitative and predictive information about food security crises. Better knowledge on the relation between climate indices and food production may increase the use of these indices in famine early warning systems and climate outlook forums on the continent. Here we explore the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), the Multivariate ENSO Index (MEI) and the Southern Oscillation Index (SOI). We explore spatial relationships between growing conditions as measured by the NDVI and the five climate indices in Eastern, Western and Southern Africa to determine the regions and periods when they have a significant impact. The focus is to provide a clear indication as to which climate index has the most impact on the three regions during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by variations in the climate indices. The particular climate index and the timing showing highest correlation depended heavily on the region examined. The research shows that climate indices can contribute to understanding growing season variability in Eastern, Western and Southern Africa

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product

One-carbon metabolism biomarkers and risk of urothelial cell carcinoma in the European prospective investigation into cancer and nutrition

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Uphill and downhill walking in unilateral lower limb amputees

Objective: To study adjustment strategies in unilateral amputees in uphill and downhill walking. Design: observational cohort study. Subjects: Seven transfemoral, 12 transtibial unilateral amputees and 10 able-bodied subjects. Methods: In a motion analysis laboratory the subjects walked over a level surface and an uphill and downhill slope. Gait velocity and lower limb joint angles were measured. Results: In uphill walking hip and knee flexion at initial contact and hip flexion in swing were increased in the prosthetic limb of transtibial amputees. In downhill walking transtibial amputees showed more knee flexion on the prosthetic side in late stance and swing. Transfemoral amputees were not able to increase prosthetic knee flexion in uphill and downhill walking. An important adjustment strategy in both amputee Groups was a smaller hip extension in late stance in uphill and downhill walking, probably related with a shorter step length. In addition, amputees increased knee flexion in early stance in the non-affected limb in uphill walking to compensate for the shorter prosthetic limb length. In downhill walking fewer adjustments were necessary, since the shorter prosthetic limb already resulted in lowering of the body. Conclusion: Uphill and downhill walking can be trained in rehabilitation, which may improve safety and confidence of amputees. Prosthetic design should focus on better control of prosthetic knee flexion abilities without reducing stability. (c) 2007 Elsevier B.V. All fights reserved

Attract and deter: a dual role for pyrrolizidine alkaloids in plant–insect interactions

Pyrrolizidine alkaloids (PAs) are the major defense compounds of plants in the Senecio genus. Here I will review the effects of PAs in Senecio on the preference and performance of specialist and generalist insect herbivores. Specialist herbivores have evolved adaptation to PAs in their host plant. They can use the alkaloids as cue to find their host plant and often they sequester PAs for their own defense against predators. Generalists, on the other hand, can be deterred by PAs. PAs can also affect survival of generalist herbivores. Usually generalist insects avoid feeding on young Senecio leaves, which contain a high concentration of alkaloids. Structurally related PAs can differ in their effects on insect herbivores, some are more toxic than others. The differences in effects of PAs on specialist and generalists could lead to opposing selection on PAs, which may maintain the genetic diversity in PA concentration and composition in Senecio species

Analysis of Gene Expression Using Gene Sets Discriminates Cancer Patients with and without Late Radiation Toxicity

BACKGROUND: Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%–10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late radiation toxicity. METHODS AND FINDINGS: Prostate carcinoma patients without evidence of cancer 2 y after curative radiotherapy were recruited in the study. Blood samples of 21 patients with severe late complications from radiation and 17 patients without symptoms were collected. Stimulated peripheral lymphocytes were mock-irradiated or irradiated with 2-Gy X-rays. The 24-h radiation response was analyzed by gene expression profiling and used for classification. Classification was performed either on the expression of separate genes or, to augment the classification power, on gene sets consisting of genes grouped together based on function or cellular colocalization. X-ray irradiation altered the expression of radio-responsive genes in both groups. This response was variable across individuals, and the expression of the most significant radio-responsive genes was unlinked to radiation toxicity. The classifier based on the radiation response of separate genes correctly classified 63% of the patients. The classifier based on affected gene sets improved correct classification to 86%, although on the individual level only 21/38 (55%) patients were classified with high certainty. The majority of the discriminative genes and gene sets belonged to the ubiquitin, apoptosis, and stress signaling networks. The apoptotic response appeared more pronounced in patients that did not develop toxicity. In an independent set of 12 patients, the toxicity status of eight was predicted correctly by the gene set classifier. CONCLUSIONS: Gene expression profiling succeeded to some extent in discriminating groups of patients with and without severe late radiotherapy toxicity. Moreover, the discriminative power was enhanced by assessment of functionally or structurally related gene sets. While prediction of individual response requires improvement, this study is a step forward in predicting susceptibility to late radiation toxicity

Successful learning: balancing self-regulation with instructional planning

Many recent studies have stressed the importance of teacher candidates’ (TCs) self-regulated learning (SRL) skills for successful learning. Because of the promising consequences of SRL for academic performance, teacher educators (TEs) are encouraged to increase TCs’ SRL opportunities in educational programs. Because of the difficulty and complexity for TEs to successfully guide TCs towards SRL, the present study contributes to the discussion how to best facilitate TEs in finding a balance between student- and teacher-control. For this purpose, a conceptual model is presented. The model draws upon literature related to the perspective of the learner, the teacher and the learning task. Besides the context of teacher education, the model is beneficial for higher education as well as teaching and teacher professionalization. It will help instructors provide a more balanced approach between teacherand student-controlled learning, and support students develop essential SRL skills

Extraction of bodily features for gait recognition and gait attractiveness evaluation

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE