Role of Crop Research and Development in food Security of Africa

Food security is the main concern in Africa as the production and productivity of crops are under continuous threat. Indigenous crops also known as orphan- or as underutilized- crops provide key contributions to food security under the present scenario of increasing world population and changing climate. Hence, these crops which belong to the major categories of cereals, legumes, fruits and root crops play a key role in the livelihood of the resource-poor farmers and consumers since they perform better than the major world crops under extreme soil and climate conditions prevalent in the continent. These indigenous crops have the major advantage that they fit well into the general socio-economic and ecological context of the region. However, despite their huge importance, African crops have generally received little attention by the global scientific community. With the current production systems, only a fraction of yield potential was achieved for most of these crops. In order to devise strategies towards boosting crop productivity in Africa, the current production constraints should be investigated and properly addressed. Key traits known to increase productivity and/or improve nutrition and diverse conventional and modern crop improvement techniques need to be implemented. Commitments in the value-chain from the research, production, marketing to distribution of improved seeds are required by relevant national and international institutions as well as African governments to promote food security in a sustainable manner. The review also presents major achievements and suggestions for stakeholders interested in African agriculture.

An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

Justification of Data Warehousing Projects

Project justification is regarded as one of the major methodological deficits in Data Warehousing practice. As reasons for applying inappropriate methods, performing incomplete evaluations, or even entirely omitting justifications, the special nature of Data Warehousing benefits and the large portion of infrastructure-related activities are stated. In this paper, the economic justification of Data Warehousing projects is analyzed, and first results from a large academiaindustry collaboration project in the field of non-technical issues of Data Warehousing are presented. As conceptual foundations, the role of the Data Warehouse system in corporate application architectures is analyzed, and the specific properties of Data Warehousing projects are discussed. Based on an applicability analysis of traditional approaches to economic IT project justification, basic steps and responsibilities for the justification of Data Warehousing projects are derived.

Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

Evaluation of therapist and client language in Motivational Interviewing (MI) sessions: A secondary analysis of data from the Southern Methodist Alcohol Research Trial (SMART) study

In the United States, “binge” drinking among college students is an emerging public health concern due to the significant physical and psychological effects on young adults. The focus is on identifying interventions that can help decrease high-risk drinking behavior among this group of drinkers. One such intervention is Motivational interviewing (MI), a client-centered therapy that aims at resolving client ambivalence by developing discrepancy and engaging the client in change talk. Of late, there is a growing interest in determining the active ingredients that influence the alliance between the therapist and the client. This study is a secondary analysis of the data obtained from the Southern Methodist Alcohol Research Trial (SMART) project, a dismantling trial of MI and feedback among heavy drinking college students. The present project examines the relationship between therapist and client language in MI sessions on a sample of “binge” drinking college students. Of the 126 SMART tapes, 30 tapes (‘MI with feedback’ group = 15, ‘MI only’ group = 15) were randomly selected for this study. MISC 2.1, a mutually exclusive and exhaustive coding system, was used to code the audio/videotaped MI sessions. Therapist and client language were analyzed for communication characteristics. Overall, therapists adopted a MI consistent style and clients were found to engage in change talk. Counselor acceptance, empathy, spirit, and complex reflections were all significantly related to client change talk (p-values ranged from 0.001 to 0.047). Additionally, therapist ‘advice without permission’ and MI Inconsistent therapist behaviors were strongly correlated with client sustain talk (p-values ranged from 0.006 to 0.048). Simple linear regression models showed a significant correlation between MI consistent (MICO) therapist language (independent variable) and change talk (dependent variable) and MI inconsistent (MIIN) therapist language (independent variable) and sustain talk (dependent variable). The study has several limitations such as small sample size, self-selection bias, poor inter-rater reliability for the global scales and the lack of a temporal measure of therapist and client language. Future studies might consider a larger sample size to obtain more statistical power. In addition the correlation between therapist language, client language and drinking outcome needs to be explored.^

Standardization of data collection for the Lifetime Surveillance of Astronaut Health (LSAH)

Problems due to the lack of data standardization and data management have lead to work inefficiencies for the staff working with the vision data for the Lifetime Surveillance of Astronaut Health. Data has been collected over 50 years in a variety of manners and then entered into a software. The lack of communication between the electronic health record (EHR) form designer, epidemiologists, and optometrists has led to some level to confusion on the capability of the EHR system and how its forms can be designed to fit all the needs of the relevant parties. EHR form customizations or form redesigns were found to be critical for using NASA's EHR system in the most beneficial way for its patients, optometrists, and epidemiologists. In order to implement a protocol, data being collected was examined to find the differences in data collection methods. Changes were implemented through the establishment of a process improvement team (PIT). Based on the findings of the PIT, suggestions have been made to improve the current EHR system. If the suggestions are implemented correctly, this will not only improve efficiency of the staff at NASA and its contractors, but set guidelines for changes in other forms such as the vision exam forms. Because NASA is at the forefront of such research and health surveillance the impact of this management change could have a drastic improvement on the collection of and adaptability of the EHR. Accurate data collection from this 50+ year study is ongoing and is going to help current and future generations understand the implications of space flight on human health. It is imperative that the vast amount of information is documented correctly.^

Overview of Data Management Planning Tools

These Data Management Plans are more comprehensive and complex than in the past. Libraries around the nation are trying to put together tools to help researchers write plans that conform to the new requirements. This session will look at some of these tools.

Collection of data on plant height along the plant diversity gradient in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains a time series of plant height measurements (vegetative and reproductive) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In addition, data on species specific plant heights for the main experiment are available from 2002. In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Plant height was recorded, generally, twice a year just before biomass harvest (during peak standing biomass in late May and in late August). Methodologies of measuring height have varied somewhat over the years. In earlier year the streched plant height was measured, while in later years the standing height without streching the plant was measured. Vegetative height was measured either as the height of the highest leaf or as the length of the main axis of non-flowering plants. Regenerating height was measured either as the height of the highest flower on a plant or as the height of the main axis of flowering. Sampled plants were either randomly selected in the core area of plots or along transects in defined distances. For details refer to the description of individual years. Starting in 2006, also the plots of the management experiment, that altered mowing frequency and fertilized subplots (see further details in the general description of the Jena Experiment) were sampled. 2. Species specific plant height was recorded two times in 2002: in late July (vegetative height) and just before biomass harvest during peak standing biomass in late August (vegetative and regenerative height). For each plot and each sown species in the species pool, 3 plant individuals (if present) from the central area of the plots were randomly selected and used to measure vegetative height (non-flowering indviduals) and regenerative height (flowering individuals) as stretched height. Provided are the means over the three measuremnts per plant species per plot.

Collection of data on soil carbon (particulate and dissolved) in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains three time series of measurements of soil carbon (particular and dissolved) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Particulate soil carbon: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. Total carbon concentration was analyzed on ball-milled subsamples by an elemental analyzer at 1150°C. Inorganic carbon concentration was measured by elemental analysis at 1150°C after removal of organic carbon for 16 h at 450°C in a muffle furnace. Organic carbon concentration was calculated as the difference between both measurements of total and inorganic carbon. 2. Particulate soil carbon (high intensity sampling): In one block of the Jena Experiment soil samples were taken to a depth of 1 m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling. 3. Dissolved organic carbon: Suction plates installed on the field site in 10, 20, 30 and 60 cm depth were used to sample soil pore water. Cumulative soil solution was sampled biweekly and analyzed for dissolved organic carbon concentration by a high TOC elemental analyzer. Annual mean values of DOC are provided.

Collection of data on particulate and dissolved soil phosphorus in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains two time series of measurements of dissolved phosphorus (organic, inorganic and total with a biweekly resolution) and dissolved inorganic phosphorus with a seasonal resolution. In addition, data on phosphorus from soil samples measured in 2007 and fractionated by different acid-extrations (Hedley fractions) are provided. All data measured at the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Dissolved phosphorus in soil solution: Suction plates installed on the field site in 10, 20, 30 and 60 cm depth were used to sample soil pore water. Cumulatively extracted soil solution was collected every two weeks from October 2002 to May 2006. The biweekly samples from 2002, 2003 and 2004 were analyzed for dissolved organic phosphorus (DOP), dissolved inorganic phosphorus (PO4P) and dissolved total phosphorus (TDP) by Continuous Flow Analyzer (CFA SAN ++, SKALAR [Breda, The Netherlands]). 2. Seasonal values of dissolved inorganic phosphorus in soil solution were calculated as volume-weighted mean values of the biweekly measurements (spring = March to May, summer = June to August, fall = September to November, winter = December to February). 3. Phosphorus fractions in soil: Five independent soil samples per plot were taken in a depth of 0-15 cm using a soil corer with an inner diameter of 1 cm. The five samples per plot were combined to one composite sample per plot. A four-step sequential P fractionation (Hedley fractions) was applied and concentrations of P fractions in soil were measured photometrically (molybdenum blue-reactive P) with a Continuous Flow Analyzer (Bran&Luebbe, Germany).

Collection of data on particulate and dissolved soil nitrogen in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains four time series of particulate and dissolved soil nitrogen measurements from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Total nitrogen from solid phase: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. In 2002 five samples per plot were taken and analyzed independently. Averaged values per depth layer are reported. In later years, three samples per plot were taken, pooled in the field, and measured as a combined sample. Sampling locations were less than 30 cm apart from sampling locations in other years. All soil samples were passed through a sieve with a mesh size of 2 mm in 2002. In later years samples were further sieved to 1 mm. No additional mineral particles were removed by this procedure. Total nitrogen concentration was analyzed on ball-milled subsamples (time 4 min, frequency 30 s-1) by an elemental analyzer at 1150°C (Elementaranalysator vario Max CN; Elementar Analysensysteme GmbH, Hanau, Germany). 2. Total nitrogen from solid phase (high intensity sampling): In block 2 of the Jena Experiment, soil samples were taken to a depth of 1m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling but were always analyzed independently and never pooled. 3. Mineral nitrogen from KCl extractions: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m (and between 2002 and 2004 also at a depth of 0.15 to 0.3 m) of the mineral soil from each of the experimental plots at various times over the years. In addition also plots of the management experiment, that altered mowing frequency and fertilized subplots (see further details below) were sampled in some later years. Samples of the soil cores per plot (subplots in case of the management experiment) were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, 2003-2005: Skalar, Breda, Netherlands; 2006-2007: AutoAnalyzer, Seal, Burgess Hill, United Kingdom). 4. Dissolved nitrogen in soil solution: Glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in April 2002 in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-), ammonium (NH4+) and total dissolved nitrogen concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+).

Linked Opinions: Describing Sentiments on the Structured Web of Data

In the paper we report on the results of our experiments on the construction of the opinion ontology. Our aim is to show the benefits of publishing in the open, on the Web, the results of the opinion mining process in a structured form. On the road to achieving this, we attempt to answer the research question to what extent opinion information can be formalized in a unified way. Furthermore, as part of the evaluation, we experiment with the usage of Semantic Web technologies and show particular use cases that support our claims.

Cross-lingual Linking on the Multilingual Web of Data (position statement)

Recently, the Semantic Web has experienced signi�cant advancements in standards and techniques, as well as in the amount of semantic information available online. Even so, mechanisms are still needed to automatically reconcile semantic information when it is expressed in di�erent natural languages, so that access to Web information across language barriers can be improved. That requires developing techniques for discovering and representing cross-lingual links on the Web of Data. In this paper we explore the different dimensions of such a problem and reflect on possible avenues of research on that topic.

Challenges for the Multilingual Web of Data

The Web has witnessed an enormous growth in the amount of semantic information published in recent years. This growth has been stimulated to a large extent by the emergence of Linked Data. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in different languages. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages. The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. In this sense, the multilingual Web of Data can be realized in our view as a layer of services and resources on top of the existing Linked Data infrastructure adding i) linguistic information for data and vocabularies in different languages, ii) mappings between data with labels in different languages, and iii) services to dynamically access and traverse Linked Data across different languages. In this article we present this vision of a multilingual Web of Data. We discuss challenges that need to be addressed to make this vision come true and discuss the role that techniques such as ontology localization, ontology mapping, and cross-lingual ontology-based information access and presentation will play in achieving this. Further, we propose an initial architecture and describe a roadmap that can provide a basis for the implementation of this vision.

Cross‐lingual link discovery in the Web of Data

Cross‐lingual link discovery in the Web of Data