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Context 1
... household roster can then be asked for like in Table 1, following the setup used in the LSMS-ISA survey. ...Context 2
... if answered with 'yes', be followed by more detailed questions regarding the quantity of fish caught, produced, consumed and sold, see Table 11. 'During the last 3 years, did you experience any major (abnormal) mortality of fish that affected your production, incomes and livelihood?' ...Context 3
... indicate that this leads to lower under-reporting, with increased overall reliability of the survey results ( Fraval et al., 2019). Collecting information on spending can be approached in a similar way through the relative assessment way of asking as in Table 12. Costs are difficult to capture in single survey application, without diving deeply into individual activities and their associated costs. ...Context 4
... is beyond the scope of this report. The most basic information that can be collected on spending is given in Table 13. This basic set of questions, which gives insight into whether earnings are being re-invested into the farm or mostly spent on the livelihood, can be expanded upon to get either a relative importance of each of the spending categories, or even can be asked for in absolute terms (but the latter with all the problems associated to asking questions about money in absolute terms in a one visit survey). ...Context 5
... there is no other generically applicable asset system available at the moment, which is why we chose to incorporate it in this report. We hereby give the example PPI for Tanzania in Table 14. ...Context 6
... man of household -Senior woman of household -Male Child or Youth -Female Child or Youth -Other family member (male) -Other family member (female) Who owns the (cattle/goats/sheep/chickens/etc)? With the same options as above. Table 15 and 16 give an overview how such expanded land and livestock tables look like Table 15. Basic land use questions, including sex-disaggregated information (based on Table 2) Starting question (M = Multiple options possible) Does your h/h own land, rent land, use common land (for growing crops or grazing animals)? ...Context 7
... off farm income one can also ask question number two, on who decides on how to use the money generated by off farm income. These questions can be added as extra columns to Tables 5, 9 and 10, and as extra row to Table 12. ...Context 8
... back over the last MONTH. Was there a time when, because of lack of money or other resources you personally… In the closed form the enumerator asks directly whether a certain food group was consumed by listing example foods that are relevant for the region where the survey is executed and asking whether one or more of these food items were consumed over the last 24h (see Table 17). ...Similar publications
The extent of tobacco cultivation remains substantially high in Bangladesh, which is the
12th largest tobacco producer in the world. Using data from a household survey of current, former,
and never tobacco farmers, based on a multi-stage stratified sampling design with a mix of purposive
and random sampling of households, this study estimated the f...
Citations
... In the paper the importance of ontologies as formalized knowledge and relationships within knowledge domains was mentioned. For socio-economic household data a socioeconomic ontology is under development, commonly known as SEOnt (Arnaud et al., 2020;Kim et al., under review), that initially links to a set of standardized survey questions, commonly known as 100Q (van Wijk et al., 2019), that builds on the RHOMIS approach (Hammond et al., 2017). SEOnt is a socioeconomic ontology of controlled vocabularies, classifications, and concordances that allow standardization of key indicators, including gender-related indicators. ...
This paper presents a lightweight, flexible, extensible, machine readable and human-intelligible metadata schema that does not depend on a specific ontology. The metadata schema for metadata of data files is based on the concept of data lakes where data is stored as they are. The purpose of the schema is to enhance data interoperability. The lack of interoperability of messy socio-economic datasets that contain a mixture of structured, semi-structured, and unstructured data means that many datasets are underutilized. Adding a minimum set of rich metadata and describing new and existing data dictionaries in a standardized way goes a long way to make these high-variety datasets interoperable and reusable and hence allows timely and actionable information to be gleaned from those datasets. The presented metadata schema OIMS can help to standardize the description of metadata. The paper introduces overall concepts of metadata, discusses design principles of metadata schemes, and presents the structure and an applied example of OIMS.
... There are several initiatives working toward standardized surveys on smallholders. The World Bank's LSMS-ISA (Osabohien, 2018), the Rural Household Multi-Indicator Survey (Hammond et al., 2017) and the CGIAR's 100Q initiative (van Wijk et al., 2019). The LSMS-ISA is a detailed survey, consisting of multiple rounds of data collection. ...
A systematic review of recent publications was conducted to assess the extent to which contemporary micro-level research on smallholders facilitates data re-use and knowledge synthesis. Following PRISMA standards for systematic review, 1,182 articles were identified (published between 2018 and 2020), and 261 articles were selected for review in full. The themes investigated were: (i) data management, including data source, variables collected, granularity, and availability of the data; (ii) the statistical methods used, including analytical approach and reproducibility; and (iii) the interpretation of results, including the scope and objectives of the study, development issues addressed, scale of recommendations made relative to the scale of the sample, and the audience for recommendations. It was observed that household surveys were the most common data source and tended to be representative at the local (community) level. There was little harmonization of the variables collected between studies. Over three quarters of the studies (77%) drew on data which was not in the public domain, 14% published newly open data, and 9% drew on datasets which were already open. Other than descriptive statistics, linear and logistic regression methods were the most common analytical method used (64% of articles). In the vast majority of those articles, regression was used as an explanatory tool, as opposed to a predictive tool. More than half of the articles (59%) made claims or recommendations which extended beyond the coverage of their datasets. In combination these two common practices may lead to erroneous understanding: the tendency to rely upon simple regressions to explain context-specific and complex associations; and the tendency to generalize beyond the remit of the data collected. We make four key recommendations: (1) increased data sharing and variable harmonization would enable data to be re-used between studies; (2) providing detailed meta-data on sampling frames and study-context would enable more powerful meta-analyses; (3) methodological openness and predictive modeling could help test the transferability of approaches; (4) more precise language in study conclusions could help decision makers understand the relevance of findings for policy planning. Following these practices could leverage greater benefits from the substantial investment already made in data collection on smallholder farms.
... The set of questions consists of the following sections: household composition and characteristics, farm characteristics, land availability and use, livestock availability and use, income and assets, gender, food security and dietary diversity, and other aspects. 17 The Ontologies and SED CoPs are working together to identify concepts from the survey questions and results which will be used to form the new SEOnt. SEOnt will provide concepts and variables to the survey forms to annotate the data collected with the 100 questions, while taking into account the sensitive nature of the personal information. ...
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.
... The current state of affairs limits our ability to compare outcomes across studies and to draw general conclusions on the effectiveness of interventions and the trade-offs between outcomes, which may be shaped by household structure, farm management and the wider social-environmental context 3,5 . Efforts like the CGIAR's Big Data Platform have also recognized this situation, and try to define common layouts for household surveys and sets of ontologies underpinning the information to be collected in household surveys 7 . ...
The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socioeconomic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity access Scale, and household dietary diversity. these variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on-and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.
... hosting two relevant communities of practice in the context of the current review: one on crop modeling and one on socioeconomic data. The community of practice on socioeconomic data supports increasing availability of interoperable data sets at farm household level based on surveys (Van Wijk et al., 2019). ...
International crop‐related research as conducted by the CGIAR uses crop modeling for a variety of purposes. By linking crop models with economic models and approaches, crop model outputs can be effectively used as inputs into socio‐economic modeling efforts for priority setting and policy advice using ex‐ante impact assessment of technologies and scenario analysis. This requires interdisciplinary collaboration and very often collaboration across a variety of research organizations. This study highlights the key topics, purposes, and approaches of socio‐economic analysis within the CGIAR related to cropping. Although each CGIAR center has a different mission, all CGIAR centers share a common strategy of striving towards a world free of hunger, poverty, and environmental degradation. This means research is mostly focused towards resource‐constrained smallholder farmers. The review covers global modeling efforts using the IMPACT model to farm household bio‐economic models for assessing the potential impact of new technologies on farming systems and livelihoods. Although the CGIAR addresses all aspects of food systems, the focus of this review is on crop commodities and the economic analysis linked to crop‐growth model results. This study, while not a comprehensive review, provides insights into the richness of the socio‐economic modeling endeavors within the CGIAR. The study highlights the need for interdisciplinary approaches to address the challenges this type of modeling faces. This article is protected by copyright. All rights reserved
... Planteome is developing a Plant Stress Ontology (PSO) that will requireThe CGIAR and partners perform a large number of agricultural household surveys yielding important data and statistics on the socioeconomic status, production systems and environment of smallholders in the developing world. The Socio-Economic Data (SED) CoP 30 , created the '100Q Working Group' that developed 100 core questions to be included in household surveys to collect consistent information on key socioeconomic indicators and consists of the following sections: household composition and characteristics, farm characteristics, land availability and use, livestock availability and use, income and assets, gender, food security and dietary diversity and other aspects(Van Wijk et al., 2019). The Ontologies and the SED CoPs are working together to identify concepts that will form the new Socioeconomic Ontology (SociO). ...
Tradeoff Analysis (TOA) is an approach to positive analysis that combines foresight analysis and simulation modeling tools from the relevant disciplines, including economics, in a participatory process designed to formulate and evaluate forward-looking, strategic decisions under high levels of uncertainty in complex systems. We motivate TOA with a prototype framework for the design and evaluation of public-good agricultural research for sustainable development. We discuss the advantages of TOA over conventional economic analysis-Benefit-Cost Analysis-for the design and evaluation of sustainable development pathways. The remainder of the paper describes the currently available modeling tools and their strengths and limitations for use in TOA, and illustrates recent applications with cross-scale case studies. We conclude with a discussion of the opportunities and challenges for the use of foresight analysis and TOA in research priority setting and management at global and project levels, using the case of the CGIAR to illustrate.
