Person: Gotor, E.
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Gotor
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E.
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Gotor, E.
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0000-0003-0533-3077 5 results
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- Advanced spatial analytics for policy support: Use cases from One CGIAR(CGIAR, 2025) Chun Song; Petsakos, A.; Mishra, A.; Mwungu, C.M.; Mbabazi, G.; Bisrat Gebrekidan; Chamberlin, J.; Mkondiwa, M.; Shuang Zhou; Zhe Guo; Liangzhi You; Thomas, T.S.; Otieno, F.; Wanjau, A.; Ghosh, A.; Robertson, R.; Pede, V.O.; Lenaerts, B.; Yego, F.; Gotor, E.; González, C.E.; Cenacchi, N.; Xinsheng Diao
Publication - Bridging the gap: Integrating crop pests and pathogens into agricultural foresight models for food security assessments(Springer, 2025) Petsakos, A.; Montes, C.; Falck-Zepeda, J.B.; Pequeno, D.N.L.; Schiek, B.; Gotor, E.
Publication - Towards a core approach for cross-sectional farm household survey data collection: a tiered setup for quantifying key farm and livelihood indicators(CGIAR Platform for Big Data in Agriculture, 2019) Wijk, M. van; Alvarez, C.; Anupama, G.; Arnaud, E.; Azzarri, C.; Burra, D.; Caracciolo, F.; Coomes, D.; Garbero, A.; Gotor, E.; Heckert, J.; Johnson, N.; Soonho Kim; Miro, B.; Muliro, J.; Shikuku, K.M.; Tyszler, M.; Valdivia, R.; Viviani, S.; Vrolijk, H.; Kruseman, G.There is an urgent need to improve the characterisation of agricultural systems at household level to enable a more efficient assessment of the capacity households to adopt a range of agricultural intervention options. Local drivers and factors need to be identified that might constrain or provide opportunities within a specified agricultural system (Carletto et al., 2015), while on the other hand generalisable standardized characteristics need to be identified that would allow robust comparisons between different systems (Frelat et al., 2016; van Wijk et al., 2014). The assessment of opportunities at smallholder farm household level to improve their livelihoods needs integration of validated standardised agricultural, poverty, nutrition and gender indicators in the quantitative characterisation of these households. This will allow us to assess how these welfare indicators vary across a farm household population and across different agro-ecological and socioeconomic conditions. Such data would also allow us to better assess how they may change over time. Furthering such a standardization across all institutes within the CGIAR (who have been estimated to conduct baseline interviews with around 180,000 farmers per year) would allow for much easier application of big data method applications for analyzing the household level data themselves, as well as for linking these data to other larger scale information sources like spatial crop yield data, climate data, market access data, roadmap data, etc. The Big Data platform of the CGIAR has therefore stimulated an effort to define how a common core of a cross-sectional household survey focusing on rural households could look like, the so-called 100Q exercise (with 100Q standing for 100 Questions that that core should contain). The core survey should deliver key information around the agricultural activities and off farm income of the household, as well as key welfare indicators focusing on poverty, food security, dietary diversity and gender equity. Within this effort a workshop was held in Rome, Italy, in December 2018, where a group of scientists from different centers of the CGIAR and partner institutions discussed how such a core approach for cross-sectional surveys could look, and what type of information should be captured. This report is a short reflection of what was discussed during this workshop, and tries to summarize the overall conclusions of this workshop into core modules of key aspects and indicators of rural farm livelihoods. This information can be used as building blocks for survey development, thereby resulting in more harmonized household survey data collection across CGIAR centers.
Publication - Exploring opportunities around climate-smart breeding for future food and nutrition security(CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS), 2019) Balié, J.; Cramer, L.; Friedmann, M.; Gotor, E.; Jones, C.S.; Kozicka, M.; Kruseman, G.; Notenbaert, A.; Place, F.; Rebolledo, C.; Thornton, P.; Wiebe, K.Foresight activities that include participatory processes as well as careful analysis can help address the great uncertainties concerning the future of food systems and the role of crop and livestock breeding. There would be big benefits to designing and carrying out a process to develop and support a value proposition for future CGIAR breeding activities. More multi-disciplinary team approaches are needed to work on trait prioritization for CGIAR and partners, embedded within a systems approach. Participatory methods to characterize stakeholders’ needs and preferences are crucial to ensure that new varieties fulfil their expectations in highly dynamic market environments.
Publication - Pathways from research on improved staple crop germplasm to poverty reduction for smallholder farmers(Elsevier, 2019) Alwang, J.; Gotor, E.; Thiele, G.; Hareau, G.; Debello, M.J.; Chamberlin, J.Innovations to improve staple crop germplasm can reduce poverty and otherwise improve farmer livelihoods through complex and multiple pathways. This paper reviews the evidence for one prominent pathway—through increased incomes (in cash and kind) for poor farmers who adopt the technology. An important determinant of poverty reduction is the ability of poor producers to adopt productivity-enhancing varieties, and the paper analyzes recent household-level data from two African countries to examine if poor producers face unique barriers to adoption. A second determinant of poverty reduction is the area available to plant these varieties and whether the intensity of adoption is great enough to significantly reduce poverty. The paper uses a double-hurdle estimation framework to model the adoption/area planted joint decision for maize farmers in Ethiopia and sweet potato farmers in Uganda. The focus of the analysis is the effect of poverty-related variables on adoption/area planted decisions. Farmer wealth, landholding, education, location, and access to support and information services are included to understand how correlates of poverty affect adoption decisions. We find evidence that landholding size is an important barrier to poverty reduction; poor farmers are able to adopt improved varieties, but their intensity is constrained by land availability. In Uganda, farmers at the 95th percentile of adoption area received about $0.13 per person per day from the incremental yield, covering < 50% of the mean household poverty gap. This gain only comes under optimistic assumptions and most adopters do not have sufficient area for the direct income effect to be large. The evidence suggests that direct, short-term impacts of increased productivity to increased income may be limited in magnitude. Nonetheless, we recognize that other, less direct pathways may be important, particularly over longer times. Impacts through indirect pathways are, however, more difficult to measure. This has implications for the design of M&E and the crafting of appropriate targets for outcomes of research on staple crops which should focus perhaps on the other pathways where poverty reduction is more probable.
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