Person: Wijk, M. van
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Wijk
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M. van
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Wijk, M. van
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0000-0003-0728-8839 9 results
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- Food availability and livelihood strategies among rural households across Uganda(Springer Netherlands, 2017) Wichern, J.; Wijk, M. van; Descheemaeker, K.; Frelat, R.; Van Asten, P.; Giller, K.E.
Publication - Small farms and development in sub-Saharan Africa: farming for food, for income or for lack of better options?(Springer, 2021) Giller, K.E.; Delaune, T.; Silva, J.V.; Wijk, M. van; Hammond, J.; Descheemaeker, K.; Van de Ven, G.; Schut, A.G.T.; Taulya, G.; Chikowo, R.; Andersson, J.A.
Publication - Drivers of household food availability in sub-Saharan Africa based on big data from small farms(National Academy of Sciences, 2016) Frelat, R.; Lopez-Ridaura, S.; Giller, K.E.; Herrero, M.; Douxchamps, S.; Andersson Djurfeldt, A.; Erenstein, O.; Henderson, B.; Kassie, M.; Paul, B.K.; Rigolot, C.; Ritzema, R.S.; Rodriguez, D.; Van Asten, P.; Wijk, M. van
Publication - Food access deficiencies in sub-Saharan Africa: prevalence and implications for agricultural interventions(Frontiers, 2019) Fraval, S.; Hammond, J.; Bogard, J.; Ng’endo, M.; Etten, J. van; Herrero, M.; Oosting, S.J.; Boer, I.J.M. de; Lannerstad, M.; Teufel, N.; Lamanna, C.; Rosenstock, T.; Pagella, T.; Vanlauwe, B.; Dontsop, P.; Baines, D.; Carpena, P.; Njingulula, P.; Okafor, C.; Wichern, J.; Ayantunde, A.; Bosire, C.; Chesterman, S.; Kihoro, E.; Rao, E.J.O.; Skirrow, T.; Steinke, J.; Stirling, C.; Yameogo, V.; Wijk, M. van
Publication - Non-linear interactions driving food security of smallholder farm households in the western highlands of Guatemala(Frontiers, 2020) Barba-Escoto, L.; Wijk, M. van; Lopez-Ridaura, S.
Publication - The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries(Nature Research, 2020) Wijk, M. van; Hammond, J.; Gorman, L.; Adams, S.; Ayantunde, A.; Baines, D.; Bolliger, A.; Bosire, C.; Carpena, P.; Chesterman, S.; Chinyophiro, A.; Daudi, H.; Dontsop, P.; Douxchamps, S.; Emera, W.D.; Fraval, S.; Fonte, S.; Hok, L.; Kiara, H.; Kihoro, E.; Korir, L.; Lamanna, C.; Chau Thi Minh Long; Manyawu, G.; Mehrabi, Z.; Mengistu, D.K.; Mercado, L.; Meza, K.; Mora, V.; Mutemi, J.; Ng’endo, M.; Njingulula, P.; Okafor, C.; Pagella, T.; Phengsavanh, P.; Rao, J.; Ritzema, R.S.; Rosenstock, T.; Skirrow, T.; Steinke, J.; Stirling, C.; Suchini, J.G.; Teufel, N.; Thorne, P.; Vanek, S.; Etten, J. van; Vanlauwe, B.; Wichern, J.; Yameogo, V.
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 - Food security and agriculture in the Western Highlands of Guatemala(Springer, 2019) Lopez-Ridaura, S.; Barba-Escoto, L.; Hellin, J.; Gerard, B.; Wijk, M. vanFood security is a major challenge in Guatemala, one of the poorest countries in the world. Food insecurity is concentrated in the Western Highlands of Guatemala (WHG) where indigenous communities have been the main victims of social, political and economic marginalization. In this study we characterize the diversity of farming households in the WHG, identify the main sources of food for different types of farm households and assess their food security status through a simple, yet robust, potential food availability indicator. Based on a large and rich dataset of nearly 5000 farm households, our results show the diversity of farming systems in the region, dominated by maize and coffee production, as well as the large differences in their potential food availability. In our model, 52% of farm households in the WHG did not have the means to attain sufficient energy from their agricultural activities. In general, diversified maize-based, coffee-based and specialized coffee farm households had larger proportions of potentially food secure households with 60%, 83% and 74% food secure households, respectively. This contrasted with farm households specialized in maize production and resource-constrained households where there were a greater proportion of households were food insecure. The analytical framework presented here, combining a typology of farm households and their livelihoods with the analysis of their food security status, provides a useful approach for better targeting development interventions towards combating hunger, poverty and malnutrition.
Publication - Climate smart agriculture, farm household typologies and food security An ex-ante assessment from Eastern India(Elsevier, 2018) Lopez-Ridaura, S.; Frelat, R.; Wijk, M. van; Valbuena, D.; Krupnik, T.J.; Jat, M.L.One of the great challenges in agricultural development and sustainable intensification is the assurance of social equity in food security oriented interventions. Development practitioners, researchers, and policy makers alike could benefit from prior insight into what interventions or environmental shocks might differentially affect farmers' food security status, in order to move towards more informed and equitable development. We examined the food security status and livelihood activities of 269 smallholder farm households (HHs) in Bihar, India. Proceeding with a four-step analysis, we first applied a multivariate statistical methodology to differentiate five primary farming system types. We next applied an indicator of food security in the form of HH potential food availability (PFA), and examined the contribution of crop, livestock, and on- and off-farm income generation to PFA within each farm HH type. Lastly, we applied scenario analysis to examine the potential impact of the adoption of ‘climate smart’ agricultural (CSA) practices in the form of conservation agriculture (CA) and improved livestock husbandry, and environmental shocks on HH PFA. Our results indicate that compared to livestock interventions, CA may hold considerable potential to boost HH PFA, though primarily for wealthier and medium-scale cereal farmers. These farm HH types were however considerably more vulnerable to food insecurity risks resulting from simulated drought, while part-time farmers and resource-poor agricultural laborers generating income from off-farm pursuits were comparatively less vulnerable, due in part to their more diversified income sources and potential to migrate in search of work. Our results underscore the importance of prior planning for development initiatives aimed at increasing smallholder food security while maintaining social equity, while providing a robust methodology to vet the implications of agricultural interventions on an ex ante basis.
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