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Azzarri, C.

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Azzarri
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Azzarri, C.
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    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
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    From plot to scale: ex-ante assessment of conservation agriculture in Zambia
    (Elsevier, 2019) Komarek, A.; Hoyoung Kwon; Haile, B.; Thierfelder, C.; Mutenje, M.; Azzarri, C.
    This study combined bottom-up and top-down approaches to assess the ex-ante effects of conservation agriculture (CA)-based systems in Zambia considering both biophysical and economic factors and prevailing farm systems characteristics. For continuous maize cropping we compared a CA-based system of no-tillage with crop residue retention to a control system of conventional tillage with crop residue removal. First, we simulated yield effects that were calibrated and evaluated against multiple datasets, including on-farm agronomic trials from two seasons and six sites. Next, we extrapolated our simulations to all maize-growing areas in Zambia using gridded climate and soil datasets. Then simulated yields (in kg ha−1) were combined with economic data from a nationally-representative household survey to construct economic indicators including benefit-cost ratios (based on gross benefits and variable costs both in $ ha−1) that captured the implicit value of crop residues and labor demands. The field scale (per ha) indicators were scaled out using harvested areas as an expansion factor. All indicators were calculated over 3-, 10-, and 20-year simulation periods using an interpolated sequence of historical climate data. Finally, we conducted a spatial farm typology analysis to help understand the spatial variation in our field-scale indicators and provide insights into trade-offs and the suitability of CA-based systems for farmers. Average changes in yield from using CA-based systems (compared with the control) at the district scale ranged from −37% to 70% (average 33%), with a similar range of changes in benefit-cost ratios once economic factors were included, in addition to intra-district yield variability. Combining the changes in benefit-cost ratios with maize harvested area resulted in an average annual change in district-scale net benefit ranging from US $ − 3.9 to US $9.9 million (with an average of US $1.1 million). The heterogeneity in biophysical and economic factors gave a ranking of provinces different according to biophysical or economic indicators, reinforcing the importance of coupling biophysical and economic approaches. The spatial farm typology analysis highlighted the specific contexts of farmers relevant to the suitability of CA, such as their mineral fertilizer applications rates, ownership of livestock, and prevailing soil texture and rainfall.
    Publication