Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Precision Agriculture for smallholder farmers: An option?
1. Precision Agriculture for smallholder farmers:
An option?
Bruno Gerard, J. Hellin, B. Govaerts, A. McDonald, T. Krupnik
Mexico City – 14 December 2013
Kite aerial photography of Bagoua village, Niger, B. Gerard 1999
2. Climat e
change
Wat er, nut rient &
energy scarcit y
World-wide average yield
(t ons ha-1 )
Diseases
Agronomy Breeding
Year
Projected
demand by
2050 (FAO)
Linear
extrapolations
of current
trends
Potential effect
of climatechange-induced
heat stress on
today’s cultivars
(intermediate
CO2 emission
scenario)
6. Principle
• Precision agriculture for smallholder farmers
should be seen at multiple scales:
– Not only dealing with within field spatial
variability but also intra-farm (and inter-farm)
resource allocation
– Precision Agriculture -> more precise agriculture
(spatial and temporal dimension)
– Where, when, what, how?
7. Why should new technologies not benefit
smallholders farmers of the world?
Penetration of cell phones in countries where we
work is high
‘From the description of site-specific activities it is obvious that
although precision agriculture, as seen in Europe and North
America, is largely irrelevant in developing countries. The need for
spatial information is actually greater, principally because of
stronger imperative for change and lack of conventional support’
Cook et al., 2003.
10. Four building blocks of precision
agriculture for smallholder farmers
-
Remote sensing and other monitoring tools
(weather, soil monitoring ) -> diagnosis, spatial and
temporal dimensions
- Nutrient, water and disease management, crop
modelling -> how you turn diagnosis into
recommendations
- Information and Communication Technologies -> how
you get diagnosis from and provide recommendations
to farmers (path for crowdsourcing)
- Mechanization -> how you apply rec. in the field
Articulation of those blocks are system specific and needs
dvpt of specific business models
11.
12. Priorities
• Recommendation domains for intensification at different granularities
(regional, national, landscape, farm)
• Yield gap and risk assessment (link with crop insurance, credit)
• Ex-ante assessment of information needs at extension and farmer levels
• Improved management practices (water, nutrients, tillage, timing) and
prototype site specific recommendations through ICT models
• Upscaling/downscaling:
On-farm trials - Proxi-sensors – UAV/airborne – spaceborne
• Data articulation/fusion/assimilation
– Vegetation, soil, climate/weather, socio-economic, markets
• Cross-regional learning!
• Additional partnership with ARIs
• Public-private partnership (i.e BASF, Syngenta, crop ins., RS)
• Capacity building of NARS and extension services