Presentation delivered by Dr. Bruno Gerard (Global Conservation Agriculture Program, CIMMYT) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico. http://www.borlaug100.org

1. Precision Agriculture for smallholder farmers:
Are we dreaming?
Bruno Gerard and Francelino Rodrigues,
International Maize and Wheat Improvement Center
Kite aerial photography of Bagoua village, Niger, B. Gerard 1999

2. Kite aerial photography of Bagoua village, Niger, B. Gerard 1999

3. A system thinker and actor!
“The greatest thing he [Norman Borlaug] did for
the field of agronomy was to begin to show people
that they had to think about multiple parts of the
system…
… If you think about what he did in the Green
Revolution, it wasn’t about genetics, and it wasn’t
about fertility, and it wasn’t about water. It was
about all of those different things together.”
Jerry Hatfield, lab director at the USDA-ARS in CSA March 2014 issue
https://www.crops.org/publications/csa/tocs/59/3

4. Projected
demand by
2050 (FAO)
Linear
extrapolations
of current
trends
Potential effect
of climate-
change-induced
heat stress on
today’s cultivars
(intermediate
CO2 emission
scenario)

5. Sustainable Intensification
More than just sustaining yield increases,
it is about economics and profitability,
social equity and environmental
friendliness
Dealing with complex and heterogeneous
systems

6. Source: Herrero et al. 2010

7. Technology
generation
Community to
landscape system
HH farming systemField Institutions &
Markets
Process
research
Enabling &
analysis tools
Output target
- Water
‘Last mile providers’
Innovation systemsParticipatory co-innovation & learning
- System interactions:
- Livestock, cash
crops; trees- Weeds
- Pests & diseases
- Soil health
- Nutrients
HH typologies
(livelihood &
biophysical)
Trade-off analysis Bio-economic
models
Geospatial (domains,
impact)
- Knowledge products
- Identify inefficiencies
(markets, providers)
Outcome Increased productivity &
stability of farming systems
Increased income of
smallholder farmers
Scale
- Tillage
- Rotation
- Intercropping
- Systems for the future
Increased yield of
maize/wheat for
smallholder farmers
- System impacts
on NRM &
ecosystem services
- Mechanisation
Business models
- Communication
products
Sustainable Intensification Framework
Courtesy: Peter Craufurd

8. “Sustainable Intensification” – producing more outputs with more efficient use
of all inputs on a durable basis, while reducing environmental damage and
building resilience, natural capital and the flow of environmental services –
High
PRODUCTIVITY
Low
Objective
Time
STABILITY
Low
High
Time
Objective
Critical Variable
RELIABILITY
High
Low
Objective
Time
ADAPTABILITY
High
Low
Objective
Time
Critical Variable
RESILIENCE
High
Objective
Time
Low
Critical Variable
EFFICIENCY
Courtesy: S. Lopez-Ridaura

9. Indicators must be integrated by multi-criteria methods for an overall
evaluation of the main advantages and disadvantages of different
solutions or scenarios (synergies and trade-offs)
INTEGRATION OF INDICATORS
Traditional System
Conventional system
Optimal
0.0
0.5
1.0
B/C ratio
Food self
sufficiency
Erosion
Soil Organic Matter
Forage self
sufficiency
Yield variability
with rainfall
Vulnerability to changes
in inputs and output
prices
Diversity of
agricultural
products
Independence to
external inputs
Independence
to hired labor
Gross Margin
Source: Lopez-Ridaura

10. Small farm
0
50
100
Gross Margin
Return to labor
Benefit/Cost
Soil Carbon
Balance
Soil Nitrogen
Balance
Soil losses
Gross margin variation with rainfall
Gross Margin
reduction in dry years
Gross Margin variation with prices of
outputs
Gross margin reduction with low
output prices
Monetary Costs
Dependence to
external inputs
0
50
100
Gross Margin
Return to labor
Benefit/Cost
Soil Carbon
Balance
Soil Nitrogen
Balance
Gross Margin variation with prices of
outputs
Gross margin reduction with low
output prices
Monetary Costs
Dependence
to external inputs
Soil losses
Gross margin variation with rainfall
Gross Margin
reduction in dry years
Large farm
Multi-criteria Farming systems analysis/ Recommendation domains
Surveys (resource endowment, crops/animals, management, ….x…)
Interviews (farm management, resource allocation, strategies)
Modeling (MCDM, farm flows, optimization)
FARMING SYSTEMS
Courtesy: S. Lopez-Ridaura

11. MKT CSH
CNS
HOME
LVSTK
OE
WOOD
MKT
CNS
HOME
LVSTK
WOOD
FOOD
OFF-FARM
CSH
MKT
CSH
CNS
HOME
LVSTK
WOOD
FOOD
MKT
CNS
HOME
LVSTK
WOOD
FOOD
OFF-FARM
MKT
CNS
HOM
E
WOOD
FOOD
OFF-FARM
CSH
Type 1
Type 5
Type 4
Type 3
Type 2
Cash
Labour
Nutrients
Resource
allocation
strategies
Tittonell (2003)
Farming Systems Typologies (Structural-functional)
FARMING SYSTEMS

13. Mueller et al., Nature 2012

14. Year
1950 1960 1970 1980 1990 2000 2010 2020
Nitrogenefficiencyincerealproduction
(megatonnescerealgrain/megatonnsfertilizerapplied)
20
30
40
50
60
70
80
Trends in N-fertilization efficiency in cereal production
(annual global cereal production divided by annual global application of N-fertilizer) (Source: FAO 2012)
Global food production has tripled during this period, but N-fertilizer
applications have increased 10-fold (Tilman et al., 2001)
Nitrogen application has
reached a point of
diminishing returns – i.e.
we are applying more and
more nitrogen to get
similar yields and this may
continue in future
Courtesy: GV Subbaro, JIRCAS

15. Our Precision Agriculture Principles
• 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?

16. 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.

17. 72.1 70
30
70.7
82.1
99
60
80
56.4
84.3
52.9
92.1
60 60
54.3
Cell phone
Data Source: CCAFS Surveys 2012

18. 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

19. Connections of remote sensing products with (decision) support
tools for farmers
Field data base
Recommendations
Crop Mgr (IRRI/CIMMYT)
Micro Credit
Field boundaries
Farmer information
Crop management data
Crop Insurance
Irrigation scheduling
Recommendation
domains
&
Diagnostics for
technology targeting
Ground Cover
Surface Soil Moisture
Chlorophyll
Key crop phenology
Crop & fallow land
Attainable Yield
Actual Yield
Yield gap
Damage maps
Surface water / flood
Remote Sensing
Digital elevation model
Climate
and weather
Data

20. Fertility management practices
• ‘Blanket’ recommendations for large areas
• Based on old data
• Developed on experiment stations, not farmers fields
Recommendations that do not
match local conditions cost
farmers yield and profits –
especially where fertilizers
are $$

21. Embracing the promise of ICTs with accessible tools for
site-specific nutrient management
for rice, maize, and wheat in S. Asia
Courtesy of Roland Buresh, IRRI
2. Compute field-
specific guideline
Model hosted
on the cloud
1. Acquire field-specific
information from farmers
Web Smartphone
3. Provide customized
field-specific guidelines
in local language Multi-format
output
The architecture is in place

22. MOBILE
PHONE
ACTUAL N&P
APPLICATION
YIELD
ESTIMATES
DECISION
SUPPORT
REMOTE
SENSING
1
2
3
4
5

23. Precision nutrient management: Farmers
Accessible Options
• Decision Support Tools
(Nutrient Expert for
wheat) for SSNM+
• Handheld sensors
• Band placement

25. Severe events (drought(s)) at different phenological stages of crop growth
Extreme heat stress (wheat) -spikelet sterility and limited grain filling.
CROPPING SYSTEMS
Malik and M.L. Jat, et al

26. The combination and sequencing of crops with different
management practices and under different environmental
conditions
Interaction occurring in crop rotations, intercropping, green
manures and cover crops and their effect on the long term
performance of the cropping systems
CROPPING SYSTEMS
Krupnik et.al CIMMYT-GCAP

27. Amazing technological breakthrough
More for less: better, easier, faster and cheaper
Gerard et al. , Soil Sci. Plant Nutr.1997
CIMMYT 2013
Photo: J. Cairns

28. False color image of CIMMYT station at Obregon, Mexico acquired from
multispectral camera at 1 m resolution on Feb. 15, 2013.
Collaborative research with QuantaLab, Cordoba/Spain

29. Thermal image of CIMMYT station at Obregon, Mexico acquired from the thermal camera at 2 m
resolution on Feb. 14, 2013. Well-watered (cooler) plots are shown in blue, while water-stressed
(warmer) plots are shown in green and red
Collaborative research with QuantaLab, Cordoba/Spain

30. Farm level benefits in
RWCS of IGP
• ~7 % gain in crop
productivity
• ~20 % (18 ha-cm yr-1)
saving in irrigation water,
• US$ 113 to 175 ha-1 higher
system profitability
• 10-13 % higher agronomic
efficiency of nitrogen
Laser land leveling is a precursor technology to CA
A success story in India
Source: Jat et al, 2005, 2006, 2009a,b,2011
Current # 25000

33. Mapping soil variability (EM38)

35. Priorities
• Recommendation domains for intensification at
different granularities (regional, national,
landscape, farm, field)
• 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

36. Priorities (cont.)
• 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

37. www.maize.org