precision agriculture for smallholder farmers: are we dreaming?
DESCRIPTION
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.orgTRANSCRIPT
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
Kite aerial photography of Bagoua village, Niger, B. Gerard 1999
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
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)
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
Source: Herrero et al. 2010
Technology generation
Community to landscape system
HH farming system Field Institutions & Markets
Process research
Enabling & analysis tools
Output target
- Water
‘Last mile providers’
Innovation systems Participatory 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
“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
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
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
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
Mueller et al., Nature 2012
Year
1950 1960 1970 1980 1990 2000 2010 2020
Nit
rogen
eff
icie
ncy
in c
erea
l pro
duct
ion
(meg
a to
nnes
cer
eal
gra
in/m
egat
onns
fert
iliz
er a
ppli
ed)
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
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?
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.
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
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
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
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 $$
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
MOBILE PHONE
ACTUAL N&P APPLICATION
YIELD ESTIMATES
DECISION SUPPORT
REMOTE SENSING
1
2
3
4
5
Precision nutrient management: Farmers Accessible Options
• Decision Support Tools (Nutrient Expert for wheat) for SSNM+
• Handheld sensors • Band placement
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
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
Amazing technological breakthrough More for less: better, easier, faster and cheaper
Gerard et al. , Soil Sci. Plant Nutr.1997
CIMMYT 2013
Photo: J. Cairns
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
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
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
Mapping soil variability (EM38)
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
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
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