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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