Precision agriculture in maize-based cropping systems. Bruno Gerard

1. Precision agriculture in maize-based cropping systems
Bruno GERARD @ CIMMYT
13th Asian Maize Conference and Expert Consultation on
“Maize for Food, Feed, Nutrition and Environmental Security
Ludhiana, 8th October 2018

2. Precision agriculture
An approach to farm management that uses information
technology to ensure that the crops and soil receive
exactly what they need for optimum health and productivity

3. Why Precision agriculture?
Contribute to better use of resources (spacewise and
timewise)
Nutrients, water, pesticides
-> Environmental benefits
-> Profitability
Algae development in
the Sea of Cortes due to
ag. nitrate pollutionShammi Mehra/Agence France-Presse — Getty Images
Farmers’ Unchecked Crop Burning Fuels India’s Air Pollution

4. New technologies should also serve smallholder
farmers in wheat and maize based systems
i.e. penetration of cell phones in countries where we work is >80%
‘From the description of site-specific activities it is obvious that
although PA, 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.

5. 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

6. Major technical innovations contributing to
the development of precision agriculture
1. Global Positioning System (GPS)
Constellation of 24 satellites

7. Major technical innovations contributing to
the development of precision agriculture
2. Earth observation platforms (satellites)
Karnal, India Zimbabwe Henan, China

8. Cloud-free Reflectance Composite:
merging image time series to build a suitable synthetic product
Product rationale
The Cloud-free Reflectance Composite product provides a cloud-free temporal
synthesis of surface reflectance values in the 10 Sentinel-2 bands designed for land
observation. It is deliveredwith several masks that will help appraisingits quality.
The compositing period can be selected between 30 and 50 days for a given site. This
provides flexibility with regard to local cloud coverage conditions and enables the
production of a high quality information.
The product is delivered on a monthly basis to ensure a proper monitoring of the observed
areas and includes relevant additional informationto support future use and interpretation.
Product specifications
! " #
$
Images from the SPOT 5 Take 5 time series over Ukraine (top)
and resulting Cloud-free Reflectance Composite obtained using 1 month of data (bottom)
10/04/2015 10/05/2015 20/05/2015
01/05/2015
Sentinel 2 A&B
• 5 day revisit 10 m
resolution
Products for:
• Technology targeting
• Crop management advice
• Monitoring and Evaluation
• Change detection

9. Major technical innovations contributing to
the development of precision agriculture
3. Unmanned Aerial Vehicles
(UAV)/Drones
eBee UAV at take-off
Sequoia sensor for eBee

10. Details of a mosaic obtained with eBee and Sequoia Sensor
Oaxaca, Mexico

11. Different resolutions!
eBee
Sentinel 2

12. Airborne Thermal IR Mosaic
Obregon/Mexico
Thermal IR to measure canopy temperature

13. Drone Photogrammetric applications

14. Major technical innovations contributing to
the development of precision agriculture
4. Proximal sensors
Mapping soil variability (EM38) – Salinity, water content

15. Yield monitoring

16. Major technical innovations contributing to
the development of precision agriculture
5. Information and communication technologies (ICT)
Plantix application by PEAT
https://plantix.net/en

17. model
Maize phenology dev.
= Genotype x Temp.f
GPS-location
variety + timing
advice
farmer preferences
‣ planting date + variety
‣ planting+harvesting date
‣ harvesting date + variety
Maize Variety Selector (MVS)
TAMASA Project

18. Ground Cover (%) from remote and proximal
sensing
Daily Weather
• Tmax
• Tmin
• Solar radiation
• Precipitation
Forecasted
irrigation need on a
weekly basis
(yes/no) sent by SMS
Water table depth
Generic soil water
balance model
Program for Advanced Numerical Irrigation (PANI)
Smartphone app for irrigation scheduling developed in
Bangladesh & Mexico1)
Irrigation
recommended
in next 7 days
1) Also works anywhere in between those 2 countries

19. Few additional examples of technical
innovations harnessed to improve
resources use efficiency in maize and
wheat based systems
R4D challenges <-> available and affordable technologies

20. 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 sequencing/optimization
in Bangladesh
Krupnik et.al

21. System Optimization: Planting Dates and Wheat
Productivity Across Indo Gangetic Plains (South Asia)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5Nov-10 Nov 30-Nov 10-Dec 20-Dec
Grainyield(t/ha)
TGP
MGP-EUP
MGP-Bihar
LGP
UGP-UP
Source: Synthesized by ML Jat from CSISA reports
(2010-12) & RK Malik, EUP CSISA reports (2011)
Polynomial relationship between sowing
time and GY of ZT wheat in EGP (n = 704)
Average date of Rice Transplanting in EUPH
0 10 20 30 40 50 60
Upto July 10
11-20 July
After July 20
Farmers(%)

22. A much needed blue revolution
Zero till planting of wheat after rice in
a sub-surface drip plot (BISA station Ludhiana)

23. Effect of residue mulch, and drip spacing and flood irrigation system on irrigation water
productivity (WPi,) during two rice and wheat growing seasons (BISA station, Ludhiana)
(Jat et al. submitted)
Zero Till DSR +
flood irrigation
WheatRice
Zero Till Wheat + flood
irrigation
conventional puddled
transplanted rice +
flood irrigation
conventional till wheat
+ flood irrigation
Zero Till DSR + drip irrigation
(T1 surface drip – T2-T6
subsurface drip with various
depth/spacing
Zero Till wheat + drip
irrigation (T1 surface drip –
T2-T6 subsurface drip with
various depth/spacing

24. Integrating precision water and nitrogen management with CA in maize-wheat system can
be game changer to diversify resource intensive rice-wheat system
Key messages :
• Increase in system productivity by > 1.5 t/ha/year
• MW system Water Use Efficiency increased from 2.11 in
conventional to 5.22 kg/m3 in CA+SSD (RW WUE
averaged around 0.7 kg/m3)
• Agronomic efficiency of N in MW system increased
significantly from 27% in conv to 33% in CA+SSD
• Lower environmental footprints of tillage, water, nitrogen
• Complementarity of CA and SSD can support convergence
of investment schemes of the Govt and make efficient use
of resources
0
10
20
CA+SSD Conv
8.78 7.58
6.3 5.68
Grain yield (t/ha)- 03 year
average
Maize Wheat
0
200
400
600
800
CA+SSD Conv
111
252
178
374
Irrigation water use (mm)-
03 year av
Maize Wheat
ML Jat, HS Sidhu and team (CIMMYT-BISA Ludhiana)

25. 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
Scaled to millions of ha in the IGP

26. Source: Heffer, P. (2013). IFA: Paris
Nutrient Use Efficiency

27. 120,000 USD
30,000 USD
120,000 USD 30,000 USD 5,000 USD 500 USD
Drone- UAV
Manned Airplane
GreenSat (Spot and Sentinel-2 satellites)
Sensor technology for nutrient
management at different costs and scales

28. Wheat and Maize Nutrient Expert: Precision Nutrient Prescriptions in
South Asia (ICAR, IPNI, CIMMYT)
20th June 2013

29. Precision nutrient management
Examples of technologies for precision N
management:
• Decision support software: e.g.
Nutrient Expert® (NE) decision
support system developed by IPNI
and CIMMYT. Provides field- as well as
farm-specific precision fertilizer
recommendations for the
smallholders.
• Optical sensors: e.g. Greenseeker –
has allowed us to reduced N use,
while maintaining the same yield in
wheat production systems in Mexico
by 60 kgN/ha.

30. GreenSat: N-rate calculation for spring wheat using NDVI from SPOT 6/7 satellite
N-rich strips

31. Decisions across scales; fertilizer
National to
sub-national
Farm to field
Agro-
ecosystem &
landscape
Fertilizer
formulation
Marketing (domains,
demand,
infrastructure)
AE & crop system-
specific
recommendations
Field-specific
recommendations
Soil properties database
ROI, population,
infrastructure, market
intelligence, benefits
Climate, slope, macro-
soil constraints (e.g.
acidity), crop
requirements
Management, soil type &
fertility, weather

32. Four building blocks for
smallholder precision agriculture
1) Remote sensing and other monitoring tools (weather, soil
monitoring ) -> diagnosis, spatial and temporal dimensions
2) Nutrient, water and disease management, crop modelling -> how
you turn diagnosis into recommendations
3) Information and Communication Technologies -> how you get
diagnosis from and provide recommendations to farmers (path
for crowdsourcing)
4) Mechanization solution -> how you apply rec. in the field
Articulation of those blocks are system specific and needs dvpt of
specific business models

33. Conclusions
– 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)
– Disease monitoring!
– Provides great research tools for better characterization and
understanding of spatial and temporal processes
– Agronomy and agricultural engineering curriculum should
have a stronger Precision Agriculture component

34. Thank you!
2
Contributors (in no particular order):
- Numerous scientists from national programs,
advanced research institutions, farmers, service
providers…
- CIMMYT/BISA colleagues: Andy McDonald,
ML Jat, HS Sidhu, Jat Kumar Tek Sapkota,
Mahesh Gathala, Tim Krupnik, TP Tiwari, Urs
Schulthess, Jack McHugh, Carlo Montes, Scott
Justice, David Guerena, Francelino Rodrigues,
Bram Govaerts, Ivan Ortiz-Monasterio, Lorena
Gonzalez, Peter Craufurd, Mainassara Zaman
Allah, Jordan Chamberlin,…
- Financial partners: USAID, BMGF, ACIAR, MAIZE
CRP donors