The Science of Agronomy to Scale

1. IS3.1 Soil fertility management and the African Green Revolution
The Science of Agronomy to Scale
Keith Shepherd

2. It’s all about reducing farmers’ decision risk
• Input use (e.g. improved seed, fertilizer) is a big risky
decision – high cost of being wrong, large uncertainty
in outcome
• Anything we can do to reduce this decision risk will
have high information value
• What is limiting? How much to put on?
Maize yield (t/ha)
Density
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3. What is the basis for reliable inference?
• What is the region of interest?
• What do we know about variation that matters?
• Are populations or sub-populations defined and
sampled in a statistically rigorous way?
• What is the basis for inferring results from one
location to another?
Shepherd et al. (2015). Land health surveillance and response: A framework
for evidence-informed land management. Agricultural Systems 132: 93–106
Convenience locations, few locations, don’t know
what they represent?

4. Soil test calibration limitations
• Soil tests require calibration using plant response
trials on each soil type - not yet done systematically in
Africa – wildly extrapolated
• Single nutrient tests (e.g. P) need adjustment for soil
type/properties (e.g. pH, organic matter, mineralogy)
• No validation of recommendations = no learning

5. Further limitations of current soil testing approaches
• Cost, speed
• Surveillance approach requires large sample
numbers – analytical costs prohibitive
• Reproducibility
• In Africa, labs face severe challenges
(electricity, grade chemicals, water quality,
gas quality, equipment servicing, etc)

6. 0
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ATVC
FELDA
WAGENINGEN
LAF
BELFAST
LAS
EDAFONEI
974BRET
WHAL
XGCALAFIGA
KSSL
HILL
CSS
NEMALAB
MARELl
ARCWSG
AECSAGRICS
AARDVARKAA
AGROLAB-SL
IGEOLUNAM
SPAL
SOILLAB
ERSAFVGSCA
IRRI
PASCAnalab
LABAMB
LSF
RHODE
AGROADGAZA
EALG
HCFR
LASPEE
WIKASO
KARI-NARL
BIOLAB
CDAgrogand
LABCAMPO
Minus major outlier
Soil test variation among labs
Wageningen Evaluating Programs for Analytical Laboratories (WEPAL)
Olsen P
test

7. Soil test recommendations ignore risk
•Performance of soil tests not
stated or validated
•Cannot trace data used to
produce recommendations
•No basis for learning &
improvement

8. “It is not possible to review the response curves from field trials (relative yields vs. soil-
P test) that form the basis of the fertilizer-P recommendation systems in each European
country or region”.
“Nor is it possible to identify the equations chosen for fitting the response curves”
“There are almost as many types of calculations as there are countries”
An overview of fertilizer-P recommendations in Europe: soil
testing, calibration and fertilizer recommendations
Jordan-Meille et al. Soil Use and Management, December 2012, 28, 419–435

9. E.g. Croplands
GeoSurvey: probability cropland presence
Define the region of interest
Africa Soil Information Service (AfSIS)
Ethiopia Soil Information System (EthiioSIS)
Ghana Soil Information Service (GhaSIS)
Nigeria Soil Information Service (NiSIS)
Tanzania Soil Information Service (TanSIS)

10. Use sampling frames – soil, crop
TanSIS
Sampling locations (clusters) (red) and alternative sampling designs (green and blue).
Valid inference

11. Soil-Plant Spectral Technology
Mid-infrared spectrometer
(MIR)
Handheld x-ray fluorescence analyser (pXRF)
•Soils properties
•Plant macro & micro nutrients
•Compost quality
•Fertilizer certification
Africa Soil
Information
Service (AfSIS)
•Digital mapping of soil properties
•Plant nutrition monitoring; large n trials
•Soil carbon inventory
•Agro-input and output quality screening
•Mining reclamation
pXRF allows rapid, low cost macro & micronutrient analysis

12. Spectral Shape Relates to
Basic Soil Properties:
• Mineral composition
• Iron oxides
• Organic matter
• Carbonates
• Soluble salts
• Particle size distribution
MIR spectral fingerprints

13. Foliar pXRF as diagnostic
One Acre Fund trials in Western Kenya: Low P, K, S, Cu, Zn
K P S Mg Ca Cu Zn Fe Mn

14. Digital mapping for spatial interpolation
Low cost soil
information
through digital
mapping

15. Hengl T, Leenaars JGB, Shepherd KD, Walsh MG, Heuvelink GBM, Mamo T, Tilahun H, Berkhout E,
Cooper M, Fegraus E, Wheeler I, Kwabena NA. 2017. Soil nutrient maps of Sub-Saharan Africa:
assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutrient Cycling in
Agroecosystems 109:77–102.
Digital soil mapping of soil nutrients

16. MIR soil spectral profiling
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pH
density
Cluste
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B
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20 40 60 80
Clay (%)
density
Cluster
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B
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CEC (ECD)
density
Cluster
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B
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K (mg/kg)
density
Clust
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B
Machakos County, Kenya (Technoserve Ltd)
• Response to applied nutrient
• Fertiliser recovery fraction

17. Application levels for spectral technology
• Digital mapping of soil constraints, crop nutritional deficiencies,
spectral soil types
• National scale
• Refinement at county / district level
• Local scale - UAV hyperspectral calibration / indices
• Cost effective soil-plant testing services for farmers
• National labs
• Rural soil-plant spectral testing labs – walk-in service to farmers
• Low cost sensors for community knowledge workers, private
enterprises

18. Spectral lab network & capacity development
Country Lab
Benin AfricaRice
Cameroon IITA; ICRAF
Cote D’Ivoire CNRA; ICRAF
Ethiopia ATA/NSTC (5); Mekelle Uni;
Ghana CSRIO-SRI
Kenya KARLO; One Acre Fund; CNLS, ICRAF
Madagascar Antananarivo Uni (collaborative).
Malawi CARS/ DARTS
Mali IER
Morocco Mohammed Vi Polytechnic /OCP (in progress)
Mozambique IAMM
Nigeria Obafemi Awolowo Un; IITA; IAR; FDMA&RD (2)
South Africa KwaZulu-Natal Dept A
Tanzania SARI; Min Ag (4); Sokoine Uni
Outside Africa Australia (CSIRO); China (YPC); India (CIMMYT; ISSS-ICAR);
Peru (IIAP); UK (Rothamsted)
Soil archiving system
Training courses; lab audits

19. Principles for taking agronomy to scale
•Define the decision dilemma
•Define the region of interest
•Sample it to provide a sound basis for inference
•Measure using rapid, low cost, reproducible methods
•Represent & communicate the uncertainty in results
•Validate recommendations using independent samples
•Maintain the link to the original data
•Focus further sampling to reduce uncertainty that matters