Infrared spectroscopy - bringing soil health information to smallholder farmers.

Infrared spectroscopy - bringing soil health information to smallholder farmers

Using only light to analyse soils

Scan a soil sample in 30 seconds Submit to online

spectral prediction app & get predicted soil properties

Spectral shape relates to basic soil properties

• Mineral composition• Iron oxides• Organic matter• Water (hydration,

hygroscopic, free)• Carbonates• Soluble salts• Particle size distribution

Functional properties

How does it work?

How do we use it?Mapping 3D soil properties for targeting soil fertility management strategies in Ethiopia

pH SOC

Africa Soil Information Service

www.africasoils.net

How do we use it?

Enabling cost-effective soil advisory services to farmers

Establish a rural soil lab for $50,000

•IAMM, Mozambique

•AfSIS, Sotuba, Mali

•AfSIS, Salien, Tanzania

•AfSIS, Chitedze, Malawi

•CNLS, Nairobi, Kenya

•CNRA, Abidjan, Cote D’Ivoire

•KARI, Nairobi, Kenya

•ICRAF, Yaounde, Cameroon

•Obafemi Awolowo University, Ibadan, Nigeria

•IAR, Zaria, Nigeria

•ATA, Addis Ababa, Ethiopia (6)

•IITA, Ibadan, Nigeria

•IITA, Yaounde, Cameroon

•IER, Arusha, Tanzania

•FMARD, Nigeria

•CNLS, Nairobi, Kenya

•BLGG, Kenya (mobile)

Who uses it?

Who else uses it?

Governments from Ghana, Nigeria and Tanzania signed up for national soil surveillance systems

Trained 717 (171 female) scientists/technicians in land/soil health surveillance field or laboratory techniques in past 12 months

2nd hands-on soil spectroscopy training course

Piloted farm soil monitoring in World Bank LSMS Ethiopia

IR analytical services to 18 CGIAR projects

Training 47 counties in Kenya with ChromAfrica

Piloting farm advisory service with One Acre Fund

Capacity building

ICRAF Soil-Plant Spectral Diagnostics Lab received 500 visitors per year for the past three years, over half of whom have received training

Conducted two hands-on soil spectroscopy training courses (each with 50 participants from 10 African countries)

In-country training in support of the spectral lab network

IR analytical services to 18 CGIAR projects

Helped private soil labs establish soil spectral analytical services: Soil Cares Initiative (mobile lab) and Crop Nutrition Services Ltd

Piloting farm advisory service with One Acre Fund to provide services to 25,000 farmers in eastern Africa

Training 47 counties in Kenya with ChromAfrica Ltd

Request from Karnataka State Government of India to help analyze 300,000 soil samples in 3 years to provide farm soil health cards

Partnerships

Improving measurements of agricultural productivity by combining household

level and soil fertility data

Coupling farm soil health measurement with household panel surveys

Using Central Statistics Agency sampling frame

Oromiya region of Ethiopia

Trained and supervised

19 enumerators and 2 supervisors in soil sample collection

4 lab technicians in soil sample processing (from Forest Research Centre, Ambo University, Hawasa Research Centre and Yabello Research Centre)

3 staff from the LSMS project and CSA attended 3-day soil infrared spectroscopy training course at ICRAF’s Soil-Plant Spectral Diagnostics Laboratory

Field guide for soil sampling

Composite soil sampling and coning

Soil sampling is tied to crop cut areas, but whole fields are also sampled

Next steps

Soil fertility status reports for Woredas Poverty – soil fertility relationships Extend pilot to another country Recommendations on standardizing the

approach within LSMS

Assessment of hydrological, financial and social risk around the supply of groundwater to

Wajir town

• ICRAF: Jan de Leeuw, Eike Luedeling, Todd Rosenstock, Keith Shepherd• Acacia Water, the Netherlands (hydrological risk assessment)• CETRAD Nanyuki, Kenya and University College London, U.K. (Social

risk assessment)

The decision problem

Research for impact

Most science never supports any decisions, even though decision-

makers are hungry for information.

http://www.mynamesnotmommy.com

Most research does not answer questions that are critical for decisions, or it is not readily available when it is needed.

Tailor research specifically to address particular decisions

Decision making under uncertainty

Identify all uncertainties in the decision of interest

Make probabilistic projections of likely decision outcomes

http://www.relationshipeconomics.net

Engage directly with decision makers

This is the core of WLE’s decision analysis procedures

Identify uncertain variables with high ‘information values’ (these

are priorities for measurements)

Why a quantitative model?

Popular approaches to assessing a large list and use of soft “scoring methods” that require a subject matter expert to pick a value on some scale for each of several factors. These usually introduce errors.

Two common and significant sources of error in expert forecasting and evaluation tasks: • overconfidence (where experts are right far less

often than their perceived confidence would indicate) and

• Inconsistency

WLE’s decision analysis approach

Optimize the decisionUse preferences of decision makers to determine best

decision

Ye sIs there significant value

to more information? Yes

No

Compute the value of additional informationDetermine where and how much measurement effort is

needed

Model the current state of uncertainty of all relevant factors Initially use calibrated estimates and then actual

measurements

Define the decision(s) – Identify relevant variablesSet up the ‘Business Case’ for the decision.

Measure where the information value is

high – Reduce uncertainty using proven empirical

methods

Outputs

Replenishment

Irrigation growth

Initial irrigated area

Water use per hectare

Aquifer size

Natural water use

Importance threshold

Identification of high-value variables

Probabilistic impact projections

Aquifer size after 70 years of abstraction (% of original)

Advantages and applications

Inclusion of uncertain variables allows truly holistic impact assessments

High information value variables are almost always not those typically measured

Probabilistic impact projections can often provide sufficient certainty about decision outcomes

Possible applications include:• Quantitative and probabilistic impact pathways• Ex-ante impact projections• Counterfactuals for impact assessments• Definition of priority variables for impact monitoring

What did we do?

What did we find

Value of information analysis

What did we achieve?