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

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. 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 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 decision Use preferences of decision makers to determine best decision Ye s Is there significant value to more information? Yes No Compute the value of additional information Determine 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 variables Set 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?