Hands-on Soil Infrared Spectroscopy Training Course Getting the best out of light 11 – 14 November 2013 Applications of soil spectroscopy on Land Health Surveillance Ermias Betemariam

Context 1  There is a lack of coherent and rigorous sampling and assessment frameworks that enable comparison of data (i.e. meta-studies) across a wide range of environmental conditions and scales  Soil monitoring is expensive to maintain  Soil degradation and loss is a challenge  High spatial variability in soil properties- large data sets reduce uncertainty Context High spatial variability of SOC can rise sevenfold when scaling up from point sample to landscape scales, resulting in high uncertainties in calculations of SOC stocks. This hinders the ability to accurately measure changes in stocks at scales relevant to emissions trading schemes (Hobley and Willgoose, 2010) Soil spectroscopy key for Land Health Surveillance

Context (2) 2 Soil comes to the global agenda: – Sustainable intensification took soil as a x-cutting – Global Environmental Benefits - land degradation and soils are among the priority global benefits (GEF/UNCCD) SOC as useful indicator of soil health Importance of soil carbon in global carbon cycle and climate mitigation Increasing demand for soil data at fine spatial resolution – carbon trading purposes requires high levels of measurement precision

Land Health (SD4) Land Health - the capacity of land to sustain delivery of essential ecosystem services 3 Land health surveillance aims to provide statistically valid estimates of land health problems, quantify key risk factors associated with land degradation, and target cost-effective interventions to reduce or reverse these risks.

Land Health Projects 4 No.Name of project 1 Africa Soil Information Service (AfSIS)/Africa Soils (SSA) 2 Strengthening capacity for diagnosis and management of soil micronutrient deficiencies (SSA) 3 Soil monitoring protocol for the World Bank Living Standards Measurement Study (Ethiopia &..) 4 Carbon sequestration options in pastoral & agro-pastoral systems in Africa (Burkina Faso & Ethiopia) 5 Land health surveillance for high value biocarbon development (Kenya, Burkina Faso & Sierra Leone) 6 Land health surveillance system for smallholder cocoa in Ivory Coast 7 Trees for food security in Eastern Africa (Rwanda, Ethiopia, Burundi & Uganda) 8 Land health surveillance for mitigation of climate change in agriculture (Kenya & Tanzania) 9 Land health surveillance system in support of Malawi food security project (Malawi) 10 Land health surveillance system for targeting agroforestry based interventions for sustainable land productivity in the western highlands of Cameroon 11 A Protocol for Measurement and Monitoring Soil Carbon Stocks in Agricultural Landscapes Land Health Projects

Land Health out-scaling projects Tibetan Plateau/ Mekong Cocoa - CDI Parklands Malawi National surveillance systems Regional Information Systems Project baselines Rangelands E/W Africa SLM Cameroon MICCA E. Africa Global-Continental Monitoring Systems Evergreen Ag / Horn of Africa CRP5 pan-tropical basins AfSIS EthioSIS- Ethiopia 5

AfSIS ✓ 60 primary sentinel sites ➡ 9,600 sampling plots ➡ 19,200 “standard” soil samples ➡ ~ 38,000 soil spectra AfSIS: Soil functional properties 6

7 Spectral diagnostics tools can be used to produce soil maps Prediction map for soil organic carbon for sub-Saharan Africa. (Source: Africa Soil Information Service)

AfSIS ✓ 60 primary sentinel sites ➡ 9,600 sampling plots ➡ 19,200 “standard” soil samples ➡ ~ 38,000 soil spectra EthioSIS 97 Sentinel sites AfSIS: Soil functional properties (1) 8

From polygon-based to probabilistic mapping 9 + Probability of observing cultivation Current lime requirement ? ~ min [prob(pH < 5.5), prob(cult)] Probability topsoil pH < very acid soils Grid-based probabilistic maps increases the reliability of the map and its power to be combined with other data sources (remote sensing & terrain data) (Walsh, 2013) = Taxonomic soil classification systems provide little information on soil functionality in particular the productivity function (Mueller et al 2010)

Living Standards Measurement Study-LSMS-IMS (3) Improve measurements of agricultural productivity through methodological validation and research 10 Low cost MIR soil testing for smallholder farmers Mobile phones for quick soil screening- being tested

Carbon sequestration in pastoral & agro-pastoral systems (4) Effects of range management on soil organic carbon stocks in savanna ecosystems of Burkina Faso & Ethiopia 11 Fire (controlled burning - 19 years) – Burkina Faso Grazing (Exclosures years) – Ethiopia Fire influence: Carbon allocation - SOC gain Decrease input - SOC loss

Results No Sig difference in SOC between burned and unburned plots 12

Results (2) No Sig difference in SOC between burned and unburned plots 13

Results (4) No sig. difference in SOC between closed and open plots for all age categories 14

15 Challenges in cocoa production Biocarbon development in East and West Africa (5) Develop effective and cost efficient carbon monitoring, reporting and verification systems that can enable smallholders to access carbon markets Soil spectroscopy will be key component Estimating biocarbon using LiDAR data- Taita, Kenya (a) indigenous forest, (b) mixed stand of local and exotic species (Eucalyptus sp.) and (c) cropland with scattered trees Janne et al., 2013

16 Smallholder cocoa in Ivory Coast-V4C (6) Disease + pest Soil fertility Major challenges LDSF and soil spectroscopy to identify constraints & target interventions in cocoa production

17 Trees for food security –ACIAR Rwanda Ethiopia Characterize land health constraints and assessing Agroforestry intervention outcomes

Mitigating Climate Change in Agriculture-MICCA (8) 18 East African Dairy Development (EADD- Kenya) Conservation agriculture (CARE- Tanzania) Characterize (baseline) and assess impacts of climate smart agriculture practices

Measurement and Monitoring Soil Carbon Stock (11) 19 Can we measure soil carbon cost effectively?

Land Health Surveillance Consistent field protocol Soil spectroscopy Coupling with remote sensing Prevalence, Risk factors, Digital mapping Sentinel sites Randomized sampling schemes 20

Measurement and Monitoring Soil Carbon Stock (11) 21 Why measure carbon? 1 What will the protocol deliver? 2 3 How much will it cost? 4 Sampling 5 Field work 6 Lab work 7 Data analysis 8 Presenting results 21

22 Sample size determination Sample allocation Moisture content Soil Carbon stock Error Measurement and Monitoring Soil Carbon Stock (11) Web and excel based tool …. and reporting DATA  INFORMATION  KNOWLEDGE  WISDOM

23 A management that leads to a DECREASE in bulk density will UNDER ESTIMATES SOC stocks & vice versa Monitoring SOC stocks (Ellert and Bettany, 1995) Bulk density as confounding variable in comparing SOC stocks Think mass not depth Why cumulative soil mass?

Cost –error analysis Comparisons of costs of measuring SOC using a commercial lab and NIR Cost IR is cheaper (~ 56%) than dry combustion method for large number of samples Throughput Combustion ~ samples/day NIR ~ 350 samples/day MIR ~ 1000/day Cost –error analysis 24

Cost –error analysis 25 Costs of measurement often exceed the benefits – soil spectroscopy address this challenge

26 Activity Sources of uncertainty Sampling Sampling design (random, stratified random) Sample size SOC measurement Natural variability (spatial) Sample preparation (e.g. contamination, subsampling) Lab method used (instrument resolution) Human error Field data collection (e.g. soil mass, vol) SOC prediction using IR Imported uncertainties (from reference data) Model (assumption) Instrument and human errors Mapping SOC Covariates used Image pre -processing (geometric and radiometric corrections) Scale/resolution (e.g. farm vs landscape) Model (assumption, strength) Sources of uncertainty

Common causes of measurement uncertainty – the instruments used, – the item being measured, – the environment, – the operator, – other sources Measurements can be precise (repeatable) but inaccurate (off-the mark) G.W. Sileshi,

28 Things to be careful! Proper labelingAvoid contamination Lets do it right

Data archiving/publishing 29 Datasaving – dataverse:

More research on cost-effective measurement tools W eb services are needed that allow optimised soil information to be automatically exchanged via the internet Proximal soil sensing Reduce uncertainties in measurements- error propagates Develop national capacities, networking and partnership Baselines are established for important soil properties across Africa Soil spectroscopy filling the data gaps- at National, Regional & Global levels Enable decision makers have clear understanding of soil status and trends Spectroscopy is proved good- adoption and application Cross sentinel/regional sites analysis 30 Finally…

31 Thank you