Hands-on Soil Infrared Spectroscopy Training Course Getting the best out of light 11 – 14 November 2013

1. Applications of soil spectroscopy on Land Health Surveillance Ermias Betemariam Hands-on Soil Infrared Spectroscopy Training CourseGetting the best out of light11 – 14 November 2013

2. Context Context • 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 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

3. Context (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

4. Land Health (SD4) Land Health - the capacity of land to sustain delivery of essential ecosystem services 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.

5. Land Health Projects Land Health Projects

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

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

8. AfSIS: Soil functional properties 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)

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

10. AfSIS: Soil functional properties (1) From polygon-based to probabilistic mapping Probability topsoil pH < 5.5 ... very acid soils Probability of observing cultivation Current lime requirement ? ~ min [prob(pH < 5.5), prob(cult)] + = • Grid-based probabilistic maps increases the reliability of the map and its power to be combined with other data sources (remote sensing & terrain data) Taxonomic soil classification systems provide little information on soil functionality in particular the productivity function (Mueller et al 2010) (Walsh, 2013)

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

12. Carbon sequestration in pastoral & agro-pastoral systems (4) Effects of range management on soil organic carbon stocks in savanna ecosystems of Burkina Faso & Ethiopia Fire (controlled burning -19 years) – Burkina Faso • Fire influence: • Carbon allocation - SOC gain • Decrease input - SOC loss Grazing (Exclosures 12- 36 years) – Ethiopia

13. Results No Sig difference in SOC between burned and unburned plots

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

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

16. Biocarbon development in East and West Africa (5) Challenges in cocoa production • 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

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

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

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

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

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

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

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

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

25. Cost –error analysis 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 ~ 30-60 samples/day NIR ~ 350 samples/day MIR ~ 1000/day

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

27. Sources of uncertainty

28. 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, 2013

29. Things to be careful! Lets do it right Avoid contamination Proper labeling

30. Data archiving/publishing Datasaving – dataverse: http://thedata.harvard.edu/dvn/

31. Finally… • More research on cost-effective measurement tools • Web 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

32. Thank you