Applications of soil spectroscopy on Land Health Surveillance

Transcription

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

2 Context (i) 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 carbon trading purposes requires high levels of measurement precision Increasing demand for soil data at fine spatial resolution 1

3 Context Context (ii) 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 2

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. 3

5 Land Health Projects Land Health Projects (i) 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 4

6 Land Health Projects (ii) 5

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

8 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 7

9 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) 8

10 AfSIS: Soil functional properties From polygon-based to probabilistic mapping Probability topsoil ph < 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) 9

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 1 0

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 years) Ethiopia 1 1

13 Results No Sig difference in SOC between burned and unburned plots 1 2

14 Results No Sig difference in SOC between burned and unburned plots 1 3

15 Results No sig. difference in SOC between closed and open plots for all age categories 1 4

16 Biocarbon Challenges development in cocoa production 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.,

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? 1 6

18 Trees for food security ACIAR Characterize land health constraints and assessing Agroforestry intervention outcomes Rwanda Ethiopia 1 7

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) 1 8

20 Measurement and Monitoring Soil Carbon Stock (11) Can we measure soil carbon cost effectively? 1 9

21 Land Health Surveillance Sentinel sites Randomized sampling schemes Consistent field protocol Prevalence, Risk factors, Digital mapping Coupling with remote sensing Soil spectroscopy 2 0

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

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 2 2

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 Bulk density C conc.(%) Depth(cm) (g/cm) SOC stock (Mg/ha) Error % (Ellert and Bettany, 1995) 2 3

25 Cost (USD) Cost (USD) Cost per sample (USD) Cost error analysis Cost error analysis Comparisons of costs of measuring SOC using a commercial lab and NIR NIR spectroscopy Thermal oxidation Sample preparation Soil sampling NIR spectroscopy Sample preparation Thermal oxidation Soil sampling Number of samples Thermal oxidation NIR spectroscopy Cost IR is cheaper (~ 56%) than dry combustion method for large number of samples Number of samples Throughput Combustion ~ samples/day NIR ~ 350 samples/day MIR ~ 1000/day 2 4

26 Half 95% confidence interval (t C ha -1 ) Half 95% confidence interval (t C ha -1 ) Cost error analysis Cost error analysis Costs of measurement often exceed the benefits soil spectroscopy address this challenge Number of samples Cost of carbon measurement (USD) 2 5

27 Sources of uncertainty Activity Sampling SOC measurement SOC prediction using IR Mapping SOC Sources of uncertainty Sampling design (random, stratified random) Sample size Natural variability (spatial) Sample preparation (e.g. contamination, subsampling) Lab method used (instrument resolution) Human error Field data collection (e.g. soil mass, volume) Imported uncertainties (from reference data) Model (assumption) Instrument and human errors Covariates used Image pre -processing (geometric and radiometric corrections) Scale/resolution (e.g. farm vs landscape) Model (assumption, strength) 2 6

28 Common causes of measurement uncertainty Accuracy versus precision the instruments used, the item being measured, the environment, the operator, other sources CASE 1 High precision (repeatable) High accuracy Random error (less biased) CASE 2 High precision (repeatable) Low accuracy Systematic error (biased) CASE 3 Low precision (not repeatable) High accuracy Random error (less biased) CASE 4 Low precision (not repeatable) Low accuracy Systematic error (biased) 2 7

29 Things to be careful! Lets do it right Avoid contamination Proper labeling 2 8

30 Data archiving/publishing Datasaving dataverse: 2 9

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 3 0

32 Thank you