Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and Implications for Agricultural Statistics
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Validates the method for improving measurements of agricultural productivity by combining household level and soil fertility data. This is achived by: developing soil-plant spectral analytical methods and diagnostic tools for rapid, low cost and reliable assessment of soil samples using light (Technology); and demonstrating applicability of integrating scientific method of soil quality assessment along with the socio-economic panel data
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Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and Implications for Agricultural Statistics
- 1. Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and Implications for Agricultural Statistics http://www.worldagroforestry.org/research/land-health Ermias Betemariam (e.betemariam@cgiar.org) Sydney Gourlay Keith Shepherd
- 2. Context 1Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Innovations in Agricultural Statistics • Administrative data is often seen as methodologically flawed and lacks credibility • Lack of consistent, good quality data on soil health • Data collection methods • The role of technology • What works in developing countries?
- 3. Context (2) 2Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Methodological improvements in smallholder agricultural statistics for decision making http://www.csa.gov.et/index.php/component/co ntent/article/14-survey-reports/123-latest- national-statistics-abstract
- 4. Objectives 3 Improving measurements of agricultural productivity by combining household level and soil fertility data • Develop soil-plant spectral analytical methods and diagnostic tools for rapid, low cost and reliable assessment of soil samples using light (Technology) • Demonstrate applicability of integrating scientific method of soil quality assessment along with the socio-economic panel data Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015|
- 5. Field data 4Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 3661 soil samples collected from three agro-ecologica zones • Household socio-economic data • Subjective assessment of soil quality • Soil sampling • Objective assessment of soil quality
- 6. Lab analyses Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 5 Laser Diffraction Particle Size Analysis(LDPSA) Total X-ray Fluorescence spectroscopy (TXRF) for elemental analysis FT Diffuse reflectance MIR Alpha MIR spectrometer
- 7. Instrumentation Dispersive VNIRFT-NIR FT-MIR Handheld NIR/MIR •Portable •Repeatability? •External service •No validation •Benchtop •Repeatability •Self serviceable •Validation in-built •ISO compliant •Industry proven •Multipurpose •Benchtop •Repeatability •No gas purging •Some servicing •Robotic •Validation in-built •ISO compliant •Outperforms NIR •Handheld •Sample homogeneity? •Variable moisture? •Repeatability? •Still expensive •Rapidly developing •Need to prepare by developing soil reference libraries Working on Rural labs Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 6 Lab analyses (2)
- 8. Why infrared spectroscopy? Rapid Low cost Reproducible Predicts many soil functional properties Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 7
- 9. 8 Land Health applications Africa Soil Spectroscopy Lab Network EthioSIS 97 Sentinel sites Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015|
- 10. MIR- reflectance 9Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015|
- 11. 1 0 Mid-infrared spectroscopy strongly predicts soil properties Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| MIR- reflectance
- 12. 1 1 Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Objective vs subjective soil quality measurements Farmers use soil texture and color to describe soil qualities
- 13. 1 2 Soil carbon and pH values Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Objective vs subjective soil quality measurements SOC (95% CI): 3.18 - 3.30% pH(95% CI): 6.28 - 6.34
- 14. 1 3 Objective vs subjective soil quality measurements Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Soil quality indicators USDA: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/health/assessment/?cid=stelprdb1237387
- 15. 1 4 Subjective soil quality assessment poorly correlated to the scientific output Science: about 17% of the soils have SOC < 2% but only 5%= poor soils Local respondents identified only 5% of the as poor soils Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Objective vs subjective soil quality measurements
- 16. 1 5 Age Sex Literacy Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Objective vs subjective soil quality measurements Age class, sex, and literacy did not significantly influence the subjective Grading of soil quality Farmers better identify poor and not-poor soils but not “Fair soils” that future Questions should consider
- 17. 0 2000 4000 6000 10 50 100 150 200 250 Carbonmeasurment cost(USD) Number of samples NIR spectroscopy Thermal oxidation Sample preparation 0 3 6 9 12 15 Carbonmeasurement costpersample(USD) NIR spectroscopy Thermal oxidation Sample preparation Soil sampling Cost –error analysis 0 2000 4000 6000 8000 0 500 1000 1500 Carbonmeasurement cost(USD) Number of samples Thermal oxidation Comparisons of costs of measuring SOC using a commercial lab and NIR Cost IR is cheaper (<~ 56%) than combustion method for large number of samples Throughput Combustion ~ 30-60 samples/day NIR ~ 350 samples/day MIR ~ 1000/day Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 1 6
- 18. Surveillance science Land health metrics Consistent field protocol Soil spectroscopy Coupling with remote sensing Sentinel sites Randomized sampling schemes Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| 1 7
- 19. AfSIS: Soil functional properties From polygon-based to probabilistic mapping + Probability of observing cultivation Current lime requirement ? ~ min [prob(pH < 5.5), prob(cult)] Probability topsoil pH < 5.5 ... 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) = 1 8 Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015|
- 20. Data sources 1 9 Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015|
- 21. • More research on cost-effective measurement tools- the future is bright • Subjective and objective measurement results do not much well • It was possible to integrate soil health measurement into the panel household survey • Enhancing our understanding of household attributes, land management practices and soil health management • Enable decision makers have clear understanding of soil status and trends Finally… 2 0 Ermias Betemariam| ISI2005 2015, | Rio de Janeiro| July 2015| Smart data - Smart decisions
Editor's Notes
- Lack of consistent, good quality data on soil health
- We won a grant to incorporate a field-scale soil monitoring component in the World Bank’s Living Standards Measurement Study, which has been helping a number of Governments establish household panel surveys and agricultural monitoring over several decades. We will be piloting a soil fertility monitoring component in two African countries.
- Our Soil-Plant Spectral Diagnostics lab continues to attract much interest – we received over 500 visitors in 2011, over half of which received some training. Requests for capacity building are growing exponentially and we need to increase our staff to deal with it.
- Global libraries are needed to be able to quickly bring new instruments into calibration
- Black red light/white/yellow Fine, medium, coarse
- A quick reminder of our conceptual framework and tools, we work by a set of surveillance science principles, which are similar to those used in public health surveillance – which emphasize quantifying health problems and associated risk factors in populations. We implement those science principles through a set of tools, which encompass use of randomized, landscape level sampling schemes. The use of consistent field sampling protocols so we collect data on land health indicators in the same way everywhere. The use of soil spectroscopy methods to provide high throughput low cost analysis of key soil health metrics, centred on soil functional properties. Coupling of the field and lab observations with remote sensing data, to provide consistent data on the population distributions and prevalence of land health problems, associated risk factors and digital mapping of indicators.