Ermias Betemariam†, Calogero Carletto˚, Sydney Gourlay˚, Keith Shepherd†

† World Agroforestry Centre˚ The World Bank

29th International Conference of Agricultural EconomistsMilan, Italy – August 8-14, 2015

Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and

Implications for Agricultural Statistics

Support• LSMS Methodological Validation Program, funded by UK Aid

Objectives• Test subjective approaches to measurement vis-à-vis

objective methods for land area, soil fertility & crop production

Partnerships• Central Statistical Agency, World Agroforestry CentreStatus• Fieldwork: Completed March 2014• Soil Testing: Completed March 2015• Analysis & Dissemination: On-going

Land and Soil Experiment Research (LASER): Ethiopia

Methodologies tested:Land Area • Traversing (i.e., compass and

rope)• GPS measurement (Garmin)• GPS measurement (Android

tablet)• Farmer self-reported area• Clinometer• Farmer self-reported incline

Completed duringthe post-planting

visiton up to two fieldsper household

Soil Fertility

• Spectral Soil Analysis • Conventional Soil Analysis • Farmer self-reported soil

quality

Samples collected during the post-planting visit, processed at

regional labs and shipped

to ICRAF Nairobi for analysis

Maize Production

• Crop-cutting using a 4m x 4m subplot and 2m x 2m subplot

• Farmer self-reported harvest

Completed by field

teams when alerted

by household

LASER Methodologies1018

households interviewed

1799 fields

selected for objective

measurement and soil testing

3791 soil samples collected*

205 fields with

crop-cutting*2 samples were collected from each field (different depths and sampling procedures), an additional sample was collected on fields with crop-cutting.

Survey Design• 3 household visits:

– Post-Planting– Crop-Cutting– Post-Harvest

• 5 mobile field teams

• CAPI administration

Elevation Rainfall AEZ

LASER Sample

85 EAs12 HH Each

Soil Sampling Protocol

Soil Sampling Protocol

Objective Data• 100% of samples were

tested with:– Mid-infrared diffuse

reflectance (MIR) spectroscopy

– Laser diffraction particle size distribution analysis (LDPSA)

• 10% of samples tested with:– Conventional wet

chemistry analysis– X-ray methods for

mineralogy (XRD)– Total element analysis

(TXRF)

Objective Data

Predictive power of

MIR spectrosco

py

Objective DataMean SD

Physical% Sand 12.3 7.1% Clay 65.1 12.8% Silt 22.6 7.4

ChemicalpH 6.3 0.6

Macronutrients:Total Carbon 3.4 1.3

Total Nitrogen 0.3 0.1Exchangeable Calcium+ 3454 1821

Potassium+ 735 278

Exchangeable Magnesium* 539 201Micronutrients:

Iron+ 160 63

Zinc+ 6 4

Phosphorous+ 46 44

Exchangeable Manganese+ 182 51+ Extracted with Mehlich 3 method* Extracted with wet method

Top Soil

Objective Data

Distribution of soil organic carbon by

administrative zone.

Objective Data

• variation of soil properties within zones…

02

46

8S

oil O

rgan

ic C

arbo

n (%

)

excludes outside values

West Arsi Zone

Enumeration Area, West Arsi Zone

…and within enumeration areas

Objective Data

Subjective DataWhat is the quality of soil on [PLOT]?

Subjective Soil Quality

Good 42%

Fair 53%

Poor 5%

ET21%

UG7.7% TZ

6.8%

MW11%

source: worldbank.org/lsms

Subjective DataFarmer assessment of soil quality included: overall quality, texture, color, and type

020

4060

Per

cent

FINE BETWEEN COARSE AND FINE COARSE

SR Good Quality SR Fair Quality SR Poor Quality

Farmer Identified Soil Texture & Quality

010

2030

4050

Per

cent

BLACK RED LIGHT

SR Good Quality SR Fair Quality SR Poor Quality

Farmer Identified Soil Color & Quality

16

Comparison: Subjective vs. Objective

13% of the top-soils have SOC < 2%

Of these, respondents only classified 6% as poor quality.

Comparison: Subjective vs. Objective

0.0

2.0

4.0

6D

ensi

ty

0 20 40 60 80 100CEC

SR Good Quality SR Fair Quality SR Poor Quality

kernel = epanechnikov, bandwidth = 2.2310

Cation Exchange Capacity

Comparison: Subjective vs. Objective

Comparison: Subjective vs. Objective

0.0

2.0

4.0

6.0

8D

ensi

ty

0 10 20 30 40Predicted % Sand

Reported as "very fine" or "fine"Reported as "between coarse and fine"Reported as "coarse" or "very coarse"

Respondent Assessment of Soil Texture

0.0

2.0

4.0

6.0

8D

ensi

ty

0 10 20 30 40Predicted % Sand

Reported as "sandy" soilReported as "clay" soilReported as "mixture of sand and clay" soil

Reported Soil Type

Scalability & Implementation Challenges• Fieldwork timeline implications:

– Avg 38 minutes per plot– Additional driving time for lab delivery– Consider crop-type and soil sample timing

• Logistics:– Sample labeling– In-country lab infrastructure

• Currently testing in-country MIR– Capacity/timeline at analytical lab

• Cost implications– Not cheap, but getting cheaper (advantage of

spectral analysis)

• Subjective soil data exhibits little variation

• Respondents (in this country context) often optimistic about state of the soils– Unclear on which

properties farmers are basing their assessment (and questionnaire design adjustments).

• Spectral soil analysis a feasible option to improve soil quality data (in the right context, and with the right budget).

Concluding Thoughts

Next Steps• Further analysis on optimal respondent for

subjective questions.– Also, improve questionnaire design by

identifying subjective questions with strongest correlation to objective measures.

• Does heterogeneity of soil properties in an EA affect the ability of respondents to assess their soil quality?

• Compare plot-level soil results with national soil maps (such as the Harmonized World Soil Database).

Questions?

Ermias Betemariam†, Calogero Carletto˚, Sydney Gourlay˚, Keith Shepherd†

† World Agroforestry Centre˚ The World Bank

29th International Conference of Agricultural EconomistsMilan, Italy – August 8-14, 2015

Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and

Implications for Agricultural Statistics