Utilizing a framework of indicators to assess
sustainable intensification
Sieglinde Snapp1,2, Philip Grabowski1, Regis Chikowo1,3, Erin Anders1 and Mateete Bekunda2
Contact: [email protected]
1Plant
Soil and Microbial Sciences Department, Michigan State University, East Lansing, Michigan - USA
2
International Institute of Tropical Agriculture (IITA), Arusha – Tanzania
3Department
of Crop Science, University of Zimbabwe, Harare – Zimbabwe
What is sustainable intensification?
Social
Economic
Productivity
Human condition
Environment
SI Framework
DOMAIN
SELECT INDICATORS
SCALE
Landscape/Administrative
Objective: evaluate
relative
sustainability of a
technology or
intervention
Productivity
Yield
Residue production
Yield variability
Economic
Profitability (gross margins)
Labor availability
Land availability
Environmental
Months soil cover
Nutrient balances
Human Condition
Nutrition
Food Security
Social
Gender equity
Social conflict
Farm/Household Scale
Field/Animal Herd Scale
Productivity domain
Yield
(partitioned by
species and
tissue type) and
residues (total =
NPP)
Animal
productivity
considering land
area
Field/plot
Farm
Household
kg biomass
(yield, fodder,
residue, weeds)
/ ha / season 1,2,3
kg (yield,
fodder,
residue) /
ha /
season1,2,3
Farmer
Net Primary Productivity 1Agricultural
perceptions
(above ground)4
survey (recall)
and ratings of
2Yield
technology
measurements
yield
3Crop models
5
performance
(point models)
kg tree product
(fruit, wood,
fodder) / area
under crown (or
trees per ha) 1,2
Animal
production by
product (milk
yield, weight
gain, meat,
manure,
reproduction
rate) 1,2
Landscape or
Administrative Unit
Measurement
method
4Remote
sensing
5Farmer
participatory
trials
Animal production by
Net commercial off-take 1Agricultural
product (milk yield, weight relative to the total
survey (recall)
gain, meat, manure) / ha
grazing and fodder
2Production
grazing and fodder land
production area1
measurements
1,2
used
Animal product/farm/year
1,2
Environment domain
Field/plot
Farm
Household
Nutrient
kg N,P, and K inputs
N/A
Partial Balance (fertilizer, manure, etc.)
less kg N, P, and K in total
biomass removed (harvest,
grazing) per hectare per
year1,2
GHG Emissions
CO2 equivalents per hectare
(also broken down by CO2,
CH4, and N2O)1
N/A
Landscape or
Administrative Unit
kg N,P, and K inputs
(fertilizer, manure,
etc.) – kg N, P, and K in
total biomass
removed (harvest,
grazing) per hectare
per year1,2
Measurement
method
1Agricultural
survey for
inputs and
outputs
CO2 equivalents per
hectare (also broken
down by CO2, CH4, and
N2O)1
1Lookup
2Lookup
tables
to estimate
nutrients in
harvest and
organic inputs
tables
for each activity
or input
Applying the indicators to evaluate legume
systems in Malawi
Systems compared:
• Mz0 – Continuous sole maize – no fertilizer
• MzNPK – Continuous sole maize with 69 kg N/ha fertilizer
• PpMz – Maize-Pigeonpea intercrop with 35 kg N/ha fertilizer
• GnPp-Mz – Groundnut-Pigeonpea intercrop rotated with maize (35 kg N/ha
fertilizer in maize phase)
Data sources:
1) Mother trials – yield and biomass (2-3 seasons)
2) APSIM modeling results – yield variability, long-term soil changes
3) Survey data (baseline for prices + hh composition; baby trials survey for
pairwise ranking of technologies
Mz0
MzNPK
Maize yield
(max 5000 kg/ha)
Results
% females prefering
(max = 100%)
• Legume systems
NOT having crop
improve soil Probability offailure
1.0
0.9
Maize residue production
(max 10,000 kg/ha)
0.8
0.7
0.6
(max = 100%)
• Competitive for
maize and
needs met
profits Probability(maxof 100%
= 100%)
0.5
• Improved
nutrition
0.0
Legume residue production
(max 10,000 kg/ha)
0.4
0.3
0.2
Legume yield
(max 860 kg/ha)
0.1
Soil N % change over 25 years
(min = -15%, max = +15%)
Gross margin per ha -base
(max $700)
• High female
ranking
Soil carbon % change over 25yrs
(min = -12%, max = +12%)
Months of soil cover
(max 12)
Gross margin per ha -hi mz price
(max $1600)
Harvest per kg N/ha added
(max 180)
Mz0
MzNPK
PP-Mz
Maize yield
(max 5000 kg/ha)
Results
% females prefering
(max = 100%)
• Legume systems
NOT having crop
improve soil Probability offailure
1.0
0.9
Maize residue production
(max 10,000 kg/ha)
0.8
0.7
0.6
(max = 100%)
• Competitive for
maize and
needs met
profits Probability(maxof 100%
= 100%)
0.5
• Improved
nutrition
0.0
Legume residue production
(max 10,000 kg/ha)
0.4
0.3
0.2
Legume yield
(max 860 kg/ha)
0.1
Soil N % change over 25 years
(min = -15%, max = +15%)
Gross margin per ha -base
(max $700)
• High female
ranking
Soil carbon % change over 25yrs
(min = -12%, max = +12%)
Months of soil cover
(max 12)
Gross margin per ha -hi mz price
(max $1600)
Harvest per kg N/ha added
(max 180)
Mz0
MzNPK
PP-Mz
Gnt-PP rotate MZ
Maize yield
(max 5000 kg/ha)
Results
% females prefering
(max = 100%)
• Legume systems
NOT having crop
improve soil Probability offailure
1.0
0.9
Maize residue production
(max 10,000 kg/ha)
0.8
0.7
0.6
(max = 100%)
• Competitive for
maize and
needs met
profits Probability(maxof 100%
= 100%)
0.5
• Improved
nutrition
0.0
Legume residue production
(max 10,000 kg/ha)
0.4
0.3
0.2
Legume yield
(max 860 kg/ha)
0.1
Soil N % change over 25 years
(min = -15%, max = +15%)
Gross margin per ha -base
(max $700)
• High female
ranking
Soil carbon % change over 25yrs
(min = -12%, max = +12%)
Months of soil cover
(max 12)
Gross margin per ha -hi mz price
(max $1600)
Harvest per kg N/ha added
(max 180)
Discussion
• Data gaps
related to
social conflict
over residue
grazing and
labor
• Complex
gender effects
from
intensifying
legume
production
Conclusion
• The SI indicator framework facilitated holistic analysis of legume
systems and the identification of important data gaps
• A transdisciplinary approach (interdisciplinary research collaboratively
engaging with farmers) is needed to develop and assess management
practices for sustainable intensification
DFID funded SAIRLA project
• Objective – Analyze and develop tools for analyzing the effects of
agricultural interventions on women and youth
• Focus on supporting decision-makers’ use of gender analysis for SI
projects and contextualizing gender indicators through farmer
participation
• IITA lead agency, Gundula Fischer as PI
• P. Grabowski and L. Zulu at MSU
• Supporting LUANAR (Malawi) and Univ. of Ghana
• August 2016 to December 2019