Long-term soil experiments provide key insights into managing rapidly changing ecosystems over decades. Such experiments directly observe soil changes under different management practices over time, helping understand sustainability and soil-environment interactions. They inform efforts to double food production while reducing environmental impacts. However, long-term experiments require many years, face loss risks, and lack comprehensive networking. Strengthening this research base could help address challenges of food security, the carbon cycle, and nutrient management control.

1. Leigh Winowiecki*,
*International Center for Tropical Agriculture (CIAT)
20 May 2014
Toward Integrated Analysis of Socio-
Ecological Data for Improved Targeting of
Resilient Farming Systems
Collaborators: Tor-Gunnar Vågen**, Peter Laderach*, Rolf
Sommer*, Jennifer Twyman*, Anton Eitzinger*, Caroline
Mwongera*, Kelvin Mashisia*, Anja Gassner**, Patti Kristjanson**
**World Agroforestry Centre (ICRAF)

2. Pressing Social-Ecological Challenges
TRADE AND ENVIRONMENT REVIEW 2013
MAKE AGRICULTURE TRULY SUSTAINABLE NOW FOR FOOD SECURITY
IN A CHANGING CLIMATE
U N I T E D N A T I O N S C O N F E R E N C E O N T R A D E A N D D E V E L O P M E N T
EMBARGO
The contents of this Report must not be
quoted or summarized in the print,
broadcast or electronic media before
18 September 2013, 17:00 hours GMT
Norbert Henninger
Mathilde Snel
Experiences with the
Development and Use
of Poverty Maps
where
poor ?
are the
World Resources Institute
10 G Street, NE
Washington, DC 20002 USA
www.wri.org
UNEP/GRID-Arendal
Service Box 706
4808 Arendal Norway
www.grida.no
Worl d
R e s o u rce s
I n s t i t u t e
UNEP
G R I D
A r e n d a l
During the Holocene, environmental
change occurred naturally and Earth’s regu-
latory capacity maintained the conditions
that enabled human development. Regular
temperatures, freshwater availability and
biogeochemical flows all stayed within a rela-
tively narrow range. Now, largely because of
a rapidly growing reliance on fossil fuels and
human development . Without pressure from
humans, the Holocene is expected to continue
for at least several thousands of years7
.
Planetary boundaries
To meet the challenge of maintaining the
Holocene state, we propose a framework
based on ‘planetary boundaries’. These
Figure 1 | Beyond the boundary. The inner green shading represents the proposed safe operating
space for nine planetary systems. The red wedges represent an estimate of the current position for
each variable. The boundaries in three systems (rate of biodiversity loss, climate change and human
interference with the nitrogen cycle), have already been exceeded.
Atmospheric
Biodiversityloss
Changeinlanduse
Global
Phosphoru
s
Nitrogen
(biogeochemical
Stratospheric
Ocean acidifi
cation
Climate change
Chem
ical pollution
(notye
t quantified)
aerosolloading(notyetquantified)
ozonedepletion
freshwateruse
flowboundary)
cycle
cycle
this will prove to be the exception rather than
the rule. Many subsystems of Earth react in
a nonlinear, often abrupt, way, and are par-
ticularly sensitive around threshold levels of
certain key variables. If these thresholds are
crossed, then important subsystems, such as a
monsoon system, could shift into a new state,
often with deleterious or potentially even
disastrous consequences for humans8,9
.
Most of these thresholds can be defined by
a critical value for one or more control vari-
ables, such as carbon dioxide concentration.
Not all processes or subsystems on Earth have
well-defined thresholds, although human
actions that undermine the resilience of such
processes or subsystems — for example, land
and water degradation — can increase the risk
that thresholds will also be crossed in other
processes, such as the climate system.
We have tried to identify the Earth-system
processes and associated thresholds which, if
crossed, could generate unacceptable envi-
ronmental change. We have found nine such
processes for which we believe it is neces-
sary to define planetary boundaries: climate
change; rate of biodiversity loss (terrestrial
and marine); interference with the nitrogen
and phosphorus cycles; stratospheric ozone
depletion; ocean acidification; global fresh-
water use; change in land use; chemical pol-
lution; and atmospheric aerosol loading (see
Fig. 1 and Table).
In general, planetary boundaries are values
for control variables that are either at a ‘safe’
distance from thresholds — for processes
with evidence of threshold behaviour — or
at dangerous levels — for processes without
472
472-475 Opinion Planetary Boundaries MH AU.indd 472472-475 Opinion Planetary Boundaries MH AU.indd 472 18/9/09 11:12:3918/9/09 11:12:39
The State of
Food Insecurity in the World
The multiple dimensions of food security
2013
REVIEW
Food Security: The Challenge of
Feeding 9 Billion People
H. Charles J. Godfray,1
* John R. Beddington,2
Ian R. Crute,3
Lawrence Haddad,4
David Lawrence,5
James F. Muir,6
Jules Pretty,7
Sherman Robinson,8
Sandy M. Thomas,9
Camilla Toulmin10
Continuing population and consumption growth will mean that the global demand for food will
increase for at least another 40 years. Growing competition for land, water, and energy, in addition to
the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement
to reduce the impact of the food system on the environment. The effects of climate change are a
further threat. But the world can produce more food and can ensure that it is used more efficiently and
during the 18th- and 19th-century Industrial and
Agricultural Revolutions and the 20th-century
Green Revolution. Increases in production will
have an important part to play, but they will be
constrained as never before by the finite resources
provided by Earth’s lands, oceans, and atmo-
sphere (10).
Patterns in global food prices are indicators of
trends in the availability of food, at least for those
who can afford it and have access to world mar-
kets. Over the past century, gross food prices have
generally fallen, leveling off in the past three dec-
ades but punctuated by price spikes such as that
caused by the 1970s oil crisis. In mid-2008, there
was an unexpected rapid rise in food prices, the
cause of which is still being debated, that subsided

3. Thinking ofBiology
Employing Philosophical
Dialogue in Collaborative
Science
SANFORD D. EIGENBRODE, MICHAEL O'ROURKE, J. D. WULFHORST, DAVID M. ALTHOFF. CAREN S. GOLDBERG,
KAYLANl MERRILL, WAYDE MORSE, MAX NIELSEN-PINCUS, JENNIFER STEPHENS, LEIGH WINOWIECKI, AND
NILSA A. BOSQUE-PEREZ
Sustainability: Science, Practice, & Policy
http://sspp.proquest.com
COMMUNITY ESSAY
Tools for enhancing interdisciplinary communication
Leigh Winowiecki
1*
, Sean Smukler
1
, Kenneth Shirley
2
, Roseline Remans
1
, Gretchen Peltier
2
, Erin Lothes
2
,
Elisabeth King
2
, Liza Comita
2
, Sandra Baptista
3
, & Leontine Alkema
4
1
Tropical Agriculture and the Rural Environment Program, Earth Institute, Columbia University, 61 Route 9W, Palisades, NY 10964
USA (email: leigh.winowiecki@gmail.com)
2
Earth Institute, Columbia University, Hogan Hall, Broadway, New York, NY 10025 USA
3
Earth Institute, Center for International Earth Science Information Network (CIESIN), 61 Route 9W, Palisades, NY 10964 USA
4
National University of Singapore, S16 Level 6, 6 Science Drive 2, 117546 Singapore
Authors’ Personal Statement:
Ecosystem Change and Human Well-being
Research and Monitoring Priorities Based on the Findings of the Millennium
Ecosystem Assessment
Ecological Society of America
http://www.jstor.org/stable/3868902 .
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact support@jstor.org.
.
Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Frontiers
in Ecology and the Environment.
http://www.jstor.org
266 SSSAJ: Volume 71: Number 2 • March–April 2007
Soil Sci. Soc. Am. J. 71:266–279
doi:10.2136/sssaj2006.0181
Received 7 May 2006.
*Corresponding author (drichter@duke.edu).
© Soil Science Society of America
677 S. Segoe Rd. Madison WI 53711 USA
With human population doubling to about 10 billion peo-
ple in 50 yr (Bongaarts, 1995), a number of society’s most
important scientific questions concern the future of the Earth’s soil
(Arnoldetal.,1990;NationalResearchCouncil,2001,Wildingand
Lin, 2006)—questions about how food production can be doubled
in the next several decades and about how humanity is transforming
soils and soils’ interactions with the wider environment. We review
how long-term soil experiments help address these questions, spe-
cifically in quantifying decade-scale transformations in soil physics,
chemistry,andbiology(Jenkinson,1991;LeighandJohnston,1994;
Rasmussen et al., 1998; Richter and Markewitz, 2001; Debreczeni
and Körschens, 2003; Tirol-Padre and Ladha 2006).
Long-term soil experiments are field experiments with per-
manent plots that are periodically sampled to quantify soil change
acrosstimescalesofdecades(Table1).Theyareespeciallyvaluableif
their time-series data are accompanied by a sample archive that can
be analyzed long after sample collection. Management treatments
are experimentally controlled and, ideally, sampling, archiving, and
analyses are well documented and statistically rigorous.
Long-running observations of environmental change are
extremely useful to environmental science, management, and
education. Long-term records are fundamental to predicting the
weather, air and water pollution, river flows, tectonic activity, wild-
life populations, and changes in vegetation (Baumgardner et al.,
2002; Magnuson, 1990; Palmer, 1968; Park et al., 2005; Robbins
et al., 1989; Likens, 1989; Leopold et al., 2005; Christensen, 1989;
Walling and Fang, 2003). Although not many soils are studied for
more than several years, our scientific understanding of the soil is
greatly influenced by a few, highly productive, long-running field
experiments (Richter and Markewitz, 2001).
Soils are nonlinear systems resulting from high-order
interactions of physics, chemistry, and biology (Young and
Crawford, 2004). As such, the details of soil change are not
readily predictable as they play out over decades, and temporal
dynamics are studied with several approaches, including short-
term studies in the lab or field, space-for-time substitutions
(i.e., chronosequences), repeated soil surveys, computer model-
ing, and LTSEs. Some uses and challenges of these approaches
are summarized in Table 2.
Short-term soil experiments (STSEs) in the lab and field have
produced much of the data that have built the sciences of soil
physics, chemistry, and biology. Ironically, STSEs often explore
soil processes subject to change over decades, topics such as
aggregation, adsorption, complexation, C dynamics, weather-
ing, microbial activity, redox, and soil fertility itself. Scaling up
and extrapolation are often a major challenge for STSEs. Small
changes or small errors extrapolated across many years can readily
bias long-term projections.
Long-Term Soil Experiments: Keys to Managing
Earth’s Rapidly Changing Ecosystems
Daniel deB. Richter Jr.*
Michael Hofmockel
Nicholas School of the Environ. and Earth Sci.
Duke Univ.
Durham, NC 27708
Mac A. Callaham, Jr.
USDA-Forest Service
Southern Research Station
Athens, GA 30602
David S. Powlson
Agriculture and Environment Division
Rothamsted Research
Harpenden, Hertfordshire
AL5 2JQ UK
Pete Smith
School of Biological Sciences
Univ. of Aberdeen
Aberdeen, Scotland
AB24 3UU UK
To meet economic and environmental demands for about 10 billion people by the mid-21st
century, humanity will be challenged to double food production from the Earth’s soil and
diminish adverse effects of soil management on the wider environment. To meet these chal-
lenges, an array of scientific approaches is being used to increase understanding of long-term
soil trends and soil–environment interactions. One of these approaches, that of long-term
soil experiments (LTSEs), provides direct observations of soil change and functioning across
time scales of decades, data critical for biological, biogeochemical, and environmental assess-
ments of sustainability; for predictions of soil productivity and soil–environment interac-
tions; and for developing models at a wide range of scales. Although LTSEs take years to
mature, are vulnerable to loss, and have yet to be comprehensively inventoried or networked,
LTSEs address a number of contemporary issues and yield data of special significance to soil
management. The objective of this study was to evaluate how LTSEs address three ques-
tions that fundamentally challenge modern society: how soils can sustain a doubling of food
production in the coming decades, how soils interact with the global C cycle, and how soil
management can establish greater control over nutrient cycling. Results demonstrate how
LTSEs produce significant data and perspectives for all three questions. Results also suggest
the need for a review of the state of our long-term soil-research base and the establishment of
an efficiently run network of LTSEs aimed at soil-management sustainability and improving
management control over C and nutrient cycling.
Abbreviations: FACE, free-air carbon dioxide experiment; IRRI, International Rice Research Institute;
LTCCE, Long-term continuous cropping experiment; LTSE, long-term soil experiment; RSS, repeated
soil survey; SFTS, space-for-time substitution; STSE, short-term soil experiment.
SOILSCIENCEISSUESCalls for Interdisciplinary &
Systematic Approaches

4. •
Reprints
This copy is for your personal, noncommercial use only. You can order presentation-ready copies for distribution to
your colleagues, clients or customers here or use the "Reprints" tool that appears next to any article. Visit
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February 11, 2012
The Age of Big Data
By STEVE LOHR
GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking.
Page 1 of 5Big Data’s Impact in the World - NYTimes.com
The Big Data phenomenon presents opportunities and
perils.(Theoretical Foundations of Big Data Analysis, 2013).
!
• Scientists are clamoring for access to the
massive quantities of information produced
by and about people, things, and their
interactions.
• Will large-scale search data help us create
better tools, services, and public goods?
• Will it transform how we study human
communication and culture, or narrow the
palette of research options and alter what
‘research’ means?
• Big Data is less about data that is big than it
is about a capacity to search, aggregate, and
cross-reference large data sets.
• Analysis: drawing on large data sets to
identify patterns in order to make economic,
social, technical, and legal claims.
!
(To link to this article: http://dx.doi.org/
10.1080/1369118X.2012.678878 (Danah Boyd &
Kate Crawford (2012) CRITICAL QUESTIONS)
Era of Big Data

5. What are the Next Steps?
!
• No longer approach these
challenges from only one dimension
(or discipline) at a time…..
• The drivers of food security and
human well-being are inter-related.
!
Food For Thought:
• How do we link (disparate)
interdisciplinary (BIG) datasets?
• How do we locate these data sets
(and assess their quality)?
• What is the guiding conceptual
framework?
• What are our research questions?

6. SPECIFIC INITIATIVES
1. Forest,Trees and Agroforestry (FTA) - Sentinel Landscapes
Theme (Global)
2. CCAFS Benchmark sites (East Africa)
3. AfricaRISING- (Babati,Tanzania)
4. AfSIS datasets - Linking Land Degradation and Productivity
INTEGRATING SOCIAL AND
ECOLOGICAL DATASETS

7. Sentinel Landscapes Theme (lead by ICRAF)
Key Research Questions:
Does a variation in tree cover/ quality affect any of the four
system level outcomes?
reduction in poverty
increased global food security
improved nutrition
better management of natural resources
What explains spatial and temporal variation of tree cover?

8. Sentinel Landscapes - http://www.cifor.org/sentinel-landscapes/
• Western Ghats, India
• West Africa- Burkina Faso, Ghana
• Nicaragua-Honduras
• Mekong (China, Laos,Vietnam)
• Boreno-Sumatra
• Cameroon
• South Africa (proof of concept)
• Oil Palm/Tropical Production Forest Observatory

9. Cross site comparisons: SL -Nicaragua/Honduras/
South Africa/Burkina Faso
The Nicaragua team, led by Dr. Norvin Sepulveda and Dra. Jenny Ordonez of CATIE, will
sample both LDSF sites in Nicaragua. The Honduran teams are led by Dr. Juan Carlos Flores
of CATIE working together with Dr. Kenny Najera of UNA and Jaime Enrique Peralta of FMV.
The UNA team will sample the Rio Blanco site near Catacamas and the FMV team will sam-
ple the remote Rio Platano site in the north. Field
training was extended to students, local farmers,
NGOs, CGIAR centres and others. Participants
were trained in navigation with the GPS units to
locate the randomly generated LDSF plots (160
per site); all aspects of the LDSF, including soil
sample collection, tree and shrub measurements,
erosion observations, among other variables; and
electronic data entry. Preliminary data analysis was conducted on the newly collected data,
including infiltration capacity curves and tree density estimates. Students from UNA will use
the LDSF data for undergraduate theses.
Nicaragua team in a coffee and cacao AF plot in
cluster 12 of the El Tuma landscape, about 30 km
from Matagalpa.
Working with Local Partners - CATIE, National Agricultural University (UNA) in Catacamas, Founda-
tion of MaderaVerde (FMV) in La Ceiba, Institute of Forest Conservation (ICF) inTegulcigalpa
Honduran team in the Brachiaria-dominated
Rio Blanco landscape. UNA students were also
included in the training!
Tor-G.Vågen (ICRAF)
LeighWinowiecki (CIAT)
December, 2012
FieldtrainingatWRFsitesinAgincourt(SouthAfrica)
TripSummary
Action points:
WRF team completes LDSF surveys in
two sites (Agincourt and Bushbuck).
OrderWorldView2 imagery.
Remote sensing analysis of Landsat
andWorldView2 imagery.
Development of a module for range-
land health monitoring for incorpo-
ration into the LDSF.
Development of a module for
improving the assessment of woody
cover and biomass in open wood-
lands, including“trees on farm”for
incorporation into the LDSF.
Development of a module for grass
and tree biodiversity assessments
for incorporation into the LDSF.
Proposal development to support
a“work package”on land health
assessment as part of the collabo-
ration between ICRAF and SUCSES
(Sustainability in Communal Socio-
Ecological Systems).
Background
The University of Witwatersrand Rural Facility (WRF)
is conducting several long-term studies, including
health and demographic surveillance of communities
in Agincourt and Bushbuckridge as part of the INDEPTH
network. Building on these studies, a team from WRF
led by Dr. Wayne Twine has been conducting vegeta-
tion surveys and assessments in these sites for the last
two years.
Following a workshop at WRF in December 2011,
it was agreed that it would be worthwhile to explore
synergies between the methods used by WRF and the
LDSF methodology developed at ICRAF. Dr. Leigh Win-
owiecki (CIAT) and Dr. Tor Vagen (ICRAF) visited WRF
in March 2012 to learn more about the WRF methods
and discuss possible collaboration. As a follow-up to
this visit, two 10 by 10 km sites were proposed that
are co-located with existing WRF vegetation plots and
INDEPTH network villages.
Funding was made available from CRP6 for WRF to
conduct LDSF surveys of these sites, including training
from CIAT/ICRAF scientists. This report is a short sum-
mary of the field training and action points following
this field training.
Field training at Agincourt
A team of scientists, Ph.D. students and field techni-
cians fromWRF were trained on the LDSF methodology
during the week of December 10th, 2012. Field surveys
were initiated at the Agincourt sites (map below) as part
of the training.
The team was trained on vegetation survey methods
following a modified version of the FAO Land Cover
Classification System (LCCS), field assessments of land
degradation risk factors and soil sampling (standard
composite samples and cumulative soil mass).
These data will be used to conduct a comprehensive
soil health assessment of the area and will be linked to
both WRF vegetation surveys and INDEPTH data.
Synergies between the LDSF and WRF vegetation
monitoring methods
One of the primary objectives of this exercise was to
look at synergies between the methods applied byWRF
and the LDSF for monitoring of rangelands and open
woodlands.The LDSF has been applied across all major
climate zones in Africa as part of several initiatives,
including the Africa Soil Information Service project.
The framework has been shown to be very effective
for landscape level assessments of soil and land health.
The WRF has implemented very detailed methods
for assessment of vegetation composition, structure
and trends and it is clear from this collaboration that
the LDSF will benefit from incorporating additional
methods based on those developed byWRF, specifically
for improved assessments of grasslands and woody
biomass. These methods are currently being adapted
and incorporated into the LDSF framework and will be
applied as part of the CRP6 sentinel landscapes initia-
tive (see action points on the right).
The WRF and CIAT/ICRAF team.
Map of the Agincourt (left) and Bushbuck (right) LDSF sites. The background is a
Landsat ETM+ Image from 2009.
Teaching field texture methods.
• Four LDSF sites in Nicaragua/Honduras
• Two LDSF sites in South Africa
• One LDSF in Burkina Faso

10. LDSF is a hierarchical field-based sampling design that
provides information on current land use practices,
biophysical constraints and landscape variability.
Tor-G.Vågen (World Agroforestry Centre (ICRAF))
LeighWinowiecki , LulsegedTamene Desta (International Centre forTropical Agriculture (CIAT))
Jerome E.Tondoh (World Agroforestry Centre (ICRAF)) v4 - 2013
the Land Degradation Surveillance Framework
LDSF
Field Guide

11. Biophysical site comparisons: Cultivation &
Erosion

12. Biophysical cross site comparisons:
Tree Density & Erosion

13. Biophysical cross site comparisons:
Infiltration Capacity & Trees

14. Next Steps for SL - Using Social-Ecological
Frameworks
!
• Integrated Analysis
• IFRI surveys (http://www.ifriresearch.net)
• Household surveys
• Land health surveys
• Institutional Mapping
!
• Understanding how
people rely on
forest resources
• Identifying key HH-
level indicators

15. 1. What are the driving factors of land degradation and
agricultural productivity (in East Africa)?
2. What are the most important factors farmers consider
when making land management decisions?
3. What are the drivers of food security?
4. How can we out-scale locally appropriate Climate Smart
Agriculture (CSA) practices?
Research Questions in our CCAFS activities:

16. • Household and community
level indicators and processes!
!
• For which changes can be
evaluated over time, of food
security, diversity of on-farm
practices, agricultural
production, etc.
Existing Datasets: Baseline Rural Household-level Survey
!
! !
!
CGIAR!Research!Program!on!!
Climate!Change,!Agriculture!and!Food!Security!(CCAFS)!
Summary'of'Baseline'Household'Survey'
Results:'Lushoto,'Tanzania'
December 2011
C.#Lyamchai,#P.#Yanda,#G.#Sayula,#P.#Kristjanson#
!
Online: Dataverse Network

17. • Online since February 2014
• Dataverse Network
• Use for Trade-off analysis
• Link with:
• CCAFS Baseline
surveyIFPRI Intra-
household Gender survey
• CIAT HH survey
• Land health survey
Existing Datasets: ImpactLITE Household-level Survey

18. ImpactLite - Cropping Systems
• Baseline Analysis Intercropping & Farm Size/ Cross-sie comparisons

19. ImpactLite - Cropping Systems
• Baseline Analysis Gender & Distance to HH

20. Kristjanson et al., 2012.Are food insecure smallholder households making
changes in their farming practices? Evidence from East Africa.
• Dependent variable: # of
food deficit months
• Variables: Credit, cash
source, education,
HHSIZE, HHnonworkers,
information, land,
production, ProdDiversity,
site, transport,
innovativeness, soil, water
• These variables
explained 40% of
variation Graphic from: Kristjanson et al., 2012 Food Sec.
(2012) 4:381–397 DOI 10.1007/s12571-012-0194-z
Using the CCAFS Baseline Survey

21. Building on existing networks and data
Assessing possible linkages between land health, food security and
farmer innovativeness, across landscapes and between sites.
Tor-G.Vågen (World Agroforestry Centre (ICRAF))
LeighWinowiecki , LulsegedTamene Desta (International Centre forTropical Agriculture (CIAT))
Jerome E.Tondoh (World Agroforestry Centre (ICRAF)) v4 - 2013
the Land Degradation Surveillance Framework
LDSF
Field Guide

22. Co-location of LDSF plots and CCAFS Household survey in
order to link land health data with responses on land
management changes
Hoima: Baseline Survey Households (n=140)
(purple) and LDSF plots (n=160) (yellow)

23. Four CCAFS
Benchmark sites
in East Africa
used in this
analysis (land
health sampling
date)
Lushoto: 2012
Borana: 2011
Nyando: 2005
Hoima: 2012

24. Biophysical constraints
often limit
management options
!
Effect of cultivation on
SOC in Lushoto is
strong
Landscape variation in
land health is high
Linking to HH survey
data
http://thedata.harvard.edu/dvn/dv/
CCAFSbaseline/faces/study/
StudyPage.xhtml?globalId=doi:
10.7910/DVN/
24451&studyListingIndex=8_15b22833
e9a960765cf70a22ac53 !
Land Health
Understanding Landscape-scale Variability of Soil Health Indicators:
Assessing the Effects of Cultivation on Soil Organic Carbon
Leigh Winowiecki, International Center for Tropical Agriculture (CIAT)
Lushoto & Hoima:
Soil Health Baseline Assessment
22 January 2014
Soil organic carbon (SOC) is an important indicator of soil
health because it integrates both inherent properties of soil as
well as anthropogenic activities, including land-use change and
land management, while contributing to the overall fertility of
the soil. Biophysical field surveys were conducted using the Land
Degradation Surveillance Framework (LDSF) in Hoima and
Lushoto as part of the “Playing out transformative adaptation in
CCAFS benchmark sites in East Africa: When, where, how and
with whom?” project. Each LDSF site had 160 sampling plots
where field observations and samples were collected, including
soil samples (0-20 cm and 20-50 cm), erosion assessments, tree
and shrub measurements, as well as current and historic land
use. LDSF sampling plots were co-located with the 140 CCAFS
Household surveys in order to conduct interdisciplinary analysis
on land health, gender and social-economic datasets.
The objective was to provide a biophysical baseline of key soil
and land health metrics across the landscape in order to under-
stand variability and effects of land use. Another key objective
was to identify opportunities for strategic land management
interventions that can improve soil health and overall produc-
tivity. There is high spatial variability in topsoil OC across the
Lushoto landscape, as shown in the map on the right (Figure 1).
In Hoima, there is less variability in SOC (Figure 2) overall. Both
sites have less topsoil OC in cultivated areas than in semi-natural
areas, with the strongest effects of cultivation in Lushoto (Figure
2). Overall, cultivation also leads to less variability in SOC.
The results highlighted here show that current cultivation
practices are leading to sharp declines in SOC in Lushoto, and
interventions need to focus on practices that stabilize or increase
SOC in order to increase the capacity of the soil to enhance
productivity and the adaptive capacity of the farming system in
general. We aim to link these datasets with existing surveys (HH
Baseline, IMPACT Lite, gender, etc.) to further understand the
different management strategies and socio-economic factors that
contribute to the variability in SOC under cultivated area.
Figure 1. Topsoil organic carbon values (g kg-1
) for each of the 160
LDSF sampling plots in Lushoto.
Figure 2. Boxplots of topsoil organic carbon (g kg-1
) for cultivated (1) and non-cultivated (0) plots in Lushoto (n= 104 and n=53, respectively)
and Hoima (n= 65 and n=95, respectively).The vertical lines show overall means for cultivated and non-cultivated plots (33 and 51 g kg-1
) .
Figure 3. Landscape photo from Lushoto illustrating the
complexity of land uses across the site.
LeighWinowiecki,throughCIAT-TSBF,hostedafieldtrainingintheKaruraforest,NairobiinSep-

25. • Predictive models of land and soil
health were developed by the ICRAF
GeoScience Lab (landscapesportal.org),
based on global LDSF datasets.
• Extracting SOC and erosion values
from MODIS imagery using HH
coordinates.
• Strong relationship between SOC and
erosion.
Soil and Land Health Indicators

26. In East Africa, farms with lower SOC values and higher erosion
prevalence rely more on off-farm income on average.
Linking Food Deficient Months with Land Health Indicators

27. Next Steps:
•Targeting areas for out scaling in
Tanzania and Uganda (CCAFS
Flagship 4 and CCAFS-CIAT-IFAD
project)!
•Conducting quantitative trade-off
analysis of different management
practices (e.g., linking to
ImpactLITE, IFPRI Gender survey,
etc.) !
•Linking to gender-disaggregated
survey data!
Ongoing analysis and activities

28. Additional Ideas
looking for spatial patterns…
Lushoto_crpldls
Longitude
Latitude
−4.85
−4.80
−4.75
−4.70
38.32 38.34 38.36 38.38 38.40
12
34
5
12
13
14
151617
18
19
2021
22
23
24
45
46
6
78
11
86107
108
109
110111
112 113114115
116117118119140
9
87
8889
90
9192939495
96
97
9899100101
102
103
104
105
120
10121122123
124
125
126
127
128
129
130
131
132133
134
135136137138
139
67686970
71
72
73
74
7576
77
78
798081
8283
8485
106
25 2627
28
29
3031
323334
35
36
37
38
39
40
41
42
43
44 4748
49
50515253
545556575859
60616263
64
65
66
0.0
0.2
0.4
0.6
0.8
1.0
Lushoto
Longitude
Latitude
−4.85
−4.80
−4.75
−4.70
38.32 38.34 38.36 38.38 38.40
0.0
0.2
0.4
0.6
0.8
1.0
Lushoto: Terrace
Longitude
Latitude
−4.85
−4.80
−4.75
−4.70
38.32 38.34 38.36 38.38 38.40
12
5
12
13
17
18
1921
22
45
46
6
78
107
108
109
111
112 113114140
989 91
939495
97
98100101
102
103
104
105
120
10121122125
126
127
131
132133
134
135136137138
139
71
72
78
798081
8283
85
106
26
28
29
3031
32
35
36
37
38
39
40
4143
4748
49
50515253
5455575859
6263
65
66
0.0
0.2
0.4
0.6
0.8
1.0

29. Part III. Linking Biophysical Data with Agronomic Surveys for Improved
Agricultural Production - AfricaRISING & CCAFS
Task
1.2.
Soil
survey
to
characterize
two
sen8nel
sites
in
the
Baba8
Project
Area
!
• Two
LDSF
surveys
(2013)
• One
co-­‐located
with
agronomic
surveys
!
!
•
Added
another
LDSF
with
GHG
measurements
and
a
weather
staEon
(2014)

30. Sustainable Intensification &
EcoEfficiency
• Matufa - Cluster 3
• Long Cluster 7

31. Assessment of Soil Properties

32. Beyond single measure constraints: Establishing Relationships
• pH and Exchangeable Sodium Percentage (ESP) - Between Sites and Depth

33. Mapping Soil Constraints in Babati District,Tanzania

34. What’s next in AfricaRISING datasets??
• Linking the soil constraints with measures of agricultural
productivity
• Understand yield variability across the sites
• Possible socio-economic-environmental trade-off analysis with
additional HH data
• Measures of EcoEfficiency

35. More next steps, needs, ideas
!
• Well organized data
• Coordination between efforts, possibly even co-location
• Interdisciplinary collaboration
• Commitment
• Innovative analytical skills
• Food for thought:
The FourV’s
!
!
!
!
!
!
!
!
http://www.digital-warriors.com/wp-content/uploads/2014/01/4-Vs-of-Big-Data.jpg

36. Thank you!