Leigh Winowiecki*,
*International Center for Tropical Agriculture (CIAT)
20 May 2014
Toward Integrated Analysis of Socio-
...
Pressing Social-Ecological Challenges
TRADE AND ENVIRONMENT REVIEW 2013
MAKE AGRICULTURE TRULY SUSTAINABLE NOW FOR FOOD SE...
Thinking ofBiology
Employing Philosophical
Dialogue in Collaborative
Science
SANFORD D. EIGENBRODE, MICHAEL O'ROURKE, J. D...
•
Reprints
This copy is for your personal, noncommercial use only. You can order presentation-ready copies for distributio...
What are the Next Steps?
!
• No longer approach these
challenges from only one dimension
(or discipline) at a time…..	

• ...
SPECIFIC INITIATIVES
1. Forest,Trees and Agroforestry (FTA) - Sentinel Landscapes
Theme (Global)	

2. CCAFS Benchmark site...
Sentinel Landscapes Theme (lead by ICRAF)
Key Research Questions:
Does a variation in tree cover/ quality affect any of th...
Sentinel Landscapes - http://www.cifor.org/sentinel-landscapes/
• Western Ghats, India	

• West Africa- Burkina Faso, Ghan...
Cross site comparisons: SL -Nicaragua/Honduras/
South Africa/Burkina Faso
The Nicaragua team, led by Dr. Norvin Sepulveda ...
LDSF is a hierarchical field-based sampling design that
provides information on current land use practices,
biophysical co...
Biophysical site comparisons: Cultivation &
Erosion
Biophysical cross site comparisons: 	

Tree Density & Erosion
Biophysical cross site comparisons:
Infiltration Capacity & Trees
Next Steps for SL - Using Social-Ecological
Frameworks
!
• Integrated Analysis 	

• IFRI surveys (http://www.ifriresearch....
1. What are the driving factors of land degradation and
agricultural productivity (in East Africa)?	

2. What are the most...
• Household and community
level indicators and processes!
!
• For which changes can be
evaluated over time, of food
securi...
• Online since February 2014	

• Dataverse Network	

• Use for Trade-off analysis
• Link with:	

• CCAFS Baseline
surveyIF...
ImpactLite - Cropping Systems
• Baseline Analysis Intercropping & Farm Size/ Cross-sie comparisons
ImpactLite - Cropping Systems
• Baseline Analysis Gender & Distance to HH
Kristjanson et al., 2012.Are food insecure smallholder households making
changes in their farming practices? Evidence from...
Building on existing networks and data
Assessing possible linkages between land health, food security and
farmer innovativ...
Co-location of LDSF plots and CCAFS Household survey in
order to link land health data with responses on land
management c...
Four CCAFS
Benchmark sites
in East Africa
used in this
analysis (land
health sampling
date)
Lushoto: 2012
Borana: 2011
Nya...
Biophysical constraints
often limit
management options

!
Effect of cultivation on
SOC in Lushoto is
strong	

Landscape va...
• Predictive models of land and soil
health were developed by the ICRAF
GeoScience Lab (landscapesportal.org),
based on gl...
In East Africa, farms with lower SOC values and higher erosion
prevalence rely more on off-farm income on average.
Linking...
Next Steps:	

•Targeting areas for out scaling in
Tanzania and Uganda (CCAFS
Flagship 4 and CCAFS-CIAT-IFAD
project)!
•Con...
Additional Ideas
looking for spatial patterns…
Lushoto_crpldls
Longitude
Latitude
−4.85
−4.80
−4.75
−4.70
38.32 38.34 38.3...
Part III. Linking Biophysical Data with Agronomic Surveys for Improved
Agricultural Production - AfricaRISING & CCAFS
Task...
Sustainable Intensification &
EcoEfficiency 	

• Matufa - Cluster 3
• Long Cluster 7
Assessment of Soil Properties
Beyond single measure constraints: Establishing Relationships
• pH and Exchangeable Sodium Percentage (ESP) - Between Site...
Mapping Soil Constraints in Babati District,Tanzania
What’s next in AfricaRISING datasets??
• Linking the soil constraints with measures of agricultural
productivity	

• Under...
More next steps, needs, ideas
!
• Well organized data	

• Coordination between efforts, possibly even co-location	

• Inte...
Thank you!
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Toward Integrated Analysis of Socio- Ecological Data for Improved Targeting of Resilient Farming Systems - Leigh Winowiecki

  1. 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. 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. 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. 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 www.nytreprints.com for samples and additional information. Order a reprint of this article now. 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. 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. 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. 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. 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. 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. 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. 11. Biophysical site comparisons: Cultivation & Erosion
  12. 12. Biophysical cross site comparisons: Tree Density & Erosion
  13. 13. Biophysical cross site comparisons: Infiltration Capacity & Trees
  14. 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. 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. 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. 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. 18. ImpactLite - Cropping Systems • Baseline Analysis Intercropping & Farm Size/ Cross-sie comparisons
  19. 19. ImpactLite - Cropping Systems • Baseline Analysis Gender & Distance to HH
  20. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30. Sustainable Intensification & EcoEfficiency • Matufa - Cluster 3 • Long Cluster 7
  31. 31. Assessment of Soil Properties
  32. 32. Beyond single measure constraints: Establishing Relationships • pH and Exchangeable Sodium Percentage (ESP) - Between Sites and Depth
  33. 33. Mapping Soil Constraints in Babati District,Tanzania
  34. 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. 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. 36. Thank you!

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