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Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
Vital Signs: An integrated monitoring system for agricultural landscapes
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Vital Signs: An integrated monitoring system for agricultural landscapes

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Presented by Roseline Remans, Columbia University at the Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013 …

Presented by Roseline Remans, Columbia University at the Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013

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  • 1. Vital Signs An Integrated Monitoring System for Agricultural Landscapes Roseline Remans, Columbia University Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013
  • 2. An Integrated Monitoring System for Agricultural Landscapes • Ecosystem Services • Agricultural Production • Human Wellbeing
  • 3. ETHIOPIA GHANA UGANDA RWANDA Vital Signs is starting in SubSaharan Africa TANZANIA MOZAMBIQUE
  • 4. Regions of impending agricultural change
  • 5. Integrated Monitoring of Agricultural Landscapes For decision making Co – location of data in space and time – to assess tradeoffs and synergies Use of existing systems and data as much as possible – often adding the environmental components Ownership by governments to link with national data collection efforts Build national capacity on data collection, storage, analysis and use
  • 6. Vital Signs Approach - 1. Analysis threads development agencies, private sector, donors, NGOs, farmer associations, national governments data + metadata archive and management decision support dashboard analytics engine (models and trade off analysis + algorithms) analytical outputs decision layer analytical layer other networks and data sources LSMS, AfSIS, FAO, GEO..... remotely sensed + in situ measurement layer 6
  • 7. VITAL SIGNS DECISION INDICATORS CATEGORIES Ecosystems Services Indicator Climate Forcing Net AFOLU Climate Forcing X Biodiversity Biodiversity Security X Wood Fuel Wood fuel Energy Security Livestock Agriculture Human wellbeing Thread X Rangeland degradation X X Forage Adequacy X X Water Water Security X X X Resilience Resilience or buffering index X X X Inclusive Wealth Sustainability index X X X Food Security Food Security Index X X Soil Health Soil Health Index X Ag. Intensification Yield Target (%) X Poverty Poverty X Health Prevalence of malaria, diarrhea, anemia X Nutrition % overweight, under weight, stunting, and wasting X X
  • 8. Thread for Soil Health October 2013 Par al nutrient budget indicator crit vals -20, -5,-20 kg/ha/ crop Net nutrient budget for N, P, K Soil fer lity indicator Soil critical values for Ca, Mg, K, P, S (AfSIS; Shepherd Vagen) Soil Exchangeable, Available Ca, Mg, K, P, S Nutrients (N, P, K) added to farm plots Nutrients (N, P, K) removed from farm plots Soil Health Index Soil Health critical value composites Soil acidity indicator Soil C deficit indicator Soil erosion indicator crit deficit 25% Soil pH critical value of 5.5 Soil pH AfSIS map data crit val 20 t/ha Revised Universal Soil Loss Equation (Rahman et al, 2009) Soil C capacity (Hassink et al., 1997) Soil C – topsoil Soil texture Slope steepness (S) and slope length (L) Soil cover and management (C) land cover Rainfall erosivity (R) Digital elevation model Rain rates
  • 9. Thread for Biodiversity October 2013 Biodiversity Security Biodiversity intactness model (modified from Scholes & Biggs 2005) Red list indicator IUCN rules for threat % Ecosystem protec on % Remaining habitat Habitat suitability Protected area network Land use Thread for Wood Fuel October 2013 Habitat Woodfuel Energy Security Loss of forest area Species richness Niche Models MaxEnt conec vity Leaf forage on Degrada index to livestock thread Supplydemand spp presence Wood produc on natl surveys Potential vegetation From Livestock Tree thread production Species abundance Model (Shackleton & from nat/ sub-nat Scholes) surveys Land cover class Woody biomass Thread for Species Food Security Presence Octoberfrom Plots 2013 Many previous studies in literature Species Abundance Annual from Plots rainfall Tree cover MODIS Tree height ICESAT Food purchased/ Security sold index VS modified algorithm based on EIU Food secrurity index (2012) Household size To climate thread Food availability Colgan et al algorithm Wood Wood consump on % Under Nutrition Thread for Gap Nutrition assesment October 2013 Dietary diversity scor e Dietary intake Composite index of anthropometr FAO.FAN ic failure ** Nutrition as integrative indicator Calories and essential nutrients Tree available per capita height FAO.FAN TA Tree algorithm species (2007) Risk of food waste Household Food Production (from Ag Intensificatio n Thread ) Spatial disaggregation Subjective food availability index Allometry Nickless & Scholes 2011 Tree basal area Food utilization Food access Food sold and purchased per capita per day Household % of Minimum cost Woodfuel of nutritious household Pop% consumption diet income spent ulation on food Overweight DHS Subnat statistics Self reportedNumber of months of food insecurity Per capita consumption* Save the Children (2009) 25 cutoff Food consumption* DHS Subnt stats TA (2007) % Underweight 18.5 cutoff Price of food items on markets -2 SD cutoff 7 day recall data on cuthousehold food off consumption of different food groups Weight for age z-score BMI % Was ng -2 SD -2 SD cutoff % Stun ng Height for age z-score Weight for height z-score *Overlap from the poverty thread WHO (2006) Quetelet’s Index TIER 4 Gender Age WHO (2006) Weight Height WHO(2006) MUAC CIAF – 2 TIERModel developed by Svedberg 2000 used extensively in current literature 125 mm
  • 10. Graphical tradeoff analysis Thread for Sustainable Agricultural Intensification October 2013 Degree of intensifica on Climate index Per yield All crop, all year yield Yield gap From climate thread Biodiversity loss Per yield From biodiv thread Water use Per yield Target yieldRealised yield multiplied Frac on of area under ag land use Input intensity Target yield per crop Realized yield per crop Nutrient use Per yield From nutrient inputs In realized crop yield subthread multiplied Input/ target input Inputs for target yield Farmer inputs Tillage, fertiliser, irrigation, seed, pesticides VS Land cover map MGMT PRACTICES Irrigation, Fertilizer use, Residue, Planting date, Harvest date From water thread SPATIAL WEATHER DATA SET (eg CRU) Temperature, Precipitation, Solar radiation, Humidity DSSAT -CSM Crop model (Koo et al., 2012; Jones et al., 2003) for specific crops Area harvested per season by crop Area harvested per season by crop Yield per hectar e per season by crop Yield per hectare per season by cr op Spatial Disaggregation AFSIS SOIL MAP DATA: Soil type, Soil carbon, Soil water content, Soil Texture Crop Yields (Harvest Choice; FAO; District)
  • 11. Vital Signs Approach - 2. Sampling framework and Measurement scales GLOBAL REGION Facilitating Providing insights comparisons among and information at different regions the scale on which agricultural investment decisions are made Tiers 1 and 2 LANDSCAPE FIELD/PLOT HOUSEHOLD Measuring relationships between agricultural intensifications, ecosystem services and human wellbeing Tiers 3 and 4 Tracking agricultural production, including inputs and outputs Using surveys on health, nutritional status, income and assets
  • 12. Sampling Framework • Tier 1 – simple measures, complete regional coverage at moderate resolution, based on models and remote sensing – Land cover, vegetation type, biomass, modeled NPP – yields • Tier 2A -1 ha plots, in situ detail, statistically valid sample - to validate Tier 1 and measure things not ‘seen’ by RS (250-500 plots sampled; • Tier 2B: 500+ HHs depending on national surveys • Population, disaggregated national statistics • Tier 3 – Flow based, continuous sampling – weather station, hydrological flows • Tier 4 – Process-oriented studies at high resolution– Five to ten 10X10 km landscapes per region – 30-40 households per landscape with associated fields
  • 13. Tanzania SAGCOT development clusters and protected areas
  • 14. Ghana Tier 2a plots and Tier 4 landscapes
  • 15. Ecosystem stocks, functions, services Natural Systems Slash & Burn Agriculture; shortened fallows Degraded Systems Rehabilitation through intensification Bonsaaso, Ghana` Mbola, Tanzania Ruhiira, Uganda Sauri, Kenya Koraro, Ethiopia Time and Population Density Intensive Management
  • 16. Regions of impending agricultural change
  • 17. SAGCOT DEVELOPMENT CLUSTERS AND PROTECTED AREAS

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