laderach p - tools and methodologies to make better decisions
TRANSCRIPT
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Herramientas e informacion para la toma de decisiones
Peter Laderach Decision and Policy Analysis DAPA
Centro Internacional de Agricultura Tropical CIAT
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Contenido• Herramientas y datos que
el CIAT desarrolla para la toma de decisiones
• Ejemplos de Latinoamérica y Honduras– Identificación de nichos de
cultivos– Denominación de Origen– Competitividad – Herramienta de trazabilidad,
control de calidad y mercadeo– Impacto del cambio climático
en cultivos
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CIAT y DAPA CIAT
MISION: Reducir el hambre y la pobreza y mejorar la salud humana en los trópicos mediante una investigación que aumente la eco-eficiencia de la agricultura.
DAPA: Decision and Policy Analysis
Cree firmemente en el poder de la información para diseñar políticas efectivas en desarrollo agrícola y manejo y conservación de los recursos naturales.
Que hacemos:
Análisis, Metodologías, Herramientas y Bases de Datos
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Silvia Elena Castaño Enna Diaz Betancourt
Juan Carlos Andrade
Simone Staiger Ana Milena Guerrero Glenn Graham Hyman
Anton Eitzineger
Lilian Patricia Torres
Carlos Nagles Jorge Cardona
Simon Cook
The Oldies
Andy Jarvis
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Natalia Uribe Robert AndradeJulián Ramirez
Daniel Jimenez Vanesa Herrera
Nora Castañeda
Mike Salazar
Jhon Ocampo
Hector Favio Tobón
The Youth
Louis Reymondin
Ovidio RiveraElizabeth Barona
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Katherin Tehelen
Victor Augusto Lizcano Angelica Ma. Henao Carolina Argote D.
Daniel Amariles
Oriana Carolina Ovalle
And the under-age
Emmanuel Zapata
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Caracterización de sitios con bases de datos de medio-ambiente
• Con un latitud y longitud, que información se puede sacar de bases de datos espaciales de medio-ambiente?
• Clima:– Promedios anuales, WorldClim– Datos diarios actuales, TRMM– Cambio climático
• Topografía– SRTM– Derivados topográficos
• Suelos
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WorldClim• Production of global high resolution 1km monthly climate
surfaces for precipitation, mean, max and min temperature
• Based on 47,554 precipitation stations, 24,542 mean temperature stations, 14,835 minimum and maximum temperature stations
• Interpolated using a thin-plate smoothing spline in the AnuClim software
• Derivatives: bioclimatic variables such as precipitation in dryest quarter, absolute minimum annual temperature, consecutive wet/dry months, seasonality etc.
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A
B
C
~1500 stations in Colombia
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WorldClimClimate stations are not randomly distributed, but most dense in
populated regions.
• Average distance from a CIAT climbing bean collection to a WorldClim station:
• Precipitation : 11.2km
• Mean temperature : 30.7km
• Minimum/maximum temperature : 33.4km
• Average distance from a WCMC cloud forest site to a WorldClim station:
• Precipitation : 20.6km
• Mean temperature : 38.8km
• Minimum/maximum temperature : 52.6km
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No solo datos mensuales• Variables BioClimaticos (Busby):
– BIO1 = Annual Mean Temperature– BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))– BIO3 = Isothermality (P2/P7) (* 100)– BIO4 = Temperature Seasonality (standard deviation *100)– BIO5 = Max Temperature of Warmest Month– BIO6 = Min Temperature of Coldest Month– BIO7 = Temperature Annual Range (P5-P6)– BIO8 = Mean Temperature of Wettest Quarter – BIO9 = Mean Temperature of Driest Quarter– BIO10 = Mean Temperature of Warmest Quarter– BIO11 = Mean Temperature of Coldest Quarter– BIO12 = Annual Precipitation– BIO13 = Precipitation of Wettest Month– BIO14 = Precipitation of Driest Month– BIO15 = Precipitation Seasonality (Coefficient of Variation)– BIO16 = Precipitation of Wettest Quarter– BIO17 = Precipitation of Driest Quarter– BIO18 = Precipitation of Warmest Quarter– BIO19 = Precipitation of Coldest Quarter
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• Indices bioclimáticos (Rivas-Martinez):• Iar Índice de aridez• Ic Índice de continentalidad o intervalo térmico anual (Tmax-Tmin en grados• centígrados)• Id Índice de diurnalidad o intervalo térmico diario (Tcmax-Tcmin en grados• centígrados)• Im Índice de mediterraneidad• Im1 Índice de mediterraneidad del mes de julio en latitud N y del mes de enero en• latitud S• Im2 Índice de mediterraneidad de los meses de julio + agosto en latitud N y de los• meses de enero + febrero en latitud S• Im3 Índice de mediterraneidad de los meses de junio + julio + agosto en latitud N y
de• diciembre + enero + febrero en latitud S• Io Índice ombrotérmico anual (Pp: Tp) 10• Iosmi Índice ombrotérmico semestral, siendo i: 1 = invernal (octubre-marzo), ... , 2 =• estival• Ioti Índice ombrotérmico trimestral, siendo i: 1 = invernal (diciembre-febrero), ... , 4• = otoñal (septiembre-noviembre)• Iom Índice ombrotérmico mensual (Ppi: Tpi) 10• Iod1 Índice ombrotérmico del mes más seco del trimestre más seco del año• Iod2 Índice ombrotérmico del bimestre más seco del trimestre más seco del año• Iod3 Índice ombrotérmico del trimestre más seco del año• IodSS1 Índice ombrotérmico del mes más seco del segundo trimestre del semestre
más• cálido del año• …….
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Datos de lluvia diario derivado de satélites - TRMM
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Time series of rainfall over SE Asia every 3 hours for the 8th Feb. 2001.
A day in the life of TRMM
midnight 3am 6am
midday9am 3pm
9pm6pm
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Que hace la plantilla
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Visualización
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Acumulación de lluvias en el año
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Balance hídrico y estación de crecimiento
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Topografia
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TopographyNow…..
• Shuttle Radar Topography Mission (SRTM) global elevation data
• 3 arc second product (~90 m resolution) and 1 arc second (~30m resolution) though the latter is restricted access
• Vertical precision – < 16 m
• Produced by NASA and USGS, made freely available 2003/2004
Significance:
Local information for entire globe
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Significant Improvement
GTOPO30
SRTM
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Cali rubbish heap Basurero Navarro
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SRTM – Version 3
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Using the data – more than just elevation
Elevation Slope Aspect Landscape Class
MoistureSolar RadiationExposureCurvature
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MAP CLOUD FREQUENCY
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Suelos
1: 5,000,000 FAO Soil Map of the World, Source: FAO
Available at: http://www.fao.org/ag/agl/agll/dsmw.htm
90m Resolution Elevation Data, Source: SRTM, USGS
Available at: http://srtm.csi.cgiar.org
1km Resolution Climate Data, in this case January Rainfall
Source: WorldClim, available at: http://biogeo.berkeley.edu
ClimateTopography
Soil
LandSat 25m resolution land cover, Source: NASA
Available at: https://zulu.ssc.nasa.gov/mrsid/mrsid.pl
Land cover
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Crop Niche Selection for Tropical Agricultre CaNaSTA
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HOMOLOGUE• FIG 1
cv. Hass var. guatemalensis
var. americana
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• Localizando geográficamente nichos de cualquier especie (o de cuantas especies se tengan datos)
Modelación de distribución de especies (MaxEnt)
Distribución completa
Distribución de alta probabilidad
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¿ Qué es MarkSim ?
Es un Software para producir datos climaticos diarios
para cualquier punto en el tropico aun donde no existen datos.
Basado en un proceso de markov de tercer orden
MARKSIMV1.0
Interpolated Climate Surfaces
Latitude,
longitude
Elevation
DAT file
CLX file
DSSAT
v 3.5
FILES (.WTG)
Calendar format
Files (.GEN)
+
++
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CLX files
DAT filesCLI files
WTG files
EL principal objetivo de MarkSim es generar informacion climatica
simulada.
Para ello requiere de un archivo .CLX que sera la entrada al modelo . Este se puede construir de varias formas segun se explican en
el manual pero requiere de unos datos minimos (latitud, longitud) si
no se poseen de datos
climaticos (precipitacion)
Si se poseen datos climaticos (precipicitacion total mes) se
pueden digitar en un archivo .dat a partir del cual markSim genera
el .CLX
El CLX tambien se puede generar a partir de latitud longitud (elevaion
opcional)
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