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_____________________________________________________________________ These appendices provide further details of the analyses carried out under the CPWF Challenge Programme on Water and Food, Basin Focal Project for the Andes and reported in: Mulligan, Rubianoand the BFP Andes team (2009) Andes Basin Focal Project (Andes BFP). Final Report. www.bfpandes.org and www.ambiotek.com/bfpandes Some of these appendices are presented in English and some in Spanish. _____________________________________________________________________

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Appendix A - Water availability analysis (WP2) A.1. Datasets and maps A.1.1. Climate

Rainfall distribution maps (Figure 1) were produced using data from the TRMM Rainfall Climatology (Mulligan, 2006a). Rainfall of >100 mm/month is common on the eastern slopes in the wet season, but recedes northwards in the dry season. The entire southern Andes have little rainfall from March to July, wetting up especially in the east from August to March. Parts of Bolivia, Chile and Argentina are persistently dry, especially in the west.

Figure 1.  Monthly rainfall throughout the Andes emphasizing the low end of observed rainfall values  

A.1.2. Landscape and land use

The Andean basin system covers elevations from sea level to almost 7000 m.a.s.l., based on the SRTM 1 km DEM (Figure 2, left). It is extensively covered by agriculture – both cropland and pasture (Figure 2, center; Ramankutty, 2008). The extent of area covered by protected areas is considerable (Figure 2, center; WDPA, 2007)1. Tree cover is extensive along the eastern flanks across most of the latitudinal range and along the western flanks in Colombia and Ecuador. Bare, un-vegetated surfaces are extensive in Southern Peru, Bolivia, Chile and parts of Argentina (Figure 3). Herbaceous cover is extensive at higher altitudes.

1 The World Database on Protected Areas (WDPA) is a joint product of UNEP and IUCN, prepared by UNEP-WCMC, supported by IUCN WCPA and working with Governments, the Secretariats of MEAs and collaborating NGOs

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Figure 2. Terrain (left), land cover (center), and protected areas (right) for the Andes Basin  

Figure 3. Vegetation cover throughout the Andes as indicated by the MODIS Vegetation Continuous Fields (VCF) product 

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A.2. Regional GIS analyses Regional scale water inputs (rainfall, fog and snowmelt) Inputs of precipitation are the most difficult and spatially variable component of the hydrological cycle to characterize: yet they are the most fundamental. We used two separate precipitation climatologies in order to understand precipitation inputs in the Andean region: (a) the WorldClim ground station based climatology (Hijmans et al., 2005), and (b) the TRMM remotely sensed climatology (Mulligan, 2006a). The two climatologies are in broad agreement and show a similar pattern for mean annual total precipitation (Figure 4), with the highest values recorded in the Chocó region of Colombia, the Caribbean lowlands and the eastern slopes of the Andes from Colombia, through Ecuador to Peru and Bolivia. The driest areas include the Bolivian Altiplano, Chile and Argentina. In general the interpolated WorldClim climatology shows less intense rainfall clusters and smoother spatial transitions compared with TRMM which identified some spatially restricted areas of very high annual rainfall total, sometimes >10,000 mm/yr. There is no simple pattern of rainfall with elevation. Though the maximum rainfall tends to decline with elevation for both climatologies, measured rainfall at a given elevation can be at any level between this maximum and zero. In spite of the broad agreement between the two climatologies, the chart embedded within Figure 4 – which shows a sample of 10,000 points from the two maps – indicates that discrepancies can be of the order of thousands of mm. This uncertainty in basic inputs because of high spatial variability makes the quantification of absolute magnitudes of water input extremely difficult, and thus substantially reduces the precision with which water accounts can be calculated. It is likely that the real incident rainfall is somewhere between the highly spatially detailed TRMM estimate and the ground-measured but highly interpolated WorldClim estimate. Figure 5 (left) shows observed cloud frequencies for 2000-2006 based on the MODIS cloud climatology of Mulligan (2006b). Cloud frequency is clearly greatest (close to 1) in the Amazonian and Pacific flanks of the northern Andes and very rare in parts of the Bolivian Altiplano. These levels of cloud frequency can lead to fog inputs contributing up to 10% of annual water flows, even in wet areas, and up to 50% in areas with high fog frequency and very low rainfall inputs such as the Chilean coast (Figure 5, right). Snowmelt is not assessed at the Andean scale in volume terms as it is a transient storage rather than a distinct input per se. However because snowmelt affects seasonal water yields, it is considered in the smaller-scale modeling system and PSS. Figure 6 shows the extent of snow cover in the southern Andes basin for January and June 2004, based on a MODIS cloud-free composite (MODIS Blue Marble Second Generation, 2004; www.kcl.ac.uk/geodata). Regional scale outputs (evapo-transpiration) Figure 7 indicates the potential evapo-transpiration as calculated by the FIESTA model. Values are greatest in the clear, high-altitude southern Andes and parts of the northern Andean foothills and Pacific coastal zone, with maximum levels of 1500 mm/yr down to 300 mm/yr in the high, cloudy peaks of Colombia and Ecuador.

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Figure 4. Precipitation inputs from WorldClim (left) and TRMM climatology (right)  

Figure 5. Cloud frequency for the Andes, based on the MODIS cloud climatology of Mulligan (2006b) (left) and River flow contributed by fog inputs (%) (right)  

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Figure 6. Snow cover in the southern Andes, January and June, based on MODIS Blue Marble Second Generation 2004 

Figure 7. Potential and actual evapo‐transpiration in the Andes basin  

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Regional scale stores (dams, wetlands, groundwater) For the present study, we used the TDD (Tropical Database of Dams) by Saenz and Mulligan (2009). The area draining into the 174 dams in the Andean region covers approximately 389,190 km2 (Figure 8). The number of dams reported in global and national public and private inventories such as the World Register of Dams (ICOLD, 2003) and the National Inventory of Dams of Venezuela (COVENPRE, 2009) is somewhat fewer (amounting to around 130 dams).

Figure 8. Watersheds of large dams in the Andes, based on the tropical dams database (TDD) and geowiki (www.kcl.ac.uk/geodata)  

Per capita water resources and dry-spots The population distribution in the Andes was modeled using the GPW dataset (CIESIN, 2005), and per capita water balance was calculated on a grid cell basis as water balance per person (Figure 9, left and center). When averaging the per capita water balance by municipality in order to understand the likely location of dry-spots, it can be seen that a number of Municipalities in Chile, Argentina and Bolivia present the least water balance per capita (Figure 9, right). The least water balance per person is found in Antofagasta De La Sierra in the Department of Catamarca (Argentina). A line of water deficit in the Andes can be traced with the main deficit zone stretching along the Peruvian coast and much of the Andes south of Peru, with a few spots of deficit in Ecuador (Figure 10).

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Figure 9. Per capita water availability of a pixel basis (left; log scale from green to red. Pink and purple colors are below minimum shown on legend). Mean per capita water availability per department (right) 

Figure 10. The line of water deficit in the Andes under current conditions (million m3/yr)  

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A.3. Climate change and land use change scenario analysis Land use change impacts The scenario for land use change between pre-agricultural times and current, indicates an increase in runoff of <1% in areas that have undergone deforestation (Figure 11). There are a few small areas (of a few pixels) where a decrease in runoff over the same period has been observed, resulting from reduced fog interception by land uses that have replaced tropical montane cloud forests.

Figure 11. Change in forest cover, water balance, evaporation and runoff due to land use change between pre‐agricultural scenario and current  

Water related impacts from industry and mining To calculate the influence of particular upstream land uses (using mining as an example) on downstream areas, we combined 1 km resolution gridded datasets for rainfall (Hijmans et al., 2005), flow direction (HydroSHEDS, Lehner et al., 2008), protected areas (WDPA, 2007), cropland and pastures (Ramankutty et al., 2008), mines (Hearn et al., 2003), urban areas (CIESIN et al., 2008) and population (CIESIN et al., 2005). For the calculation of downstream impact, all input data were converted to 1 km resolution grids and for each pixel the annual total rainfall (long term mean) was multiplied by a scalar value representing the proportion of the pixel covered by potentially polluting human land uses (referred to as P) and this value is then summed along the flow network from source to mouth to give RfC. The pixel proportions were calculated as:

Point source polluting activities = (mines + urban)

Unprotected agricultural land = (pasture + cropland) * (1-protected)

P = max (1.0, point source polluting activities, unprotected agricultural land)

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Where: Mines = mines according to Hearn et al. (2003) Urban = urban areas according to CIESIN et al. (2004) (binary) Pasture = pasture land according to Ramankutty et al. (2008) Cropland = cropland according to Ramankutty et al. (2008) Protected = nationally or internationally protected areas according to WDPA (2007) (binary).

Note that all binary variables are assumed to occupy the entire 1 km pixel. The rainfall falling on all areas was also calculated and summed along the flow network to give RfA. (P/RfA) * 100 is then the percentage of flow in a given pixel that fell as rain on an upstream human influenced area and is thus a measure of the potential upstream influences on water quality (Figure 12; left). In terms of potential number of people affected, Figure 12 (right) indicates the population of the same urban areas weighted by the proportion of water derived from mining areas and is thus an index of potential impact on people.

Figure 12.  Mean percentage of water accessible to major urban areas derived from mining areas upstream (left). Potential number of people in urban areas affected by water derived from mining areas upstream (right) 

A.4. Modeling

A.4.1. Water Scarcity Model for The Andean System of Basins The “Water Scarcity Model for The Andean System of Basins” (see excel document attached) has been developed to obtain a estimation of the extent of water scarcity in the Ambato, Fúquene and Jequetepeque Andean sub-basins. The model computes the effect of potential factors contributing to water scarcity in three components: contamination, water availability and access. The model can also be used to estimate the cost of water access by gravity supply systems, either for household (domestic) or agricultural use.

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A.4.2. Water Scarcity Spreadsheet To obtain an estimation of the extent of water scarcity, the following variables are required: Contamination Component The parameters included in the Water Quality variable are:

Dissolved Oxygen (DO) Fecal coliform Phosphorus Turbidity Specific conductance

The parameters included in the Solids Concentration variable are:

Total suspended solids (only for irrigation use) Total dissolved solids (only for household use)

Water Availability Component The parameters included in the sectoral water use includes: households, industry, irrigation, service and livestock sectors.

Total superficial water offer (obtained through water balances or as output from the Policy Support System)

Sectoral water use (includes household, industry, irrigation, service and livestock sector demands)

Water Access Component This component only includes the source distance as a variable.

A.4.3. Household and Agriculture Spreadsheets The household and agriculture spreadsheets were designed to generate a water accessibility cost estimation in terms of infrastructure unit. They consist of two parts:

Hydraulic Design Approximation of the hydraulic component design of supply networks. Includes the Darcy-Weisbach equation (see below) which relates the head loss – or pressure loss – due to friction along a given length of pipe to the average velocity of the fluid flow, a dimensionless friction factor (calculated by the Colebrook-White equation, see below), and the average velocity of the water flow. Darcy-Weisbach Equation:

Colebrook - White Equation:

hf: head loss due to friction f: dimensionless friction factor or Darcy friction factor l: length of the pipe d: hydraulic diameter of the pipe (internal diameter) V: average velocity of the fluid flow, equal to the volumetric flow rate per

unit cross-sectional wetted area g: local acceleration due to gravity Ks: roughness height Re: dimensionless Reynolds number v: kinematic viscosity

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Water Accessibility Calculates an approximate cost of access to water through labor costs, infrastructure costs, treatment costs, and indirect costs. The gravity water supply system proposed assumes that the supplier source selection has adequate water availability in terms of quantity and quality. However, the model includes an optional section of estimated costs for treated water, which due to its complexity is only considered if the user has information about this cost (e.g., water treatment plants).

To obtain an estimation of water accessibility cost, the following variables are required:

• Type pipe (only for agriculture spreadsheet) • Flow: corresponds to the flow demanded by the user (design flow), which

is established according to the Daily Maximum Flow Demanded • Nominal diameter: refers to existing commercial diameters (see database

in the model) • SDR: refers to the ratio external diameter/minimum thickness (a

dimensionless number that identifies a class of pressure) • Length: corresponds to the tube length. Can be calculated by sections

(according to the diameters and SDR’s given by the design of the system, and entering the respective length of each section), or standardized (using an average diameter for the total pipe length of the system)

• Labor Cost: corresponds to the average cost for day labor • Exchange: The database model that includes technical specifications and

costs of the pipes was prepared for Colombian pesos. Here the user can enter the current exchange rate to calculate the costs in US dollars.

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Appendix B - Water productivity analysis (WP3) B.1. Regional GIS analysis

Crop per drop agricultural water productivity

In order to estimate (crop per drop) water productivity at the Andean scale, first the spatial patterns in plant production had to be examined. This was achieved by a whole-Andes analysis of plant production based on dry matter production (DMP) calculated from SPOT VGT (1998-2008) and masked to exclude trees, followed by the whole-Andes analysis of production per unit rainfall (crop per drop). Crop productivity for the Andes is shown in Figure 13, with trees (MODIS VCF tree fraction >0.4) masked. The spatial pattern of productivity is clear and this is of both greater relevance and greater reliability than the absolute values of DMP – which on the one hand are not equivalent to yield (yield fractions for most crops are commonly 0.4-0.5), and secondly are subject to contamination by isolated trees and other vegetation that shares occupancy of the 1 km pixels with the crops in question. Averaging crop productivity by elevation indicates that the lowest elevations have the highest productivity (Figure 14, left). When averaged by catchment, Andean catchments in Colombia and Ecuador have the highest crop productivity, along with Eastern foothill catchments in the South (Figure 14, right). On average through the Andes, crop productivity increases when moving downstream from the hill slopes. Comparing crop productivity for some of the Andes basins with satellite imagery (Figure 15) indicates the coherence between high productivity and intensive agriculture (for example of the outskirts of Cajamarca) and low productivity with mining and high elevation (for example in the Jequetepeque and Ambato catchments). Averaging by elevation and catchment (Figure 16) indicates that the lowest elevations have greatest crop per drop and that small, lowland-dominated Pacific and Eastern foothill catchments have greatest crop per drop. Water productivity in the Andes is rather low in comparison with the lowlands of Colombia, Ecuador, Peru and Bolivia as shown in Figure 17 for crops, irrigated crops and pasture.

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Figure 13. Crop productivity in the Andes (mean 1998‐2008)  

Figure 14. Mean crop productivity averaged by elevation (left) and by catchment (right) 

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Figure 15. Crop productivity for the various sub‐catchments compared with LANDSAT and IKONOS imagery  

Figure 16. Crop per drop averaged by elevation and by catchment  

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Figure 17. Crop per drop productivity for dry crops, irrigated crops and pasture in the mountains and lowalnds of Colombia, Ecuador, Peru and Bolivia 

Environmental flows The role of environmental flows from protected areas for sustained provision of water resources (quantity and quality) was illustrated by combining 1 km resolution gridded datasets for rainfall (Hijmans et al., 2005), protected areas (WDPA, 2007), flow direction (HydroSHEDS, Lehner et al., 2008), urban areas (CIESIN et al., 2008), and population (Landscan, LandScanTM Global Population Database, 2007). Annual total rainfall (long term mean) was multiplied by a binary value representing the protected area status in each pixel and summed along the flow network from source to mouth to give RfC. The rainfall falling on all areas was also calculated and summed along the flow network to give RfA. (RfC/RfA) * 100 is then the percentage of flow in a given pixel that fell as rain on an upstream protected area. This is a better measure of an areas influence on downstream flow than a simple calculation of land area since it is rainfall-weighted land area that is important to downstream flow. Figure 18 shows for parts of Colombia and Ecuador in each 1 km pixel the percentage of all accumulated rainfall that originated as rainfall on a protected area. This is thus a measure, for each pixel, of the influence of protected areas on stream flow. Thus, assuming that protected areas provide positive hydrological benefits, this is a measure of the magnitude of these benefits. As can be seen, the influence of protected areas soon diminishes as one moves downstream and flows are dominated by the much more ubiquitous unprotected landscapes.

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Figure 18. Percentage of water derived from protected areas upstream (extract for Colombia/Ecuador)  

Water for urban populations The Andean population is highly urbanized with a concentration of large urban areas in Colombia and, to a lesser extent, throughout the southern Andes (Figure 19, left). These urban areas are associated with significant areas of irrigated agriculture (according to Siebert et al., 2007; Figure 19, center), and the highest recorded GDP (according to CIESIN, 2002; Figure 19, right).

Figure 19. Urbanization (left), irrigation (center) and GDP (right) in the Andes. Pink represents zero, purple close to zero. 

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Figure 20. Urban population consuming water that originated as rain on a protected area (extract for Colombia/Ecuador). Purple represents zero.  

Threats to productivity (climate and land use change) Figure 21 indicates the spatial coincidence of pasture, cropland, urban areas and protected areas in the Andes and indicates a highly impacted system with extensive pasture lands especially in the northern and central Andes and extensive cropland especially in Colombia, Ecuador and northern Peru. Urbanizations are littered throughout the basin but are especially concentrated in the North and are juxtaposed with protected areas throughout the Andes. To estimate the impacts of climate change we are using the results of 17 Global Climate Models (GCM) forced by the SRES A2a scenario, downscaled to 1 km resolution (Ramirez and Jarvis, 2008), and presented as differences between present and the 2050s. The present climate is defined as the average climate between 1950 and 2000, and the 2050s climate as the average climate between 2040 and 2069. The key characteristics of the SRES A2 family of scenarios are “Business-as-Usual” economic growth in industrialized countries, but slow economic growth in less-industrialized countries (IPCC, 2007). It assumes a slowdown in the demographic transition in less-industrialized countries, resulting in a continuous population growth up to some 14 billion in 2100, large gaps in economic prosperity between and within the regions, less valuation of material prosperity, and more emphasis on traditional values. It projects slightly lower GHG emissions than the previous IS92a scenario, but also slightly lower aerosol loadings, such that the warming response differs little from that of the IS92a scenario. CO2 concentrations by 2050 are 779 ppmv, more than double the pre-industrial (1850) level of 330 ppmv. Figure 22 shows the mean temperature and precipitation change for the 17 models for the Andes. Temperatures are expected to increase throughout the basin, but by much more in the northeastern Andes and the high southern Andes. Precipitation is also expected to increase throughout the basin by around 70-100 mm/yr but up to 300 mm/yr in parts of Chile, Argentina and the northwestern Andes.

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However, the actual magnitude of change is highly uncertain for some variables in some areas. Figure 23 shows the standard deviation (SD) in temperature and precipitation change between the 17 models (a measure of their uncertainty). For temperature, the SD is within a degree or less for most of the high Andes but for parts of the northern and eastern Andes is of the order of 2 degree or more (very high considering the magnitude of change expected). For precipitation, SD is less than 250 mm/yr for most of the Andes but peaks to more than 2000 mm/yr in isolated spots where models disagree on large-scale changes in precipitation. The seasonality of change indicates a weak seasonality in precipitation change in the Andes (Figure 24), compared with the strong seasonality in precipitation for the Amazon (not shown). Seasonality in temperature change is considerable however, with much greater change in the central Andes from May-August (Figure 25).

Figure 21. The spatial coincidence of pasture, cropland, urban areas and protected areas for the Andes 

Figure 22. Temperature and precipitation change current to 2050s as the mean of 17 GCMs SRES A2a scenario  

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Figure 23. Standard deviation (uncertainty) of 17 models for temperature and precipitation change in the Andes  

Figure 24. Seasonality of precipitation change expected to the 2050s for the Andes as the mean of 17 GCMs (January to December from top left to bottom right)  

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Figure 25. Seasonality of temperature change (°C) expected to the 2050s for the Andes as the mean of 17 GCMs (January to December from top left to bottom right)  

Figure 26 shows expected change in population density for the Andes according to the GPW dataset (CIESIN, 2005).

Figure 26. Population growth projected for the Andes (1990‐2015) according to the GPW dataset 

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B.2. Modeling

Fisheries Model: Pesquerías en las Cuencas Andinas - Diseño de la ecuación para el cálculo del costo de producción de peces comerciales

En la actualidad la acuicultura se ha consolidado como una actividad de tipo económica, rentable y eficiente, donde además de generar ingresos al productor, se constituye como una fuente alternativa de proteína para la seguridad alimentaria mundial y a su vez como una actividad generadora de empleo e ingresos para los menos favorecidos.

De acuerdo a lo anterior y a la notable disminución de la pesca de captura en el mundo, se ha inducido al crecimiento de la producción acuícola, lo que trae consigo la necesidad de conocer los costos de inversión y producción que esta actividad demanda, al igual que la aceptación del producto, y los factores involucrados en ésta.

El objetivo de este avance es presentar las variables y el planteamiento conceptual en el que se fundamenta cada uno de los costos generados en el diseño de un sistema acuícola, para esto es necesario investigar y profundizar en el tema, lo cual permita conocer esta actividad desde el punto de vista comercial y rentable.

A. Generalidades de la Acuicultura

La FAO (2003) define la acuicultura como el cultivo de organismos acuáticos, incluyendo peces, moluscos, crustáceos y plantas acuáticas, que implica la intervención del hombre en el proceso de cría para aumentar la producción, en operaciones como la siembra, la alimentación, y la protección de depredadores entre otros.

La producción acuícola se clasifica de acuerdo a su densidad y manejo, en sistemas extensivos, semi-extensivos, intensivos y superintensivo.

• Extensivos: Se caracteriza por un grado mínimo de modificación del medio ambiente, existiendo muy poco control sobre el mismo y la calidad y la cantidad de los insumos agregados para estimular, suplementar o reponer la cadena alimenticia.

En algunos casos tiene como fin repoblar o aprovechar un cuerpo de agua determinado. Se realiza en embalses y reservorios, dejando que los peces subsistan de la oferta de alimento natural que se produzca. La densidad de siembra se ubica entre 1–2 peces/m2.

• Semi-intensivos: En este sistema se realiza una modificación significativa sobre el ambiente, se tiene control completo sobre el agua, las especies cultivadas y las especies que se cosechan. Se utilizan fertilizantes para lograr una máxima producción; también puede usarse un alimento suplementario no completo, para complementar la productividad natural sin necesidad de utilizar aireación mecánica.

Generalmente se usan estanques de tierra que se pueden llenar y drenar al gusto del productor. La densidad de siembra final está entre 5 y 10 peces/m2.

• Intensivos: Se ha hecho una modificación sustantiva sobre el medio ambiente, con control completo sobre el agua, especies sembradas y cosechadas; se usa una tasa de siembra grande, que va de 10 a 20 peces/m2, ejerciendo mayor control sobre la calidad de agua (ya sea a través de aireación de emergencia o con recambios diarios) y todo

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nutriente necesario para el crecimiento, que proviene del suministro de un alimento completo y balanceado.

• Superintensivo: Cumple con las condiciones de un sistema intensivo con la diferencia que es a mayor escala de producción. En los estanques deben hacerse recambios diarios de agua, de hasta un 100%/hora; también se utilizan aireadores mecánicos. Los estanques son generalmente de concreto y de tipo “raceways” (canales de corriente rápida, con compuertas controladas para el paso del agua) para que pueda darse un mejor intercambio de agua y una mayor oxigenación.

De acuerdo a la cantidad de especies trabajadas también se clasifica en:

• Monocultivo: Se utiliza una sola especie durante todo el cultivo. • Policultivo: cultivo de dos o más especies en el mismo estanque con el

propósito de aprovechar mejor el espacio y el alimento. • Cultivos integrados: Se fundamenta en el aprovechamiento directo de

excrementos provenientes de actividades ganaderas o desechos agrícolas, para alimentar a los peces de un estanque, existiendo una recirculación de elementos en el medio.

B. Estructura de la Ecuación

La metodología empleada para el diseño del modelo parte de una minuciosa revisión bibliográfica, a partir de la cual fue definida la estructura del modelo:

I. Se estudiaron los principales componentes involucrados en la toma de decisión sobre aptitud acuícola, en este punto se observó que los componentes que definen el medio biofísico (agua y suelo) y social, predominan sobre cualquier otro.

II. Al definir los componentes se sumaron las variables que rigen cada uno de

estos, que en algunos casos corresponden a más de una por componente. III. Una vez definidas las variables se estudiaron los posibles indicadores, los

cuales sirven como medida de la aptitud conferida a partir de ciertos valores. De este modo se generó una base de datos donde se distingue para cada variable el nivel de aptitud de las mismas.

Las siguientes son las variables seleccionadas con sus respectivos indicadores: COMPONENTE SUELO VARIABLE 1: Aptitud del terreno

• INDICADOR 1: Pendiente • INDICADOR 2: Textura

VARIABLE 2: Disposición de áreas libres

• INDICADOR: Cobertura del suelo COMPONENTE SOCIAL VARIABLE 1: Tamaño de la población demandante

• INDICADOR: Densidad poblacional VARIABLE 2: Acceso a mercados potenciales

• INDICADOR: Distancia a áreas urbanas

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Para más detalle sobre la metodología empleada consultar Ramirez (2008). Al momento de calcular los costos de producción de cualquier actividad económica, se hace necesario conocer que elementos son requeridos en el desarrollo de la actividad. En el caso de un sistema acuícola los elementos básicos son las estructuras físicas (estanques), los peces, el alimento o concentrado, la disponibilidad y calidad del agua, y el mantenimiento del sistema, los cuales de acuerdo al tipo de cultivo a implementar varían en tamaño, cantidad y forma, y por consiguiente en costos. El objetivo de la ecuación es calcular el costo total de inversión y producción de peces, en la implementación de un sistema acuícola. Para esto se han estudiado los requerimientos de las especies de peces comerciales en el mercado (tilapia, cachama, trucha) en cuanto a su densidad de siembra, peso de venta y nutrición. La ecuación se ha pensado de tal forma que el usuario escoja y diseñe un sistema de acuerdo a sus necesidades y disponibilidad de área, esto quiere decir que los valores de cantidad y costos son proporcionales a la magnitud del proyecto y a la escala de producción. Desde un punto de vista objetivo en cuanto a la rentabilidad del sistema (ganancia), se ha tomado como referencia los costos básicos de un sistema semi-intensivo, donde no se tiene en cuenta los costos de tecnología avanzada como son las bombas, aireadores y recambios de agua del 100% en periodos de tiempo cortos. Para suplir las necesidades que dicha tecnología proporciona, se han estandarizado algunos factores como son

- Sistema de flujo continuo - Densidad de siembra no superior al límite de peces por m2 establecido - Toma de agua por gravedad

B.1. Costos

Los costos del sistema se encuentran divididos en costos iniciales, costos variables y costos fijos, y la suma de los tres genera el costo total del sistema, a continuación se explica detalladamente cada uno.

Costos Iniciales

Hacen referencia a la inversión que se hace inicialmente en un sistema por razones de construcción y adecuación del lugar. Dentro de los costos iniciales de un sistema acuícola se ubica la construcción de los estanques y adecuación del terreno, además del diseño y construcción del sistema de abastecimiento de agua y sistema de drenaje.

Costos Variables

Corresponde aquellos costos que incluyen una variación en cantidad y calidad de acuerdo al estado del mercado de compra, donde influyen diferentes factores tales como, la variación en la moneda de acuerdo al país, la producción de insumos y las especificaciones solicitadas por el demandante del producto.

Costos Fijos

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Son aquellos costos que se deben pagar sin importar ninguna variación tanto en el mercado de precios como en la generación del producto final. Se ubican dentro de estos costos en un sistema acuícola, la nómina y el pago de los servicios públicos usados en el proceso de producción (opcional).

B.2. Generación de la Ecuación

Para la construcción de la ecuación ya se han estandarizado algunos de los factores que la determinan, a continuación se explica paso a paso como se construyo y las variables adicionales que esto involucra.

Costos Iniciales (CI)

Paso 1. Determinación de las variables

Dentro de los costos iniciales se encuentra la construcción de un estanque con su respectivo sistema de abastecimiento de agua y drenaje. Esto incluye los costos de:

• (Rb): Remoción del volumen de tierra para la construcción del estanque mediante un buldózer

• (M): Materiales para el recubrimiento del estanque con una proporción de 3(arcilla):1(cemento)

• (T): Tubería o canales de acuerdo a la distancia del estanque a la fuente abastecedora

• (C): Construcción (mano de obra)

Paso 2. Costos

CTMRbCI +++= (1)

Donde: • bBRb $*=   (2) 

• )$*()$*3( cCaAM +=   (3) 

• * ciaDisdT tan*$=   (4) 

• jJC $*=   (5) 

*La elección de la tubería depende del caudal de entrada

Table 1. Simbología de las variables en la ecuación de Costos Iniciales. 

Variable Unidad Símbolo (Und) Símbolo (Costo)

Vol. de tierra m3*$Und. B $b

Arcilla (Kg,gr,Ton)*$Und. A $a

Cemento (Kg,gr,Ton)*$Und. C $c

Tubería m*$Und. D $d

Mano de obra No.Jornal*$Und. J $j

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Nota: La letras mayúsculas indican los datos de acuerdo a la unidad de la variable y las minúsculas el costo por unidad como se muestra en la tabla 1.

Reemplazando las ecuaciones 2, 3, 4 y 5 en la 1, de esto se tiene:

( ) ( )( ) ( ) )$*($*$*$*3$* jJdDcCaAbBCI ++++=  

Costos Variables (CV)

Paso 1. Determinación de las variables

Dentro de los costos variables se incluye, la adecuación de los componentes físicos, químicos y biológicos, del estanque y el agua para la siembra exitosa de la semilla, se calcula para el ciclo de vida del pez en condiciones de producción, el cual oscila entre seis y ocho meses. Los costos variables corresponden a:

• (A1): Peces para producción (alevinos)

• (Co): Alimento a suministrar (concentrado)

• (AF): Abono o fertilizante para el crecimiento de plancton y fitoplancton, como alimento adicional para los peces. Se aplica una vez al mes.

• (Cal): El componente óxido de calcio necesario para estabilizar el pH del agua antes de realizar la siembra

Paso 2. Costos

CalAFCoAlCV +++= (6)

Donde

• ( )[ ]

eEALeAl

eAl

$**25,1$*siembra de Densidad*25,1

$*siembra de Densidad0,25*siembra de Densidad

==

+= (7)

La ecuación (7) calcula el número total de peces a comprar conociendo el porcentaje de mortalidad de los mismos, esto para adquirir el excedente de mortalidad y finalmente se obtenga una densidad de siembra optima. Este porcentaje se obtiene de sumar al porcentaje de mortalidad del pez que en promedio para las especies nombradas corresponde a un 20%, un 5% adicional.

• ( )

fECofCo

$*748*$*748*siembra de Densidad

==

(8)

El valor 748 en la ecuación (2) se obtiene de multiplicar el peso promedio del pez por la ración alimenticia (Table 2) para un ciclo de 180 días, lo cual indica que un pez consume 748 g de alimento en ese tiempo, alcanzando un peso final de 400 g.

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Table 2. Ración alimenticia para  nutrición de peces. 

Fuente: Modificado de Saveedra (2006)

•

( )

cpmgP

GAF

gO

OPAF

*$**25,1

$*teFertilizan P %

estanque Área*25,1

52

52

=

= (9)

•

10$*

$*1000

100*)(m estanque Área2

2

hGCal

hm

KgcalCal

=

= (10)

Table 3. Simbología de las variables en la ecuación de costos variables. 

Variable Unidad Símbolo (Und) Símbolo (Costo)

Densidad de siembra (pez*m2)*$Und. E $e

Alimento (Kg*$Und)*ciclo - $f

Abono (Kg*$Und)*ciclo - $g

Área del estanque m2 G -

% de P2O5 en el abono % P2O5 P -

Cal Kg*$Und. - $h

Ciclo del pez por mes Mes cpm -

Reemplazando las ecuaciones 7, 8, 9 y 10, tenemos que:

( ) ( )[ ] ⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛++=

10$*$*25,1*$*748$*25,1* h

PcpmgGfeECV

Peso Promedio del Pez(g) Ración Alimenticia (%)

‹ 10 5

10 - 25 4,5

25 - 75 3,4

75 - 150 3

150 - 250 2,5

250 - 400 2

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Costos Fijos (CF)

Paso 1. Determinación de las variables

Se incluyen dentro de estos costos las labores de mantenimiento, aseo y restauración del lugar, para un tiempo determinado por lo general se calcula por ciclo de pez para obtener el costo por ciclo.

• Nómina por el número de trabajadores (N)

Paso 2. Costos

NCF = (11)

Donde

• cpmjJN

zCiclodelpeSMLVesTrabajadorNoN**

**..==

   (12)

(SMLV: Salario mínimo legal vigente)

Table 4. Simbología de las variables en la ecuación de costos fijos. 

Variable Unidad Símbolo (Und) Símbolo (Costo)

No. de trabajadores Personas*$SMLV J $j

Reemplazando las ecuaciones 12 en 11, se tiene que:

( )cpjJCF *$*=

B.3. Ecuaciones Finales

Inversión Total (IT)

Corresponde a la suma de todos los costos antes explicados.

CFCVCIIT ++=  

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( ) ( )( ) ( )[ ] ( ) ( )[ ] ( )[ ]cpjJhP

cpmgGfeEjJdDcCaAbBIT *$*10$*$*25,1*$*748$*25,1*)$*($*$*$*3$* +⎥

⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛+++++++=

 

Costo del Ciclo (CC)

Hace referencia a los costos del Sistema Acuícola luego del cumplimiento del primer ciclo, lo que determina realmente el costo de producción de los peces cultivados por unidad.

CFCVCC +=  

( ) ( )[ ] ( )[ ]cpjJhP

cpmgGfeECC *$*10$*$*25,1*$*748$*25,1* +⎥

⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛++=

Costo de Producción (CP)

Corresponde al costo de producción de cada pez, se obtiene de la relación entre los costos del ciclo y la densidad de siembra.

( )E

CFCVCP +=  

( ) ( )[ ] ( )[ ]

E

cpjJhP

cpmgGfeECP

*$*10$*$*25,1*$*748$*25,1* +⎥

⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛++

=

Ganancia (Ga)

Es el valor obtenido de la diferencia entre la venta de los peces producidos, menos el costo de producción del total de la siembra.

( ) CCECPGa −= *  

C. Elaboración del mapa de aptitud acuícola

A partir del cual se construye el mapa de aptitud de la Región Andina, y los datos requeridos por cada indicador, se puso en marcha la metodología planteada, donde se reclasifican los datos usados de acuerdo a una escala de aptitud común de 1 a 5 donde el valor uno corresponde a No Apto y cinco a Muy Apto. La metodología usada es llamada Weighted Overlay, ésta corresponde a una técnica para la aplicación de una escala de valores común creando un análisis integrado. Esta herramienta luego de reclasificar los valores de las capas de datos de entrada dentro de una escala de evaluación común (aptitud o preferencia) y asignando un porcentaje de influencia, realiza una suma ponderada de los valores de dichas capas obteniendo una capa de salida con la misma escala de valores asignada, que para este caso corresponde a la clasificación de la aptitud acuícola del terreno en estudio. Con el fin de evitar errores al modelo fue necesario definir algunas de las restricciones que imposibilitan la construcción de sistemas

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acuícolas en un área seleccionada; éstas con un único valor fueron cuerpos de agua y área urbana. Con la finalidad de escoger el mejor porcentaje de influencia para definir de manera acertada la ecuación final del modelo, se usaron diferentes escenarios, los cuales entrarían en el proceso de validación definiendo así el más conveniente. A partir de una base de datos construida por la Corporación Colombiana Internacional (CCI) resultado de la encuesta de producción acuícola realizada en los departamentos de Antioquia, Huila, Tolima, y Valle del Cauca en el año 2008, fue posible realizar la validación del modelo representado en el mapa de aptitud acuícola. La metodología seguida consistió en sobreponer los puntos (coordenadas) que representaron sistemas acuícola correspondientes a cada departamento sobre los mapa de aptitud construidos con base en los diferentes escenarios, observando en qué nivel de aptitud se ubican. Cabe resaltar que se parte del supuesto de que los puntos acuícolas existentes se ubican en áreas aptas, lo cual respalda su existencia, desconociendo si sus condiciones de ubicación son las más óptimas y la producción la deseada, factores que definen las variables empleadas en el modelo.

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Appendix C - Institutional analysis (WP4) C.1. Water rights and tenure It is common to observe that the concept of water rights is understood from the perspective of legal frameworks. However researchers such as Boelens and Zwarteveen (2003), Beccar et al. (2002), Bruns et al. (2005), Roth and Nkonya (2008) have exposed that water rights are beyond the formal laws and written documents approved by the state. In the irrigation sector most of the water rights are built in the routine life and they are reflected in everyday water management practices (Beccar et al., 2002). Mollingan (2003) defines management as almost synonymous of control. Nevertheless, management refers to manipulating, handling or maneuvering persons or objects towards an objective, while control involves more the idea of authority, guidance to obtain desirable results or behaviors through three dimensions: technical, organizational, and socio –economic and political control. Boelens and Zwarteveen (2002) state that water rights and water control are defined by the Socio-legal dimension, which constitutes the social agreements to claim for water right and have access of the resource, which result in an act of legitimacy. In addition, the right to decide who should participate in the decisions making process and who should be excluded is part of this dimension. In this sense, the right to use and access water (quality, duration, timing and place of acquisition) is up to social relations, power structure, control and authority (Bruns et al., 2005). Thus, water rights involve rights to control, rights on decision making, rights to establish authority and “rights to exercise local constructions of hydraulic identity and culture water practices” (Beccar et al., 2002 in Bruns et al., 2005). According to such conceptualization, the RUT irrigation district is being studied from September 2009 up to now. RUT alludes to the municipalities of Roldanillo, la Union and Toro, they are located within the Cauca Valley in the South-west of Colombia. This area covers 10.200 has and its altitude varies from 915 and 980 msl (mean sea level). This system works also as a flood protection and it operates through three pumping stations. In this way water is distributed along two main canals. The first one is known as the interceptor canal, runs along the western mountain range and it has 31 km. The second one is called Marginal canal has 44 km that runs along the protection dike (see more Urritia, 2006). The irrigation activities depends highly on the pumped motors which takes water directly from the canals and the water delivered by gravity though the hydraulics gates. The construction of the district was attached to the conceptions of land improvements and water regulation since this area was susceptible to floods. From 1958 up to 1970 the construction was concluded through the agreements between a North American company called Lilienthal and the government. At the first stage the district was foreseen for small farmers according to agrarian reform law (Urrutia, 2006). Despite this fact, there was little users’ participation during the design and construction of the unit district. Users were seen merely as a part of technical consideration. Hence users never felt the construction of the irrigation unit district as their own project; it was more conceived as a part of the State interests (Univalle, 2005). At that time water rights were considered as a farmers’ irrigation requirements and the authorized demands to use part of the water flowing through the canals were given by the government authority (CVC, then INCORA and after HIMAT).

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Irrigation requirements depended on a series of obligations that farmers had to fallow like the presentation of a crop schedule starting the year, they had to pay for the water used, they had to ask for the water needed 48 hours before and all the canals located close to their fields had to be in a good condition. In addition, there were strict sanctions for those who try to manipulate the hydraulic infrastructure without authorization or try to get more water than the fixed before. For these kinds of cases farmers lost their water rights by stopping irrigation periods to their plots. Generally these periods were not longer than one day. In 1990 the policy of Irrigation Management Transfer (IMT) was implemented as a part of the economic liberalization strategy of the government (Restrepo and Vermillon, 1998). This process has involved the devolution of authority and responsibilities from the government to water users. As a result of this process, numerous changes have occurred: new ways of self-organization, rules, authority and land occupation. These changes have shaped new patterns of water distribution and water rights. However, not only the process of IMT has influenced the water management practices in the RUT. Agricultural policy reforms, policies of market liberalization and migration process have had a big impact on the operation of the irrigation district and the distribution of water rights. During the same decade of the IMT, other neoliberal policies allowed to import crops to get into the market with lower prices generating an unequal competence with the national products. As a result of these policies, the traditional crops cultivated in the RUT (cotton, sorghum, maize and soybean) declined. Moreover, new social groups arrived to the area; they were drug traffickers which draw upon the situation to purchase lands to a high price and forced farmers to sell their lands. It brought that, many small and middle farmers left their fields and moved to the urban areas and few actors started to accumulate big areas of lands. According to various interviews, local dwellers have stated that the agrarian reform in the 60’s benefited 1287 families through the redistribution of lands. Nowadays, due to “narcotráfico” and policies of market liberalization, 500 families remain in the RUT. The changes of land size properties in the RUT and agriculture, introduced the sugar cane crop, which have had a high increase after the IMT. In 1993, 63.00 has were occupied by this crop, currently 3,459 has out of 10,200 has, are cultivated by sugar cane. In this manner its water demands are too high in comparison to other crops. For instance, some irrigation requirements showed that in 35 ha, maize crops demanded 2’016,000 liters/s, fruit trees 3’312,000 liters/s, and sugar cane 13’176,000 liters/s. The capacity and design of RUT irrigation system mismatch with the high water needed for the sugar cane and the water delivered through the canals which is not enough for all the water users. This fact worsens with technical aspects of the infrastructure and dry seasons unexpected because climate change. The interceptor canal is a representative sample of such situation, where the water delivered is unfair; since the users in the upper sections of the channel receive more water than needed, whereas the ones in the lower section cannot irrigate their plots. As a solution, drainage canals are used to irrigate plots, some people extract water under the ground and other water users claims their water rights to the water user association (ASORUT) that is in charge of the operation system. Unfortunately ASORUT does not work as a real association due to lack of people’s participation. For this reason, the board of directors in ASORUT is formed by larger farmers some of them sugar cane producers. In this way, the decision making process is addressed to the interest of their representatives.

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ASORUT works as an enterprise with the aim to sell water and satisfies water needs of all users. Therefore water rights after the IMT have been conceived as the right to receive water when the payment is done. Most of the obligations and rules that were established before disappeared. For instance, there is not any crop schedule in the beginning of the year, each farmer cultivate as they want and according to product prices. In addition, some farmers manipulate water infrastructure, others can have access to water without previous authorization. In this context, ASORUT has not the enough authority to impose strict rules or sanctions since the presence of coercive groups associated to the “narcotráfico” phenomenon. Besides, ASORUT has not the ownership authority to act under legal frameworks; it just has the administrative responsibility of the irrigation system. Nevertheless, the irrigation inspectors are important actors, they are constantly checking how the water management practices are carried out in the field, they distribute water along the canals, they reported amount of water used and allocated and they interact directly with the water users. In this sense, it might be said that water rights can not be only conceived through the payment on time since they also are shaped by the social relations between water users and irrigation inspectors. Irrigation inspectors are old dwellers in the RUT, they stated that their work depend on how they treat water users. Hence, they need to understand idiosyncrasies’ people to control water distribution. The inspectors’ role determines the total water sold each month and this money is used to cover operation and maintenance cost of ASORUT. The instability of prices respect to agricultural products and the exclusion of people during the design and construction of the irrigation system have had a big influence on how people respond to water management and use. For instance small farmers are not willing to pay water irrigation tariffs and cannot maintain their canals in a good condition, most of them have a big debt with ASORUT and others make a big effort to do their payment with delay or others just take water in a fraudulent way. The last situation affects the water rights of other users who used to request water previously but cannot get it on time, even though these actions are also observed among middle and large farmers. The last group has used to break canals infrastructure to increase the amount of water running to their plots, especially in the case of sugar cane crops or others have built new infrastructure with authorization in order to catch more water. In conclusion water rights in the RUT system after the IMT depends on social relationships between irrigation inspectors and farmers (water users), access to technology to extract water, agriculture cost efficiency, plot location along the canals, access to under ground water, payments of water tariffs and farmer’s water use behaviors. This depends on household allocation decision on labor, time, and the profitability of irrigated agriculture. Moreover, it is evidence that land occupation process, historical aspects of the RUT construction and social migration events have shaped new ways to distribute water and define water rights in the RUT irrigation system.

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C.2. Primary data

C.2.1. Organizational analysis (Institutional Perceptions)

Workshop respondents were asked to qualitatively assess organizations’ performance on seven criteria commonly used to measure the performance of private sector firms (representativity of interests; respect and credibility; confidence; mission clarity; communication and information; follow laws and rules; cohesion of group). The 15 categories of organizations ranged from local to national level entities with activities and interests in the Fúquene basin. Results of the perception analysis of organizational performance were diverse (Table 5), not only across organizations (see range of average scores) but also amongst the performance criteria within organizations (see standard deviations). Table 5. Perceptions of organizational performance in the Fúquene focus sub‐basin (Source: Fúquene workshop WP4) 

Organization Represen-tativity of Interests

Respect, Credibility

Confi-dence

Mission Clarity

Commu-nication,

Infor- mation

Follow Laws and

Rules

Group cohesion

Average Standard Deviation

NGOs 28 17 31 26 29 26 27 27.8 2.2

Research / Education 24 27 27 27 24 28 26 26.4 1.5

Municipal government 21 21 19 24 23 21 20 21.4 2.1

State government 23 19 19 22 18 21 18 19.6 1.8

National government 21 20 17 19 20 21 19 19.2 1.5

Church 22 22 17 23 15 17 20 18.4 3.1

Health 22 19 17 21 16 20 15 17.8 2.6

Cattle ranchers 23 23 19 16 20 14 14 16.6 2.8

Merchants 20 22 22 16 16 14 16 16.8 3.0

State environmental 17 13 15 19 17 18 15 16.8 1.8

Tourism 21 20 20 14 16 14 15 15.8 2.5

Farmer 19 23 21 13 20 11 12 15.4 4.7

Agribusiness 15 16 10 15 8 10 11 10.8 2.6

Mineral 18 10 9 13 10 11 11 10.8 1.5

Fish 12 14 13 8 9 7 13 10.0 2.8

Average 20.4 19.1 18.4 18.4 17.4 16.9 16.8

Std Deviation 3.9 4.4 5.7 5.4 5.7 6.0 4.9

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C.2.2. Institutional Needs and Perspectives

A survey was carried out to provide a clearer picture of the current needs and perspectives of actors working in and researching key sectors related to development processes within the Andes. Overall the questionnaire received 80 responses with a higher response rate from Spanish speakers (65%). Of these respondents 46% defined themselves as development workers, 26% as scientists, 21% as students, 13% as students and 9% as public sector employees. Eighty-three percent of respondents defined their level of knowledge related to computer based policy support as low or medium. The majority of respondents who did have experience with Policy Support Systems were either in the capacity as a consultant (27%) or for research purposes (39%). Thirty percent described themselves as having no experience at all. One important objective of the questionnaire was to ensure the relevance of the policy options within the system. Nineteen percent of respondents attributed a lack of relevant content within computer based policy support systems as a factor in their low uptake, an additional 9% considered a good range of policy options as an important component in a successful use of them. Therefore we included questions regarding the priorities of policy makers in Andean watersheds in order to gain the perspective of the wide range of respondents who completed the questionnaire. Most respondents agreed that one of the highest priorities in Andean watersheds is soil erosion, with 71% of respondents saying that any future policy support system should include a policy option regarding implementation of soil conservation measures, 44% said that the effects of soil erosion on agricultural livelihoods should be considered more in the policy making process and 55% agreed that indeed this is a high priority for policy makers. Another theme that emerged from the questionnaire was reform in the institutional approach regarding the management of water resources with 48% agreeing that this should be included as a policy option in any future computer based policy support system. The availability and quality of water were signaled as important policy priorities, 66% of respondents observed that equality of access to water resources are of high importance within watersheds and this is a high priority for policy makers (65%). In other comments many respondents called for a more participatory and integrative policy making process both between different policy making institutions and between local communities and policy makers. The implementation of Payment for Environmental services which 58% of respondents said should be included in a future PSS, are an effective way to improve institutional water management and increase co-operation between different water users and institutions. Regarding the existing methods of policy making within Andean watersheds the majority of respondents agree that the lack of availability of quality data is a key influential factor that affects decision-making processes. 45% of respondents agreed that data is not currently used when making decisions, that the low levels of usage of existing computer based policy support is due to a lack of data (44%) and that the most important factor in the successful use of any future computer based policy support system would be the availability of reliable data (43%). However, 33% of respondents agreed that spatial analysis and modeling tools are also being used to trigger the generation and use of reliable data. The level of detail and geographic resolution required to be effective were also singled out as a factor for success in the use of computer based policy support (23%). Regarding the level of required detail for a range of potential users, the

36

watershed level up to the national level were generally considered the most important. However, transferring this level of detail into the PSS must be done in a way that ensures accessibility to all levels of users. Low levels of staff training/ capacity (35%) and poor quality of computer equipment (24%) in governance institutions were chosen as important reasons for a low uptake of computer based policy support within the region. Considering that national public sector development agencies (46%) and local municipal planning offices (48%) were selected as the sectors most likely to benefit from the PSS, this signals the need for an effective strategy of knowledge sharing that takes into consideration the technical capacities of all potential users. However the issue of low technical capacity within governance institutions can also be approached with the collaboration of other sectors that were also chosen as sectors likely to benefit from the systems use such as NGOs engaged with rural development (40%) and the academic sector (38%).

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C.3. Organizational analysis in the four Andes BFP countries An institutional database was assembled for the found Andes BFP countries, using a matrix with the following categories: Name of the institution, brief description, webpage, email, address, telephone, contact person, function. The database is publicly available at: http://webpc.ciat.cgiar.org/impacto/Datos/Institucional.htm. A continuación se presenta el esquema institucional presente en cada uno de los países de la región Andina estudiados: Colombia, Ecuador, Perú y Bolivia. Colombia Para la gestión del recurso hídrico se cuenta con diferentes instituciones de orden Nacional, Regional y Municipal. Dentro de esta estructura existe el SINA, Sistema Nacional Ambiental, que tiene como objetivo promover la coordinación institucional y el flujo de información entre instituciones de orden nacional y regional. La responsabilidad ejecutora de la gestión ambiental son las Autoridades Ambientales Competentes (AAC), dentro de las cuales se encuentra el grupo de las Corporaciones Autónomas Regionales (CAR). Aquí se cuenta con diferentes instrumentos de planificación, ordenamiento territorial y económicos, entre los que se destacan: planes de desarrollo, Consejo Nacional de Planificación Económica y Social (CONPES), planes de ordenamiento territorial (POT), planes de ordenamiento de cuencas hidrográficas (POMCHS) e instrumentos de orden económico como las tasas retributivas y licencias ambientales. Sin embargo, gran parte de la normatividad se encuentra focalizada en temas de cobertura de agua potable, sin abordar la gestión integral del recurso hídrico. En la política y gestión del agua no se involucran sectores de alto impacto como el sector agrícola, de energía y minas. Los instrumentos de planificación no están articulados, ni las instituciones encargadas de los procesos de planificación. Es un país que cuenta con una amplia legislación ambiental siendo una de las más extensas en Latinoamérica. En la figura 1, se presenta el esquema institucional de Colombia, relacionado con la definición de políticas y la gestión del recurso hídrico.

Figure 27. Esquema general de las instituciones relacionadas con la generación de políticas y la gestión del recurso hídrico en Colombia. 

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Ecuador El modelo organizacional en Ecuador, establece un sistema descentralizado de gestión ambiental, que involucra varias instituciones, con competencias y dependencias de la gestión ambiental del recurso hídrico. Varios ministerios se organizaron a partir de 2008, en el Ministerio de Coordinación donde se encuentra el Ministerio Ambiental. Se creó la Secretaria Nacional del Agua (SENAGUA), como ente principal de gestión del agua, el cual fusiona al Consejo Nacional Ambiental y al Instituto Nacional de Recursos Hídricos, pero en su creación, no se especifica el mecanismo de coordinación intersectorial y que jerarquía tiene frente a otras instituciones de gestión del agua. A nivel Regional se encuentran los Consejos Provinciales y Municipales, con autonomía propia en la gestión del agua. Así mismo, existen las Corporaciones Regionales de Desarrollo (CRD) que son entidades de gestión de aspectos relacionados con los recursos hídricos, las cuales están actualmente en proceso de reestructuración. En este país, el sistema institucional para la gestión de los recursos hídricos es muy complejo y presenta una gran inestabilidad por las múltiples reformas al sistema organizacional. Se presenta alta dispersión de la gestión del agua con ambigüedad, duplicidad de funciones y competencias, sin claros mecanismos de coordinación intersectorial o de asignación de responsabilidades. En la figura 2, se presenta el esquema institucional de Ecuador, relacionado con la definición de políticas y la gestión del recurso hídrico.

Figure 28. Esquema general de las instituciones relacionadas con la generación de políticas y la gestión del recurso hídrico en Ecuador.

Peru El modelo organizacional en la gestión de los recursos hídricos en Perú, involucra a organizaciones descentralizadas a nivel Nacional, Regional y Local. En el 2008, se crea el Ministerio del Ambiente, que incorpora el Consejo Nacional del Ambiente (CONAM) y la Intendencia de Áreas Naturales Protegidas (INRENA). A nivel Ministerial, la gestión del agua se encuentra dentro del Ministerio de Agricultura, al cual se encuentra adscrito la Autoridad Nacional del Agua (ANA), sin embargo esta gestión está en gran medida enfocada al uso del agua para riego. Aquí las constantes estructuraciones han generado una institucionalidad débil para la gestión del agua. No existe un sistema de políticas que armonice la gestión de los recursos hídricos. En la figura 3, se presenta el esquema institucional de Perú, relacionado con la definición de políticas y la gestión del recurso hídrico.

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Figure 29. Esquema general de las instituciones relacionadas con la generación de políticas y la gestión del recurso hídrico en Perú.

Bolivia En Bolivia, hasta el año 2008 la estructura institucional para la gestión del ambiente, estaba representado por el Ministerio de Desarrollo Sostenible (MDS), el cual fue fraccionado en tres ministerios: planificación del desarrollo, desarrollo rural, agropecuario y medio ambiente y el Ministerio del Agua. Este se estructura en tres Viceministerios: cuencas y recursos hídricos, riego y servicios básicos. Aun no se cuenta con mecanismos claros de coordinación con otros ministerios, ni la operación propia de este ministerio. Aquí las Organizaciones No Gubernamentales (ONG) tanto internacionales como nacionales, tienen una alta injerencia sobre las políticas, programas y proyectos de la gestión del agua, generando una alta dependencia de ayuda y cooperación internacional, lo que resta autonomía en la toma de decisiones. En la figura 4, se presenta el esquema institucional de Bolivia, relacionado con la definición de políticas y la gestión del recurso hídrico.

Figure 30. Esquema general de las instituciones relacionadas con la generación de políticas y la gestión del recurso hídrico en Bolivia.

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C.4. Institutional Environment Index (IEI) Inicialmente, el índice ha sido concebido para orientar y estimar el impacto de las intervenciones de tipo tecnológico o de innovación institucional, este sirve a su vez para conocer lo que sucede en cada localidad más allá de las normas y regulaciones que regulan el accionar institucional. De manera similar, estudios previos dirigidos a orientar inversiones estratégicas en infraestructura han considerado los componentes económicos, organización política y ambiente institucional como los determinantes claves en el éxito de acciones de desarrollo (Eggertsson, 1996; Williamson, 1993, citado por Saleth and Dinar, 2004). Varios supuestos han sido la base para el calculo del índice de ambiente institucional a saber:

• Condiciones de pobreza son asociadas a un pobre desarrollo de la capacidad institucional local y nacional para responder a las necesidades de la población.

• Bajos niveles de educación, salud y acceso a servicios básicos son también considerados como reflejo de un pobre desarrollo de la capacidad institucional para implementar acciones de desarrollo.

• Bajos niveles de inversión por parte del gobierno central en cada municipalidad inciden en un pobre desarrollo de la infraestructura y capacidades para atender intervenciones.

• La existencia de conflicto social representado en desplazamiento, guerra o violencia desmedida reflejan limitaciones en la institucionalidad del estado para garantizar la vida de la población.

El método utilizado para la elaboración del índice de ambiente institucional utilizo herramientas de tipo estadístico como análisis de componentes principales y agrupaciones. Dado que las variables utilizadas en los países considerados no fueron en todos los casos las mismas, el índice obtenido fue estandarizado para ofrecer una representación de dicha característica a nivel regional. El modelo generado para la derivación del índice en cada país se presenta en su formulación más detallada así como de manera simplificada permitiendo a los agentes de desarrollo calcularlo sin necesidad de contar con todo el conjunto de datos. Dichos modelos harán parte del sistema de apoyo a políticas desarrollado por el BFP Andes como un filtro que determina la viabilidad o no de una intervención externa. El método utilizado para la elaboración del índice de ambiente institucional utilizo herramientas de tipo estadístico como análisis de componentes principales y agrupaciones. Dado que las variables utilizadas en los países considerados no fueron en todos los casos las mismas, el índice obtenido fue estandarizado con los valores obtenidos para Bolivia y excluyendo las ciudades capitales (primarias y secundarias) para ofrecer una representación de dicha característica a nivel regional con un enfoque en lo rural. Los pasos llevados a cabo para la obtención del índice de ambiente institucional fueron:

i. Selección de las variables ii. Colecta de información de diferentes fuentes iii. Organización y depuración de la base de datos iv. Estimación de datos perdidos v. Análisis de regresión múltiple vi. Análisis de agrupaciones y componentes principales, y vii. Estandarización de los índices de cada país y representación espacial.

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Las variables consideradas incluyeron: Sociales: Indicadores de Pobreza (NBI y líneas de pobreza), estado actual

de un variado numero de indicadores del sector de educación (estudiantes matriculados, alfabetismo, nivel de escolaridad, etc.), Salud (desnutrición crónica y total), aspectos demográficos, infraestructura de servicios públicos (acceso a agua, electricidad, educación), inversiones sociales y no sociales de origen central (agua potable, saneamiento e irrigación).

Económicas: Consumo por cápita, poder adquisitivo, apoyo financiero. Políticas: Población desplazada por la violencia.

La búsqueda de estas variables se realizó en bases de datos de oficinas estadísticas de cada país, ministerios y otras fuentes certificadas. Los datos fueron organizados en una base de datos jerárquica usando como llaves las diferentes unidades administrativas usadas en cada país (Departamento, Provincia, Cantón, Sección, Municipio). Cada variable fue objeto de revisión para asegurar la consistencia en valores y unidades. Algunas de estas fueron normalizadas utilizando la población total de cada municipio. Posteriormente se estimaron los valores perdidos utilizando variables independientes altamente correlacionadas por medio de la técnica Regresión por Etapas. De manera paralela se construyo una matriz para identificar variables redundantes que luego de su exclusión permitieron contar con un modelo más robusto y simplificado. Cuando no se contó con variables asociadas se utilizo el promedio de la unidad administrativa mayor a la cual la municipalidad pertenecía. Variables con un número de datos faltantes mayor al 70% fueron eliminadas del análisis. Análisis de componentes principales (PCA) fue utilizado para reducir el número de variables y así simplificar las dimensiones representadas por el conjunto de variables. Para explicar mejor el significado de cada componente, se realizó un análisis de agrupaciones el cual permitió identificar las municipalidades que poseían características similares de las utilizadas en el PCA. Durante este proceso se utilizaron pruebas asociadas a cada una de estas técnicas que permitieron discriminar la inclusión o no de variables y el número de agrupaciones más adecuado con el cual es posible describir la población (Milligan and Cooper, 1984). El índice fue calculado con los valores de los componentes asociados a cada variable y ponderados para cada agrupación. Una vez calculado el índice para cada municipalidad, éste fue llevado a la base geográfica para su representación espacial. A continuación se presenta a manera de ejemplo el modelo generado para la derivación del índice en Ecuador en su formulación más simple donde los valores de los coeficientes fueron aproximados a la unidad: Índice de Ambiente Institucional (IAI) - Ecuador = ∑ (2(A+B)+C+D+E)/5

A = Tasa de Analfabetismo B = Necesidades Básicas Insatisfechas NBI del último censo C = Desnutrición global en niños menores de 5 años D = Porcentaje de población por debajo de la línea de pobreza E = Porcentaje de población por debajo de la línea de extrema pobreza.

Cinco de doce variables utilizadas fueron las determinantes en el modelo. Los dos primeros componentes del PCA explicaron 83% del total de los datos. Al evaluar los valores de algunas de las variables (Figure 31, Figure 32) frente a los valores del IAI es posible evidenciar que entre mayor el valor del índice mayores problemas de analfabetismo y de NBI se encuentran en los municipios del Ecuador.

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Figure 31. Relación entre los valores del índice de ambiente institucional y el % de analfabetismo en Ecuador 

Figure 32. Relación entre los valores del índice de ambiente institucional y el % de NBI en Ecuador 

Al representar el valor del índice de manera espacial es posible notar que alrededor de los centros urbanos el índice adquiere valores muy superiores a una heterogénea situación en la zona rural (vea Figura 26 del reporte principal). Estos corresponden con la expansión de los centros urbanos debido a la inmigración de población de escasos recursos. Igualmente, zonas con bajos niveles de acceso como la costa pacífica Colombiana, la Amazonia Peruana y la Sierra del Ecuador y Perú presentan los valores más altos del índice, correspondiendo estas áreas con aquellas donde se concentra la pobreza, hacer menos presencia el estado y presenta déficits en la prestación de servicios públicos como salud y educación. Esta información es de particular importancia para aquellas organizaciones interesadas en realizar inversiones en desarrollo en múltiples temáticas, incluidas agua, salud, educación e infraestructura así como para hacer seguimiento al efecto de intervenciones que propendan por el desarrollo social y económico de las regiones.

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Appendix D - Poverty analysis (WP1) D.1. Poverty in the Andes compared with other CPWF basins Table 6 summarizes the different methods and findings of poverty analyses carried out in the nine CPWF basins. Several projects applied standard poverty mapping techniques, often using the livelihoods framework (Bebbington, 2001), three basins used ordinary least squares and geographically weighted regression to search for correlations with the driving factors of poverty, while another three projects conducted Bayesian network modeling (including the Andes BFP). In some cases, researchers were encouraged to apply methods that other basins were using. Data sources included census data, household surveys and focus group surveys, and poverty was represented as headcount ratio, poverty gap index, child mortality, or chronic malnutrition.

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Table 6. Methods and findings of studies on water‐poverty relationships in eight CPWF river basins  

Basin Methods Findings

Niger River (Clanet, 2008)

• Country data descriptive analysis • Interpolation of variables from geo-

referenced DHS • 3 indicators: wealth index, child

mortality and child morbidity • GWR water variables: distance to

dams, TARWR (total available renewable water resources) metric.

• Weak relationship with water quantity variables • Good relationship with water quality (child health

correlated with protected water sources) • Irrigation not of large enough scale to assess

relationship with poverty. • Strong relationships with education.

Volta River (Lemoalle, 2008)

• Descriptive analysis • Includes diseases related to water:

malaria, shistosomiasis, etc • Bayesian network analysis

• No statistical analysis of water and poverty • Problems of water scarcity and quality apparent • Ownership of cattle and market oriented agriculture

associated with lower poverty • Smaller farm size, higher poverty. • BN analysis showed important links with drought,

crop yield, employment status and land ownership • Water productivity related to crop yield related to

poverty • Some links explained by geographic coincidence.

Indo-Gangetic Basin (Sharma, 2008)

• Descriptive analysis • Regression

• Access to irrigation would seem to be important, because it mitigates water scarcity in regions such as the Punjab; but many cases with irrigation that are very poor

Karkeh (Ahmad, 2008)

• Head count and income gap measures derived from household surveys

• Descriptive analysis of driving factors

• Found that poverty is lower in upland areas (refuting common notion that it is higher)

• No direct linkages with poverty, but some indirect ones

• Limited opportunity for water management interventions to reduce poverty

Limpopo (Merrey, 2008)

• Review of Water Poverty Index Livelihoods analysis

• Methods for this group not entirely clear

• Water scarcity appears to be an important driver of poverty (according to the literature)

Mekong (Kirby, 2008)

• Livelihood framework • Case studies: wealth ranking, focus

group discussions, household interviews

• Bayesian network analysis

• Water-poverty problems vary spatially • Large irrigation infrastructure projects may not be

effective

Yellow River (BFP, 2008)

• Analysis of existing household data (otherwise methods unclear)

• Poverty reduction advances with de-collectivization of agriculture

Andes BFP

• Bayesian network analysis of sub-basins

• Multivariable regression • GWR • Scenario analysis

• In some defined niches water- poverty relationships are evident

• Broad relationships at the macro-level are not clear

Sao Francisco • Geographically weighted regression

D.2. Review of Poverty Measures Due to the numerous dimensions of poverty, there are a large number of methods to study it. In Latin America, one of the most commonly used methods is the Unsatisfied Basic Needs (UBN) approach (Schuschny and Gallopin, 2004). It relies on indicators from which livelihood conditions can be inferred (e.g., house construction materials, number of rooms in the house, attendance of children in school) and which are usually collected in normal censuses. The UBN approach is usually adapted to national conditions, using indicators that are relevant in a regional or national context. Comparisons between different countries are therefore difficult if the indicators used vary between countries.

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A relatively new approach to poverty assessment is the Small Area Estimation (SME) method (Ghosh and Rao, 1994). This method determines the relationship between consumption variables from household surveys and other poverty-related variables commonly collected in censuses. Regression relationships can then be used to map the poverty variables onto the census geography. Pioneering work on this methodology has been carried out in Ecuador, Colombia, and Peru. As with the UBN, the SME approach varies from country to country, depending on the indicators and variables used by national censuses and local household surveys. Other methods for measuring poverty include the globally standardized Human Development Index (HDI), which combines variables related to household wellbeing, health, and education (Sagara and Najam, 1998). It is based on key indicators of wellbeing (income), health (life expectancy, infant mortality, stunting), and socio-demographic variables such as literacy rate and rural population. The HDI is also the method most commonly used throughout the world, and has been adopted by the United Nations Development Program (UNDP). Infant mortality rate is often used as an indicator of poverty and wellbeing. Many studies have found the infant mortality rate to be correlated with household income or other poverty measures. Childhood stunting and other anthropomorphic measures are also relevant poverty indicators. When households suffer from poor harvests or otherwise do not have sufficient income to meet basic food needs, over the long term this affects the height and weight of children under five years. These indicators are relatively easy to acquire and allow cross-country evaluations. Finally, some key socio-demographic variables include rural population since – in very general terms – rural dwellers are usually poorer than urban people, due to a lack of access to resources. To evaluate the relationship between the different poverty indicators, we have examined four poverty measures (HDI, UBN, consumption-based, and SME) for the case of Bolivia (Table 7). The SME (percentage of poor in rural areas) shows a negative correlation with both consumption (r=-0.77) and the HDI (r=-0.68), suggesting that high rates of rural population with poor education status and low life expectancy are the ones where poverty is concentrated. The Pearson correlation coefficient between SME and UBN is 0.78, indicating that when structural deficiencies of households in rural areas increase, so does the number of rural poor. The negative and significant correlations between UBN and the HDI (r=-0.71), as well as consumption (r=-0.85) are consistent with the relationships mentioned above and show the same tendencies with rural population. Table 7. Correlations between different poverty measures for Bolivia 

Measures Pearson Correlation

Coefficient (r) Significance (p)

UBN vs. Consumption -0.85 0.0001

UBN vs. HDI -0.71 0.0001

% Rural Population (SME) vs. Consumption -0.77 0.0001

% Rural Population (SME) vs. UBN 0.78 0.0001

% Rural Population (SME) vs. HDI -0.68 0.0001

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D.3. The levels and distribution of poverty in the Andes The spatial structure of poverty can be partly discerned from the visualization of poverty data in map form, displayed for the whole of the Andes virtual basin. However, these ‘choropleth’ maps of administrative areas often hide important patterns of poverty and the use of point data – rather than polygons – makes some spatial statistics easier to calculate and visualize, such as spatial dependency and spatial autocorrelation. The choice of which point to represent an area often depends on the purpose of the analysis, but the most common is the central point or the 'centroid'. Centroids conventionally represent the 'centre-of-gravity' of a particular area based on the land area alone. For poverty analysis in the Andes basin we have followed the same principle, but instead calculated the centroid based on the weight of the population in each administrative unit. These weighted centroids are a better choice of point than conventional centroids because there are many administrative units which cover huge expanses but whose population is often concentrated along the transportation network, or close to the borders of neighboring units. The calculation of weighted centroids involves the creation of multiple points for each administrative unit, where each point represents a raster cell in the 2002 population dataset (Landscan, 2002). Every point is assigned a value equal to the population and all points are then projected (UTM Zone 17S, PSAD56 Datum) and combined according to their weights to give a single centroid per administrative unit. Spatial autocorrelation (using Moran’s I; Farrow et al., 2005) is the degree to which the values of any variable are more (positive spatial autocorrelation) or less (negative spatial autocorrelation) similar to near neighbors as opposed to places further away. We measured spatial autocorrelation for the different poverty measures independently for each country, given the different measures of poverty used and the different dates of these values. The first measure calculates the autocorrelation for all the points in the country and is called global spatial autocorrelation. The software GeoDa (Fotheringham et al., 2004) was used to calculate the values of Moran’s I, using the six nearest neighbors for each point (Table 8). Local Indicators of Spatial Autocorrelation (LISA) were calculated to see where clusters of high or low poverty are located within the four Andes BFP countries (Fotheringham et al. 2004). They are similar to the global spatial autocorrelation calculation and use the same spatial weights but are calculated at each location. The indicators produced are the significance of the autocorrelation, and the kind of autocorrelation encountered, i.e. clusters of places with low poverty, clusters of places with high poverty, and ‘spatial outliers’ of low poverty surrounded by high poverty and vice versa. The values for Moran’s I for the four countries (Table 8) suggest that there will be many clusters of high or low poverty and relatively few spatial outliers.

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Table 8. Local Indicators of Spatial Autocorrelation (LISA) for the four Andes BFP countries, using Moran’s I 

Country Variable Moran's I1 Interpretation

Colombia Infant mortality rate, 20072 0.59 clustered

Ecuador Food consumption poverty incidence, 20013 0.40 slightly clustered

Peru Under-nutrition incidence, 20054 0.68 clustered

Bolivia Consumption poverty incidence, 20015 0.45 slightly clustered 1 Moran's I in GeoDa with six nearest neighbors 2 Colombia (infant mortality rate, 2007) Moran's I = 0.13 moderately clustered. Z-Score = 68.3 (Inverse

distance, global standardization in ArcGIS) 3 Ecuador (food consumption FGT0_H, 2001) Moran's I = 0.07 slightly clustered. Z-Score = 30.1 (Inverse

distance, global standardization in ArcGIS) 4 Peru (under-nutrition, 2005) Moran's I = 0.18 clustered. Z-Score = 104.8 (Inverse distance, global

standardization in ArcGIS 5 Bolivia (consumption, FGT_0, 2001) Moran's I = 0.11 moderately clustered. Z-Score = 17.9 (Inverse distance,

global standardization in ArcGIS)

D.4. Factors influencing poverty at country level Regression analysis has a long history of use in understanding poverty relationships. Most often, the method is applied to household-level data. Since our study was looking at the broad scale of the Andean watersheds the method was applied to administrative districts. Assessment of spatial autocorrelation (see section D.3. above) was applied to the dataset to account for spatial dependence between administrative districts. Our assessment also considered that relationships between poverty and other variables may vary across the study area. The methodology is based on previous work using a similar approach, for example an analysis carried out for Ecuador using the Geographically Weighted Regression (GWR) technique (Farrow et al., 2009). Similar studies have been conducted for Kenya, Sri Lanka, and for the Sao Francisco basin in Brazil.

D.5. Factors influencing poverty at watershed scale Figure 33 illustrates the spatial distribution of poverty in the three Andes BFP focus sub-basins, using data from several studies carried out in each basin to understand the key factors that drive poverty (e.g., Johnson et al., 2009; Gómez et al., 2005; Raben, 2007; Chapalbay et al., 2007; Poats, 2006; Proaño and Poats, 2000).

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Figure 33. Distribution of poverty in the three Andes BFP focus sub‐basins using the Unsatisfied Basic Needs (UBN) (above and center, Peru and Colombia) and Poverty line (below, Ecuador) indicators 

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For two of the focus sub-basins (Ambato and Jequetepeque), we carried out Bayesian network modeling (see Figure 35 and Figure 36) to simulate different scenarios, altering the values of factors that could be viewed as interventions, to observe the effect on poverty variables. Bayesian networks model the probabilistic relationships between variables. The nodes in the network represent indicators that are conditioned by the variables that are linked to them (father-son). We used household data from Chapalbay et al. (2007) and Gómez et al. (2005), excluding all variables with less than 70% responses. Descriptive statistics and correlation matrices were developed to aid the development of the network and how variables fit into it. Analysis was carried out using Genie 2.0 software (Figure 34).

Figure 34. Initial Bayesian network modeling scheme for the Andes BFP focus sub‐basins Ambato (Ecuador) and Jequetepeque (Perú)  

Figure 35. Ambato (Ecuador) focus basin: Relationships between water, education and poverty  

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Figure 36. Jequetepeque (Peru) focus basin: Relationships between water, education and poverty  

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Appendix E - Intervention analysis (WP5) Matrix for data collection An extensive assessment of significant interventions for water resource management in the Andean region over the past 10 years resulted in a database of 55 projects (11 in Colombia, 25 in Peru, 8 in Ecuador, 6 in Bolivia, and 6 regional / Andean). See attached excel file. Of the 55 interventions, 11 (2 regional initiatives, and 3 projects in each of the three Andes BFP focus basins) were selected for qualitative analyze to (a) assess their impact on water productivity, poverty and the environment, (b) identify the most important factors of success or failure, and (c) develop recommendations for improvements of future interventions. Only those projects were selected that aimed specifically at improving water productivity and reducing poverty. Interventions that contributed to poverty reduction by tackling other principal objectives (e.g., yield increases in crop production, rural business and product chain development, or investments in infrastructure), were not included. Table 9. List of eleven water‐ and poverty‐related interventions in the Andes BFP focus basins

Name of the Intervention

Fúquene - Colombia Jequetepeque – Peru Ambato - Ecuador

Proyecto Checua Proyecto especial Jequetepeque-

Zaña CORICAM

Fondo Fúquene Fomento tecnología siembra directa, labranza minima y

abonos verdes

Fondo Manejo Sostenible Páramos

Recuperación Fúquene Proyecto Compensación Equitativa por Servicios

Amientales Hidrológicos (CESAH)

Proyecto Vasos de la Regulación de la Cuenca Alta del Río Ambato

Pago por servicios ambientales como

mecanismo para promover el desarrollo rural en las

cuencas altas de los trópicos (PSA)

Pago por servicios ambientales como mecanismo para provomer el desarrollo rural en las cuencas

alta de los trópicos (PSA)

Pago por servicios ambientales como mecanismo para provomer el desarrollo rural en las cuencas

alta de los trópicos (PSA)

Proeycto Cuencas Andinas (GTZ-CONDESAN)

Proyecto Cuencas Andinas (GTZ-CONDESAN)

Proyecto Cuencas Andinas (GTZ-CONDESAN)

  Primary and secondary data about the 11 interventions were collected from interviews during field visits to the study areas, interviews and working documents provided by other (non-local) actors involved in the interventions, material from the CONDESAN electronic archives, and websites from partner organizations. All information was recorded in a data matrix consisting of five main components: generalities, context, socio-economic impacts, environmental impacts, and water availability and productivity. Qualitative analysis of these data was then carried out using the Community Capitals Framework (CCF) by Flora and Flora (2004). Using a framework of seven basic capitals (natural, cultural, human, social, political, financial, and built), the CCF approach helps identifying the capitals in which investments have been made, and the impact of those investments on other capitals. It facilitates analysis of a multiple factors and processes that interact with each other, demonstrating the impacts of non-profit sector

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investments of multiple capitals on the livelihoods and sustainability of communities to which they are committed. Using CCF, we identified (a) the stocks of each capital before the intervention (baseline or intervention context), (b) the investments made for each capital (intervention strategy), and (c) the interactions between the different capitals and their impacts on each other. For this purpose, each capital was split into several case-specific parameters (Table 10, Table 11). The (qualitative) impact analysis of the 11 water- and poverty-related interventions, was carried out using specific impact indicators for financial and natural capital (Table 12). Table 10. Intervention context of 11 water‐related projects in the Andes BFP focus basins using CCF (Flora & Flora, 2008). 

Intervention Context Parameter Description Human Capital Education Level of instruction of the beneficiaries skills, abilities, and

knowledge of community members

Allow the implementation of technological or institutional innovations

Social Capital Trust Confidence level of the beneficiaries in authorities, technical assistants, government, others.

Network cohesion Presences of organized groups, social networks, community leadership, and institutional arranges

Political Capital Community access to power

Community members influence decision making

Groups of interest Presences of groups of interests that allow sustainable use and manage of natural resources

Natural resources use Procedure and rules for sustainable use of the natural resources

Cultural Capital Traditions, customs Allow the implementation of technological or institutional innovations

Financial Capital Credits Credit access Markets Market access Built Capital Storage and watering

infrastructure Infrastructure ensure water availability and distribution

Drainage infrastructure Reduce soil salinity risks Road access Allow communication Liquid and solid waste Infrastructure for liquid and solid waste treatment Natural Capital Environmental

degradation Diminish water availability and/or soils productivity

Biophysics features Appropriate for agricultural production Environmental services Conservation of environmental services

Table 11. Intervention strategies of 11 water‐related projects in the Andes BFP focus basins using CCF (Flora & Flora, 2008)

Intervention Strategy Parameter Description Human Capital Increase skills, abilities, and knowledge of community members (+) Social Capital Increase sense of belonging (Empowerment of communities) (+) Increase trust (+) Increase reciprocity (+) Increase network cohesion (+) Political Capital Increase community access to power (+) Move to a fair and equitable water and land allocation: (+) Cultural Capital Adoption of soil management techniques (e.g) conservation Agriculture) (+) Move to sustainable uses of land and water (+) Increase community capacity to adapt to change (+) Build Capital Road access (+) Increase water infrastructure (e.g. water storage capacity, water distribution,

water application) (+) Technological improvements (e.g. Crops varieties, watering techniques)

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Table 12. Impacts of 11 water‐related projects in the Andes BFP focus basins using CCF (Flora & Flora, 2008)

Intervention Impacts Parameter Description Financial Capital Increase short term economic benefits (+) Increase on incomes (+) Increase access to financial funds (+) Natural Capital Diminish environmental degradation (+) Increase availability of Natural resources (+) Increase on soils water storage capacity: (+) Regulation of water flows: (+) Increase on agricultural water productivity: (+) Improves conservation of ES (+)

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Appendix F – The AguAAndes PSS Policy exercises The AguAAndes PSS described in the main report comes with a series of policy exercises: the key two are described here. The first policy exercise allows users to define a baseline simulation of climate and hydrology for any part of the Andes at 1 km spatial resolution. This is a hot-spotting tool which provides a consistent hydrological dataset for the Andes but also allows the identification of areas with specific hydrological conditions (e.g., aridity, seasonal drought) and hazards (e.g., soil erosion). The second policy exercise defines the impact of climate change. Both exercises are described in greater detail within the AguAAndes system (www.policysupport.org/links/aguaandes), and are summarized here. Policy Exercise 1: Carry out a baseline simulation The purpose of this policy exercise is to carry out a baseline simulation providing information on climate, landscape, water and soil erosion conditions in a basin of your choice anywhere in the Andes. This baseline provide graphical, numerical, narrative and time-series analyses that can be visualized online or downloaded for further processing and which provide policy support for interventions in areas where significant local GIS or data capacity may not exist. To carry out such an analysis first go to www.policysupport.org/links/aguaandes and follow the instructions for signing in as a policy analyst. Then:

a. Use ‘define area’ to identify the 1 km resolution tile that you require support for then use the web-form that appears to enter a name for the tile and a location (a latitude and longitude anywhere within it). Click ‘Submit Choice’.

b. Then choose ‘Policy exercises’ and click ‘Submit choice’ to opt for the default, first of the policy exercises (‘baseline simulation’).

c. The model will then access and process the necessary input data (currently 149 separate raster maps), the model will run, and you will be presented with a series of maps and time series that you can browse online, geo-browse (in Google Earth or Google maps) or download as ARCASCII grids for your own offline processing and use. Using the AguAAndes PSS, a baseline simulation, providing a wealth of hydrological information should be possible within 15 minutes.

Policy Exercise 2: Carry out a climate change simulation: The purpose of this policy exercise is to carry out a simulation that compares the current hydrological baseline with that which is expected under one of a series of climate change scenario. This will provide information on expected changes in climate and their hydrological and soil erosion consequences for policy support. To carry out such an analysis first go to www.policysupport.org/links/aguaandes and follow the instructions for signing in as a policy analyst. Then:

a. If you have not already defined an area by carrying out the previous policy exercise, use ‘define area’ to identify the 1 km resolution tile that you require support for then use the form that appears to enter a name for the tile and a location (a latitude and longitude anywhere within it). Click ‘Submit Choice’.

b. Then choose ‘Policy exercises’, choose the ‘Climate change’ radio button and click ‘Submit choice’ to opt for this policy exercise.

c. The model will then access and process the necessary input data (currently 149 separate raster maps), the model will run for the baseline simulation, and then run again for a mean climate change scenario (using

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the mean of 17 GCMs A2a scenario discussed in this report). You will be presented with a series of maps, narratives and time series of the differences between the baseline and the climate change scenarios that you can browse online, geo-browse (in Google Earth or Google maps) or download as ARCASCII grids for your own offline processing and use. A climate change simulation, providing detailed analyses of the likely hydrological impacts of climate change projections for your region should be possible within 20 minutes.