For further information contact: Douglas Pachico

[Project Description] [Logframe] [1999 Highlights]
[Output I: Future Impact of Research Estimated] [Output II: Impact of Past Research Monitored]
[Output III: Tools to Assess Impact of Research] [Output IV: Institutional Capacity Assessment Enhanced] [Donors] [Partners] [Staff List] [Publications and Presentations List] [References cited in Report]

Durante 1999 se realizaron labores de obtención, compilación, actualización y adición de datos clave a la mayoría de las bases de datos que el proyecto BP1 ha venido manteniendo, desde hace varios años, con el propósito de disponer de sistemas de información que puedan servir de apoyo, en un momento dado, al proyecto de Evaluación de Impacto, a los proyectos de investigación del CIAT y a los diferentes proyectos de instituciones e investigadores colaboradores del CIAT.

La Base de datos Socioeconómicos de América Latina y el Caribe, la cual dispone de información proveniente de diversas fuentes como FAO, USDA, FMI y World Bank entre otros, fue actualizada en un año más de información al haber sido incorporados datos correspondientes al año 1997 para las series de datos sobre comercio, uso de fertilizantes, riego y maquinaria, precios al productor, población, y tipo de cambio. Las serie de datos de producción se actualizaron con datos correspondientes al año 1998. Igualmente, durante 1999 se adicionó la serie de datos para Precios Internacionales de diferentes productos agropecuarios cubriendo los años 1967 a 1997.

La base de Datos de Colombia se adicionó con datos correspondientes al año 1997, a nivel de departamento y a nivel nacional, en lo que respecta a las series superficie cosechada, producción y rendimiento de los diferentes productos agrícolas. Nuevas series de datos se adicionaron a esta base de datos, las cuales se relacionan en el cuadro 1.

Cuadro 1. Series de datos adicionadas durante 1999 a la base de datos de Colombia.

3.2.A. Modificaciones

Durante 1999 se realizaron modificaciones en el diseño de muchas de las páginas del Web de Evaluación de Impacto con el propósito de obtener una mejor apariencia así como de permitirle al usuario una mayor facilidad de navegación.

Algunas de las páginas modificadas son las siguientes:

• Página principal (ver Figura 1).
• Menú de Bases de Datos disponibles vía Internet (ver Figura 2).
• Despliegue de consultas a la base de datos en forma gráfica (ver Figura 3)
• Menú de Trends in CIAT Commodities (ver Figura 4).

Figura 1. Página Principal.

3.2.B. Acceso a Bases de Datos

Toda la información almacenada en las diferentes bases de datos (base de datos de América Latina, base de datos de Colombia, base de datos de Variedades) está disponible vía Internet. Mediante el menú de acceso (Figura 2), cualquier usuario puede ingresar a la base de datos y extraer la información deseada la cual puede ser visualizada en forma de tabla o en forma de gráfica (ver Figura 3). La gráfica obtenida puede ser modificada por el usuario directamente en su propio computador y ajustada a sus necesidades particulares ya sea en lo que respecta a tipo de gráfica, color, título, o cualquier otra de sus características.

Figura 2. Menú de acceso a las Bases de Datos.

Figura 3. Respuesta, en forma gráfica, a consulta realizada a la base de datos.

3.2.C. Trends in CIAT Commodities

El sistema de generación automática de reportes, disponible en la Base de Datos Socioeconómicos, fue complementado para permitir la elaboración de tablas de tendencias, no sólo para América Latina sino también para Africa y Asia.

Utilizando dicho sistema se generaron los cuadros correspondientes a los Trends 1997 para los productos Arroz, Frijol, Carne, Leche y Yuca. Los cuadros respectivos pueden ser consultados vía Internet previa selección del producto deseado a partir del menú principal de productos el cual es desplegado en parte superior de la Figura 4.

Figura 4. Menú de acceso a Trends in CIAT Commodities.

3.2.D. Utilización del Web y consultas a las bases de datos vía Internet

El Web dispone de un sistema de información de visitas que permite analizar los diferentes accesos, dado que el sistema registra datos acerca del país de origen, la dirección Internet del sitio de ingreso, la fecha y la hora del ingreso, entre otros. Esta información nos muestra que el Web ha sido consultado por usuarios de Estados Unidos, Canadá, 9 países de Sur América, 5 países de Centro América y el Caribe, 14 países de Europa, 9 países de Asia, 3 países de África además de Australia y Nueva Zelandia. Los usuarios en general están vinculados a instituciones educativas, comerciales, gubernamentales o a organizaciones como la FAO, el Banco Mundial o el CGIAR.

El cuadro 2 muestra un total de 1009 visitas (distribuidas por región) que fueron realizadas durante el período 20 de Octubre de 1998 al 20 de Septiembre de 1999, lo que significa, que durante los 334 días considerados, la Web tuvo un promedio ligeramente superior a 3 visitas diarias. Se observa en este cuadro, que el grupo principal de usuarios está conformado por organizaciones sin ánimo de lucro donde están incluidos los centros internacionales pertenecientes al CGIAR, la FAO, el Banco Mundial, entre otros. El segundo mayor grupo de usuarios lo conforman los Estados Unidos y Canadá seguidos muy de cerca por los países Europeos. Un cuarto grupo lo conforman el resto de países del continente americano (Sur y Centro América) ubicados muy distantes de los países del Asia y del continente Africano. Se observa igualmente, que a un buen número de usuarios, el sistema de automático de registro fue incapaz de identificarles su procedencia.

Cuadro 2. Visitas por región realizadas al Web de Impacto.

En cuanto al uso de las bases de datos a través de Internet, las estadísticas muestran un total de 324 consultas realizadas a la base de datos de América Latina, 174 consultas efectuadas a la base de datos de Colombia y 71 a la base de datos de Variedades.

Los datos más solicitados en la base de datos de América Latina corresponden a las series de Producción de Cultivos con 125 consultas y a las series con los indicadores macroeconómicos Tasa de Cambio e IPC con 67 consultas. Las series restantes muestran un número de consultas realizadas que varían entre 23 y 49.

La base de datos de Colombia tuvo 174 solicitudes de información donde se destaca que 68 de ellas correspondieron a consultas sobre datos de producción de cultivos. Las series de datos restantes tuvieron solicitudes que varían entre 18 y 29 consultas.

Las estadísticas anteriores son el resumen para el período comprendido entre el 10 de Enero de 1999, fecha en que se implementó el sistema de registro de consultas, y el 20 se Septiembre de 1999. Es destacable que en este período de solo 8 meses, los visitantes al Web han mostrado interés por el sistema de información implementado, el cual seguirá permanentemente en proceso de actualización, crecimiento y desarrollo.

3.3 Methods - Economic Surplus Models - by: J. A. García, L. Rivas

Durante 1999 se realizaron una serie de actividades tendientes tanto a mejorar el modelo de análisis de excedentes económicos - MODEXC, como a hacerlo disponible a una mayor población que pudiera estar interesada en hacer uso del modelo.

Por las razones anteriores, el modelo se convirtió a una nueva versión en Excel-97 donde el lenguaje utilizado para comunicación con el usuario es el Inglés. De igual manera, el manual del usuario, inicialmente escrito en idioma Español, fue traducido al idioma Inglés y posteriormente editado y publicado en el documento "MODEXC Release 4.1. A Friendly Computer Model" (ver Figura 5).

Figura 5. Portada del manual de usuario de MODEXC (versión en Inglés)

La versión del modelo MODEXC disponible en Internet ha sido solicitada durante el período Enero 20/99 a Septiembre 20/99 en 70 ocasiones. Las estadísticas registradas muestran, que personas radicadas en los Estados Unidos, Australia, 9 países de Sur América, 9 países de Centro América y el Caribe, 4 países de Europa, 3 países de África y 1 país del Asia han obtenido copia del modelo y del manual. De igual manera, los registros muestran que un total de 40 usuarios están vinculados con instituciones de investigación, 17 con universidades y 13 con instituciones de carácter comercial. Estos resultados nos indican que MODEXC está llegando a una amplia comunidad donde puede llegar a constituirse en una buena herramienta de apoyo, particularmente en el caso de las instituciones universitarias y de investigación.

Modelo de Excedentes Económicos MODEX

Metas para 1999

· Incrementar la utilización del modelo tanto a nivel nacional como internacional

· Continuar con el mejoramiento de aspectos conceptuales y teóricos relevantes.

Resumen

Para cumplir con el primer objetivo se produjo una versión en inglés tanto del modelo como del manual, los que se pusieron a disposición de los usuarios en la pagina Web del Proyecto de Evaluación de Impacto, para facilitar su conocimiento y difusión.

En la parte conceptual del modelo se hizo una mejora significativa en el tema de impacto del cambio tecnológico sobre la demanda. Esto último se puede ejemplificar en mercados como el de yuca, en donde un cambio tecnológico en el procesamiento y utilización del producto seco, puede derivar en un incremento en la demanda por yuca fresca.

Las versiones anteriores del modelo incluían un parámetro (w), que correspondía al incremento genérico anual de la demanda, derivado de un cambio tecnológico en la producción. Dado que al mercado pueden entrar diferentes tecnologías, en distintos momentos del período de evaluación, con diferentes patrones de difusión y que algunas de ellas pueden inducir incrementos en la demanda y otras no, la versión anterior del modelo era muy limitada, ya que no asociaba el incremento de la demanda con una tecnología específica ni con su patrón de difusión.

La versión actual relaciona el crecimiento de la demanda con cada tecnología evaluada y con su patrón específico de difusión y de obsolescencia. De ésta forma, la magnitud del efecto sobre la demanda en el caso de una tecnología dada, dependerá del momento en que empieza su adopción, de la intensidad de la misma, y de la velocidad del proceso de obsolescencia o desadopción.

Para lograr lo anterior, se asume que el coeficiente (w_i) corresponde a la tasa de crecimiento de la demanda, inducida por la adopción de la tecnología i. Tal coeficiente tiene un patrón de crecimiento logístico, similar al de la adopción de la tecnología bajo evaluación. De ésta forma la presión de demanda generada por la adopción de la tecnología i, es baja cuando ella se encuentra en sus primeras fases de adopción, se incrementa cuando la adopción se consolida y comienza a declinar en la medida en que la tecnología entra en su fase de obsolescencia.

3.4 New Tools For The Economic Evaluation Of Agricultural Technologies Having Natural Resource Impacts: Cropping in the Savanna Ecosystems of Meta Province, Colombia - by: S. Wood

BACKGROUND AND JUSTIFICATION

Greater demands for accountability, shrinking research resources, and the growing complexities of research goals are focusing the attention of R&D managers on the need for improved R&D evaluation and decision making methods. Furthermore, the increasingly competitive nature of R&D funding is accelerating the search for areas of comparative advantage or complementarity in research -- essential information for selecting strategically important research themes and appropriate R&D partners.

Methods for evaluating the direct production affects of technology, such as those involving increased genetic potential or improved efficiency in the use of inputs such as seed, labor, machinery, and fertilizer are fairly well established and increasingly applied. However, when the scope of inquiry is expanded to include productivity dynamics arising from the interaction between new technologies, production systems, and natural resource stocks and flows, these methods are at best partial and more often indedquate. There is an urgent need to systematically extend the R&D evaluation framework in order to encompass this natural resource dimension.

A major development issue in the extensive humid tropical savannas of Latin America is the difficulty of managing the highly-weathered soils in a sustainable way. Even modest attempts at increasing livestock and crop productivity must be undertaken with great care if a rapid decline in (already low) soil fertility is to be avoided. Furthermore, new technologies should not only prevent soil productivity losses in a cost-effective manner but, ideally, should also help to increase the inherent productive capacity of soil over the long-run. As has been demonstrated in the Brazilian cerrados, there are potentially large payoffs from bringing savannas into more intensive, long-term agricultural production (Recognising that a mix of agricultural and other land uses such as ecosystem conservation (e.g., preservation of gallery forests and other important biological habitats) may well be preferred from a social, rather than private, perspective). In Colombia, however, the savanna (or Llanos) area has been subjected to much land speculation, associated primarily with the laundering of drug money, and land prices have been artificially inflated relative to their agricultural opportunity cost. At the same time, continued guerilla operations and high interest rates have done little to foster positive attitudes to long-term land-enhancing investments. It is important, therefore, that realistic analyses are made of the potential economic attractiveness of new agricultural technologies targeted to such areas. This study, therefore, is concerned with the development of improved methods that can not only evaluate the direct and natural resource impacts of technical change, but that can also represent the broader policy and market context within which technical change takes place.

The International Centre for Tropical Agriculture (CIAT) has been a pioneer in the development and practical application of economic approaches to research evaluation (Pinstrup Andersen et al 1976, Scobie and Posada 1977, Lynam and Jones 1984, Pachico, Lynam and Jones 1987). In recent years the International Food Policy Research Institute (IFPRI) has also been active in further development of R&D evaluation methods as well as analytical tools such as the DREAM software (Alston, Norton and Pardey 1995, and Wood and Baitx 1998). Since the evaluation of natural resource related research poses new and difficult challenges CIAT and IFPRI are collaborating in this joint methodological research venture together with Michigan State University (MSU), the International Fertilizer Development Centre (IFDC), and postgraduate students from Wageningen and Bogotá.

STATEMENT OF THE PROBLEM

Accelerated loss of soil productivity under cultivation

The development problem faced is that while the savannas are extremely extensive their agricultural use is limited, primarily because of poor soil productivity. Not only are the savanna soils of relatively low inherent fertility under natural conditions, but even those low levels degrade relatively rapidly under cultivation (typically the soils are unproductive after 3-5 years of cultivation). Furthermore, the soils are relatively poorly drained, and in the rainy season many areas are difficult to access. These biophysical limitations have, in turn, provided little incentive for systematic, sustained investment in infrastructure in the llanos. Most economic exploitation has been associated with low-productivity extensive livestock operations. Investment in agricultural research targeted to the llanos has been made in the expectation that more intensive, sustainable production systems can be developed. There is even a hope that some of these production systems could bring about significant long-term increases in the intrinsic productive capacity of savanna soils (e.g., by building up organic matter, and improving the soil’s physical and biochemical properties).

Estimating the potential biophysical impacts of technical change

From an evaluation perspective the goal is to broaden the traditional scope of analysis. There is increasing concern with measuring the impact of technology not only on crop or livestock productivity, but also on the stock and condition of the underlying natural resource base (in this study the soil resource). To do this requires some means of estimating the implications of each change in soil condition on productivity in the current and all subsequent years – knowing that devising adequate measures of soil condition is, in itself, no easy matter. For example, while there is growing evidence that much of the rapid decline in the productivity of llanos soils under cultivation is related to a breakdown in physical structure, soil scientists and agronomists have traditionally focused more on measuring chemical soil properties such as the availability of nitrogen, phosphorus, and organic matter.

In an attempt to capture the complex interactions of climate, soil, and crops for R&D purposes there has been a major scientific investment in the development of crop growth simulation models, and perhaps the best known of these is the DSSAT© suite of models for, rice, wheat, maize, barley, sorghum, millet, beans, soybean, peanut, potato, cassava, and pasture. While they offer, in principle at least, the opportunity for integrated assessment of crop yield (main product and residues), soil moisture, nitrogen, phosphorus, and most recently, organic matter, the models largely reflect accumulated research experience in temperate zones, most notably in the USA. Furthermore, there is considerable variability in the reliability and completeness of each of the component modules. Nonetheless, with proper calibration there is growing evidence that such models can provide a systematic and robust means of jointly estimating important crop, water, and soil variables over a broad range of biophysical and management conditions, including those found in tropical developing countries. The models allow analysts to evaluate new genetic materials with improvements in, say, yield potential, light, water or fertilizer use efficiency, and pest and disease resistance, as well as improved management practices such as in the timing and quantity of water and (organic or inorganic) fertilizer applications.

Simulation models can also be extremely helpful in overcoming another significant constraint in performing ex ante R&D assessments – how to assess the combined effect of a number of simultaneous technological enhancements. A crop improvement research program may comprise a range of distinct initiatives related to both germplasm and agronomy and it is extremely difficult to know how those technologies may work together. In the absence of explicit experimental data, analysts have simply (and most likely erroneously) assumed the combined effect of different technologies to be additive. Crop simulation models offer the possibility of applying a number of simultaneous technological changes and then relying on the model’s internal intelligence of crop growth processes to integrate those changes in a meaningful way, and trace out their combined impact on yields and soil conditions. This makes for much simpler, and probably more reliable, elicitation of R&D expectations from scientists. Each disciplinary group need only describe its expected R&D outputs in terms of the specific plant, soil, or water processes their work will impact, and need not be asked to make extremely speculative estimates of the final consequence of their work on, say, crop yields in farmers’ fields.

Finally, the use of process-driven simulation model holds open the possibility of extrapolating calibrated models across space and time to look at the broader effects of technical change. We are particularly concerned here with extrapolating across time since a major goal of research is to develop viable long-term production systems. Unfortunately, long-term experiments are rare and simulation models offer a promising way of making some judgement about long-term prospects even though we may only have, say, (as in the experiments used in this study) 3-5 years of experimental data on which to base our forecasts. The implicit assumption is that we can make more reasonable forecasts if our underlying prediction tool (the crop growth simulation) is built around the capacity to model generic physical processes than if it is, for example, a black-box regression technique.

MSU and IFDC are collborating in the study because of their long association with the original crop growth components of DSSAT but also because of their exoertise in specific new components of direct relevance to the study. MSU has recently developed a phosphorus response model that is capable of representing the complex dynamics of savanna soils in which phosphorus availability is one of, if not the, major soil fertility constraint. IFDC’s expertise includes the integration of DSSAT with GIS as well as participating in the development of the "sequential" module that handles multi-year simulation. IFDC has also worked on improving the DSSAT user interface.

Estimating the potential socio-economic impacts technical change

The established means of evaluating the impacts of technical change in agricultural production were not designed for the complexities of measuring and valuing related impacts on natural resources. Existing methods generally treat a new technology as having a constant yield increase or cost reduction effect in the field of any given adopter. Thus, R&D impacts on the aggregate level of production arise only as a consequence of changes in adoption over time (e.g. as more producers adopt the technology the impact of the technology becomes greater). In the cases of interest here, however, the introduction of crop production systems into natural savanna bring about significant changes in soil conditions that have cumulative impacts on in-situ productivity, and the assumed "ceteris paribus" condition of the economic evaluation no longer holds.

Experimental data are available from the Carimagua site in Metá department over a number of years, and cover a number of different production system treatments (rice monoculture, maize monoculture, maize/soybean rotation) and untreated control plots. These data allow the change in yields and soil variables for each production system treatment in each year to be calculated relative to the control plot, and hence, automatically allows for (nets out) the effects of year-to-year climate variation. By calibrating the DSSAT model to these different treatment effects we can estimate the relative changes in yields, soil moisture, nitrogen, phosphorus (an perhaps carbon), with and without these treatments. Furthermore, DSSAT will be used to extrapolate the effects observed over 3-5 years to a longer time series of effects for say 25 years that will correspond to the time needed to make an economic evaluation (because adoption can occur, and hence economic benefits can be gained, over many years).(The evaluation of impacts on pasture improvement was purposely excluded because the DSSAT pasture model was not considered sufficiently reliable. Furthermore, and perhaps more importantly, pasture effects are compounded by livestock grazing and herd management issues that confound the evaluation. To keep the evaluation tractable the study focuses on crop systems. Extension to pasture/animal sysyems would be a logical next step in extending this line of research)

The economic evaluation will be made independently for each commodity - rice, maize, soybean, and will explicitly take into account only the (observed and DSSAT simulated) yield effects associated with each production system treatment. However, it is important to note that;

1. Yield levels are changing as a consequence of the underlying changes in soil resource conditions. Hence, we do capture all the (on-site) agricultural productivity impacts of changes in the soil resource, and,
2. DSSAT models the maize/soybean rotation in a single simulation, recognizing the relevant biophysical interactions between the two crops. Thus, even though the economic model treats them as separate commodities from a market perspective, the R&D effects input to the economic model do include any production interactions between the crops, e.g., the yield effects we obtain for maize are greater than they would be otherwise be because of the nitrogen fixing capacity of preceeding soybean crops.

On the basis of the market data currently being collected we expect to use the municipio as the basic economic unit of analysis, although municipios will be stratified (and perhaps broken down) by zones using the Colombian agro-ecological classification system (IGAC 1985). Only production will be broken out at the municipio level, and we will use two demand regions – one to represent the current and projected internal demand in the Metá department, and the other (which we will assume is located in Villavicencio) to represent actual and potential export to Bogotá, the recipient of practically all the department’s agricultural surplus. A preliminary list of the specific production and consumption regions to be modelled in each commodity simulation is attached. In the cases of maize and rice, production data are available at a disaggregated level; for traditional and technical maize, and for irrigated and rainfed rice. The new technologies being tested in the Carimagua experiments are for technical maize and rainfed rice, but all types will be included in the economic analysis to ensure that potential impacts on the overall maize and rice markets are taken into account.

After calculating total benefits for both producers and consumers we will use farm size and land use data collected from agricultural census to estimate the likely distribution of producer benefits among different producer groups.

OBJECTIVES

General Objective

The principal objective of this study is to develop and test the feasibility of new methods for the economic evaluation of technologies having significant impact on the stock or flows of natural resources that underpin agroecosystem productivity. If the methods can be validated they can serve not only to assess the likely impact of technologies currently under development for the Colombian Llanos (the source of the study’s empirical data), but also as a general means of testing a range of strategic research policy and technology design scenarios. Such a capacity could help generate greater research benefits and alter the distribution of benefits in ways deemed more socially acceptable (for example, by increasing the share of benefits accruing to poor rural households).

Specific Objectives

1. To describe the existing production systems in the Metá department with regard to the production of maize, rice and soybean
2. To analyse the trends in prices, production costs and technology adoption (specifically in irrigated and rainfed rice, and technical and traditional maize production systems) in Meta department.
3. To use the DSSAT model to generate long term estimates of biophysical impacts of R&D (by calibrating the model on data from the CIAT/CORPOICA experimental station at Carimagua, Meta. These biophysical simulations will be performed at CIAT).
4. To combine the results of 2 and 3 above and perform a range of economic simulations of the potential benefits of the new rice, maize and soybean technology across the Metá department.
5. Make an assessment of the distribution of economic benefits both spatially (e.g., across different municipios), and by different producer groups

RESEARCH QUESTIONS

In order to satisfy the objectives mentioned above consideration must be given to the following research questions:

1. Assuming no technical change and falling world commodity prices can, Llanos crop producers remain competitive? How much would the sector shrink in the next 25 years if no new technologies were introduced?
2. Could monoculture production of rice and maize be biophysically and economically sustainable? If so, under what level of inputs?
3. Based on the experimental data, what would be the long-term gains of adopting a maize/soybean rotation?
4. Given recent trends in farm level crop productivity, prices and technology adoption in the Metá department what would be the potential economic benefits from promoting the new maize/soybean rotation? Would those benefits cover the estimated costs of research?
5. Do the the potential benefits vary spatially, reflecting the variation of agroecological, market, and demographic conditions? Which producer and consumer groups will be most affected?

THE APPROACH

The economic framework for evaluating R&D relies on the concepts of welfare economics, in particular on estimating changes in economic surplus (in total, as well as separately for producers and consumers). Benefit streams arising from the adoption of new methods of production for rice, maize, and maize/soybean can be calculated on an annual basis over a user-specified simulation period, normally of 20-30 years. And these benefits can then be offset against R&D costs in order to calculate benefit-cost measures of the attractiveness of the R&D investment.

Representing technical change

If we first consider that a new technology is fully adopted, its likely impact will depend on two main considerations;

1. The initial market conditions for the specific agricultural commodity that the technology was designed for
2. The subsequent change in supply and demand conditions for the given commodity over time. Specifically the difference between the market evolution with and without the new technology.

In reality the decision to adopt (or not adopt) the new technology is taken by different producers at different times so the likely rates and levels of adoption in different regions are important elements in estimating the stream of economic benefits arising from technical change. The full technology investment and use cycle incorporated in the economic model includes;

1. A time lag for the development and testing of new technologies
2. A measure of the uncertainty associated with the R&D process (probability of R&D success)
3. The potential impact of technology, K (arising from, say, yield increasing or input reducing technologies) that is expressed as a reduction in the unit cost of production. The size of K in each year, K_t, will be estimated by the DSSAT model.
4. A time lag following the release of a new technology until the ceiling level of adoption is likely to be reached
5. A ceiling level of adoption (in area or quantity terms)
6. A rate of disadoption if, say, the effects of a single "wave" of new technology are being modelled.

The data

The information on which this study will be based comes from primary and secondary sources. Primary data comes from several sets of "long-term" (3-5 year) experimental field data and on-farm trials involving different crop and pasture germplasm, crop rotations, and management practices in the llanos. The experiments monitored changes in pasture production, animal live weight gains, and crop yields over time as well as recording the simultaneous changes in a range of soil properties. These benchmark data help establish the biophysical (and, hence, economic) impacts of new production systems in an experimental setting. The on-farm trials and municipio production data provide additional information on the extent to which producers are actually adopting new technologies given the overall sector trends and the resource and market constraints that producers face. These data have been collected over the last 10-15 years by CIAT in collaboration with the Colombian national research agency CORPOICA (formerly, until 1993, known as ICA), the International Fertilizer Development Centre (IFDC) and others.

Secondary information for the Metá province being collected in Bogotá and Villevicencio includes; production, area, yield, prices, and costs of production, as well as census data on population and farm size. As far as possible this data is being collected at the municipio level (of which there are 24 in Metá). Additional information will include detail on wholesaler prices at Villevicencio and Bogotá the major markets for agricultural surplus from Metá department.

Methods and data analysis

After defining the scenarios for estimating the potential economic impact of new production technologies in the llanos of Metá, the R&D evaluation package, DREAM, will be used to simulate the likely magnitude and distribution of the economic benefits of technical change.

Biophysical data from the Department of Meta will be used to identify a number of relatively homogeneous agroecological zones across the department, beyond the experimental sites. The biophysical model, calibrated data from the experimental sites, will then be run with regionalized climate and soil parameters, and the results of that calibration (performed at CIAT) will be linked directly to municipio data in the economic evaluation. This evaluation will include estimates of the total economic benefits for both producers and consumers.

Workplan 1999-2000

January - June 1999

• Format experimental data for input and use in DSSAT model (sequential analysis component). This work will be undertaken at CIAT (Mariela Rivera)
• Initiate calabration of DSSAT model for rice and maize monoculture treatments, as well as the maize/soybean rotation treatments.(Marial Rivera – CIAT)
• Collect production, market, and adoption data for Meta province. (Astrid Hernandez, Liliana Mosquera, Bogota and Cali)
• Collect biophysical data and survey/census data for Meta province (Astrid Hernandez)
• Establish dialogue with MSU and IFDC and plan the details of their involvement (Stanley Wood).

July - December 1999

• Hold a technical review and work planning meeting at IFPRI (Washinton DC) (IFPRI, CIAT, IFDC, MSU)
• Review progress on calibration of DSSAT
• Plan for integration of the phosphorus model
• Prepare a schedule of biophysical simulations
• Decide on temporal and spatial extrapoaltion approaches and allocate implementation responsibilities
• Complete biophysical simulation work at both Cariamgua and for Meta Department (CIAT, MSU, IFDC)
• Prepare data for units of analysis in the economic simulation (municipio by agroecological zone units). (Hernandez, Mosquera, Wood, IFDC)
• Design a range of evaluation scenarios that encompass policy, trade and technology issues (see research questions above) as well as incorporate the agreed biophysical scenario (Wood, Hernandez, Mosquera)
• Make preliminary economic analysis for Meta Department (Wood, Hernandez, Mosquera)

January - March 2000

• Write up results, perform additional analyses as guided by the results and make recommendations on the technical and institutional feasibility of the approaches developed (IFPRI, CIAT, MSU, IFDC)
• Submit report to CIAT/IFPRI management and the donor IDB (April 2000)