Tradeoff Analysis Workshop

November 17-21, 2003; Dakar, Senegal

Biophysical Working Group.

Introduction

The Tradeoff Analysis System is a nice example of a bio-economic model that links biophysical and economic modeling. Its application requires a multi-disciplinary research team with backgrounds in a number of disciplines. The disciplines strongly depend on the indicators and scenarios that have been defined in the research priority setting (Step 2 of the tradeoff analysis process1). Despite the differences between applications we will always need team members with a bio-physical and economic background. The biophysical scientists work on the soil and climatic databases, the crop-growth simulation models to estimate the inherent productivities and the environmental impact models. The economist will work on the farm survey, econometric models to estimate the production models and the economic simulation model. The two groups will jointly participate in the discussions with stakeholders and the definition of indicators and scenarios. The definition of the latter may require specific data collection to estimate changes in model parameters or input data. In this Section of the course, the bio-physical workgroup will establish a soil and climatic database appropriate for the Tradeoff Analysis Model. On Wednesday we establish the soil databases and setup the DSSAT models for a first run with the tradeoff analysis model in which we use the model of day 1 and 2 and carry out a basic scenario run. On Thursday we will go in more detail of carbon modeling, the simulation of crop rotations and the definition of a number of climate scenarios.

All data for our working group can be found in the directory TO31_NI_BP.

1 See Section 1 of the on-line course or the Tradeoff Analysis Model User guide.

1 Soil and climate data

In the Tradeoff Analysis System, soil and climatic data are used for the estimation of the inherent productivity and the analysis of environmental impacts. It is difficult to list the exact data requirement as this is dictated by the type of biophysical models. Note that the selection of models is not only dictated by the indicators and scenarios. The spatial and temporal scales at which these models have to deliver the output is similarly important. Finally, the level of detail is important. A simple qualitative land evaluation may provide basic estimates with respect to the expected productivity of farmers’ fields. Here, of course one should remember the role the inherent productivity plays in the tradeoff analysis model. It is used as a relative value of growing conditions of farmers’ fields. Clearly mechanistic simulation models do have their specific advantages, but the data requirements increase exponentially with model complexity. The basic unit of analysis of the tradeoff analysis model is a farmers’ field. This means that our models should operate at a similar spatial scale and that data are required at the same level. Soil data are typically not available at such a level of detail. We therefore have two options: 1. Specifically collect data for the survey fields (i.e. fields included in the farm survey), or 2. Disaggregate the available soil survey data to a level that they can be used at the farm level. Both options will involve data collection in the field. So far, the Tradeoff Analysis Team decided to disaggregate available, general purpose soil surveys. In its applications in the Peruvian and Ecuadorian Andes, detailed digital elevation models were available. Given the importance of topography as a driving factor in soil formation and the extreme differences in topography the TOA team was able to downscale the soil survey with a minimum of field data observations. Even under the relatively flat conditions in Senegal important soil differences do occur with topographic differences:

Source: Manlay et al, 2002.

2 However, there are no detailed digital elevation models available that are accurate enough to reveal these differences. We therefore made use of a false color Landsat thematic Mapper image that was available at the internet. In addition, two detailed soil maps were available for small parts of the study area. We followed the following procedure: 1. Digitize the detailed soil survey, 2. Overlay the soil survey with the satellite image, 3. Obtain distributions of the reflections in each of the soil units and for each of the three bands (Green-Red-Infrared). 4. Develop a classification procedure that allows us to classify the Landsat image on the basis of the observed reflectancies. 5. Apply the classification to the satellite image, 6. Finally a generalization filter of 100 meter diameter to generalize the classification and to get larger soil units.

Weather data form an important input parameter in the crop growth simulation model. 10 years of data from the weather station in Nioro was available. No other weather station was located in the study area. The Nioro weather station provided data on minimum and maximum temperature, rainfall and sun hours at a daily time step. Sun hours were used to estimate solar radiation. Given the lack of topographic differences we assumed that the Nioro data are representative for the total study area. In cases with more topography we could e.g. change the temperature with altitude. A PhD student in the Tradeoff Analysis project (Guillermo Baigorria) developed a mechanistic tool for the interpolation of weather data. Atmospheric conditions are derived from the measured conditions for a few weather stations. Subsequently the weather conditions in other locations are simulated.

So far, the TOA-team did not provide a set of generic tools for the development of site- specific data sets on soil and climate. The procedures that were applied in the different study sites were dictated by local conditions and the available data.

Structuring soil and weather data for the Tradeoff Analysis System

Within the Tradeoff Analysis System we make use of the concept of functional horizons. Functional horizons are soil horizons that have been classified for specific purpose (or function). This deviates from some of the basic soil classification procedures where soils were classified on the basis of their pedogenetic history. In that approach, e.g. clay cutans at 1.5 meter depth were highly relevant whereas they have little importance for current agricultural practices. Often, the functional horizons are described on the basis of macro- morphological properties that can easily be observed in the field. Logically this has important consequences for the surveying procedures. In the concept of functional horizons we observe a shift from a soil description that focuses on the exact soil properties for each soil horizon towards a soil description that has accurate information on the depth and thickness of the different soil horizons. This can be illustrated by the following two examples:

3 1. the thickness of the topsoil varies between 10 and 30 centimeters with an organic matter content of 2.1-2.2%. 2. the thickness of the topsoil varies between 20 and 25 centimeters with an organic matter content of 2.0-2.5%. You can easily check the effect of the two procedures. Calculate the range in total organic matter for the two examples in kg/ha. Assume a bulk density of 1.0 g/cm-3. It stresses the fact that in many cases more emphasis should be placed on the depth of the soil layers and reduce the number of expensive soil chemical and physical soil properties

Exercise 1: In this exercise we will establish the soil database for the TOA system on the basis of a number of basic datasets: - A soil survey for a small part of the study area, - A Landsat Thematic Mapper image for the study area.

1. Open the Arcview project file Dakar.apr in our data directory C:\TO31_NI_BP and look at the available data. 2. Think of a procedure to use the satellite image and the soil survey to create a soil map for the whole study area. Write your procedures out in a flowchart. 3. If you think you have sufficient Arcview knowledge you can carry out the procedure and create the soil map. Note: the soil map should be in a grid format. 4. If you did not succeed in the previous task, follow the following directions:

1. Look at the available datasets?. Do you see a visual correlation between the Landsat image and the soil units? Try to describe the relationship in words. 2. Determine the reflectancies of the thematic mapper image for each of the soil types: 1. Select the soil map by clicking on its legend 2. Go to the option Summarize zones in the Analysis menu and follow the instructions 3. Derive the reflectancies for each of the soil types. 3. Apply your classification to the entire satellite image. Select the landsat image, go to reclassify under the Analysis menu, apply the classification. 4. You may want to generalize your outcomes to get larger units using the neighborhood statistics in the analysis menu.

5. Now create a new sub-directory in our tradeoff directory (C:\TO31_NI_BP) called GIS. 6. Export the soil map to the GIS directory using the Export data source option in the File menu. 7. Copy the soildat.dbf, hordat.dbf, and profdat.dbf files (from the C:\TO31_NI\GIS directory to the new GIS directory.

4 8. Enter the Tradeoff Analysis model and define the directory with GIS data and the different soil database files.

Execise 2: In this exercise we will set up the weather files and the weather map for the study area. This is relatively straightforward since we only have a single weather file available in this exercise. 1. Create a grid with a single value for the whole study area in the same format as the soil map. (Note: an easy way to create this map is to reclassify our soil map to a single value). 2. Export the map (via export data source in the File menu) to our new GIS directory. 3. Create a new dbase file that contains the id of the weather map and the code of our weather station and looks similar to the following file:

Place the file also in the GIS directory 4. Enter the Tradeoff Analysis model and define the data files and maps for the climate database.

Exercise 3: This simple version of the tradeoff analysis model makes use of the village ids. The survey data showed that significant management differences were found between the different villages. We therefore have to include the village number in the files with the inherent productivity. A point map with the villages is available in our Dakar database. We will create simple thiessen polygons to create a map of the villages. 1. Arcview does not recognize Thiessen polygons but we can interpolate the village identifier using inverse distance weighted interpolation with a neighborhood of 1. Interpolate grid can be found in the Surface menu. Create a grid with the same specifications as our soil map. 2. Export the database using Export data sources in the file menu to our GIS. 3. Define the appropriate settings in the tradeoff analysis model.

Setting up the DSSAT files for Millet and Groundnut. In the database directory you will find the cost files and a datafile with the timing of operations from the 2001 survey.

Exercise 4: 1. If you are not familiar with the DSSAT suite of models study the database structure as it is described in the documentation.. 2. Adapt the timing of operations and the fertilization levels in the X-files 3. Define the activities in the Tradeoff program.

5 Ready? If you are all set you can start running the tradeoff model! 1. Calculate the inherent productivities of the survey fields (Survfields.dbf) and Run the model estimation, 2. Sample a set of fields 3. Run the simulation model and draw the tradeoff diagrams. 4. Now try to stratify the area and produce different tradeoff diagrams for different soil types.

References Manlay, R.J., Kairé, M., Masse, D., Chotte, J.-L., Ciornei, G., Floret, C., 2002. Carbon, nitrogen and phosphorus allocation in agro-ecosystems of a West-African savanna. I. The plant component under semi-permanent cultivation. Agriculture, Ecosystems and Environment 88, 215-232.