Modeling Spatial Crop Production: A GIS Approach
Satya Priya, Ryosuke Shibasaki and Shiro Ochi
Center for Spatial Information Science, University of Tokyo
7-22-1, Roppongi, Minato-ku, Tokyo 106, Japan
Fax: 81-3-3479-2762
E-mail: satya@skl.iis.u-tokyo.ac.jp
Abstract:
Spatial Erosion Productivity Impact Calculator (Spatial-EPIC) gave a new direction to simulate crop production at regional scale based on microscopic simulation at each small piece of land in an efficient way. The result presented in this paper is based on existing/available spatial agro-climatic dataset but information related to soil and water has not been included spatially at this stage as they are under process of development. Using Spatial-EPIC crop model observed crop yield for the period 1995-1993 are compared with yields simulated and it is found that agreement between simulated and observed crop yield shows better correlation.
Introduction
GIS based modeling of an agroecosystem is expected to give a new approach in order to provide agricultural managers with a powerful tool to assess simultaneously the effect of farm practices to crop practices to crop production in addition to soil and water resources. At present, most of the crop models are location explicit information. Therefore, development of spatially or raster based biophysical crop model with go a long way in helping us to understand many intricacies of modeling of large areas at 1 to 10 km resolution. The large-scale distribution of crops is larges determined by climate. This study gives a preliminary result of Spatial-EPIC (which is in the process in identifying weakness of GIS data interpolation capabilities. We focused our efforts mostly on climatic dataset generation for three major crops Maize-Wheat-Rice which can be further used to analyze the response of other natural systems to climate change in a later course. The scale resolution of most crop models used in simulating climate change impacts is, for all practical purposes, a single point on the earth's surface. To get regional estimates of crop responses to climate change requires either scaling up the model simulations from point estimates or scaling up the data inputs from point measurements and then performing the simulations. Both ways will result in some loss of information due to aggregation error. The effect of spatial scale of climate inputs on crop simulation is examined in this study.
The purpose of this paper is to examine the effect of spatial aggregation (i.e, the size of the areas from which the synthesis of multiple in situ data points into a single areal estimate is made) of climate data used in crop models and find an agreement between modeled and observed crop yields. We begin with interpolation of WMO data at the degree (almost 10km on ground) grid resolution. Specifically this paper evaluates the relative impacts of climate and crop spatially throughout the study area.
Materials and Methods
In this section, the followings are described; the region of the study, 1) the crop model used in the analysis , 2) the modeling of climate data to a climate model grid network and 3) the procedures used to compare observed and simulated yields over a continuum of spatial scales within the grid network. The area chosen for this study compasses one of the northeastern state of India namely Bihar. The area studied is located between 21° 58' to 27° 31' North latitude and 83° 19' to 88° 17' East longitude. It is overlain at 10km grid size, which cover the entire state Bihar (Figure 1). Three different crops were modeled for the period of 1985-1993 using (Spatial-EPIC) a crop growth model described below. In northern India there are two distinct seasons Kharif (July to October), and Rabi (October to March). Therefore the crops modeled were Kharif maize, Rabi wheat and kharif rice at every two year crop rotation. The period between March to June (locally called as Zaid season) left fallow during the simulation. This distribution mimics the predominant crops, which is being taken widely in the area.
Figure 1. Study area showing WMO stations and overlain with interpolated climate data as an
Simulation Models:
Until now there have been a lot of studies on agricultural potential productivity but to relate actual crop productivity, model based simulations are not adequate. Estimates of on-farm and off-farm productivity are being done using experimental/point based model. Biophysical spatial based model is still lacking to compute them at regional or national level. Simulation models, such as EPIC (Willimas, 1990) model used was originally point-based developed by USDA-ARS. The first results of Spatial-EPIC being developed by the author is presented in paper (described below) together with mathematical presentation of the biophysical crop simulation process. The model is designed to predict what would happen under different conditions, such as change in temperature, precipitation, atmospheric CO2, soil moisture and so on at regional/national scale. The results presented in this paper are based on climate data. A model developed by Richardson (1981) was selected for use in EPIC because it simulates temperature and radiation, which are mutually correlated with rainfall. The weather generator module offers a convenient method of obtaining the numerous long sequence weather data that are required for the simulat
