GIS-Based Regional Spatial Crop Yield Modeling
Satya Priya* and Ryosuke SHIBASAKI**
*Post Doctoral Fellow, **Professor
Center for Spatial Information Science
University of Tokyo, 4-6-1 Komaba
Meguro-ku, Tokyo 153-8505, JAPAN
Fax: +81-3-5452-6414
Email: satya@skl.iis.u ,satya@tokyo.ac.jp
Keywords: Agriculture Productivity, GIS, Spatial Modeling
Abstract
Traditional decision support systems based on crop simulation models are normally site-specific.
In order to address the effect of spatial variability of soil conditions, topography and weather
variables on crop production, a Geographic Information Systems (GIS) was linked with crop
simulation models. This system was used to predict spatial yield variability of wheat, rice and
maize crop on regional level as a function of spatial soil water conditions under various weather
regimes. The model "Spatial-EPIC" (Satya et.al., 1998) has been developed to understand the
interaction between crops and the environment to accomplish the task. As example, India was
used for model application and validation viewpoint, because of the fact that the country owes
the most diversified agroecosystem. Results showed that the crop yield of maize, wheat and rice
crop varied significantly as a function of seasonal climatic variation, favorable crop growing
region, soil water holding characteristics and selected crop management strategies (e.g., the
temporal increase of fertilizer application, irrigation applied and so on.)
Introduction
Agricultural system are overwhelmingly a complex process of air, water, soil, plants, animals,
micro-organism and everything else in a bounded area that people have modified for the
purposes of agricultural production. An agroecosystem can be of any specific size. It can be a
single field, household farm or it can be the agricultural landscape of a village, region or nation.
Some of the most important decision in agricultural production, such as what crops to grow and
on how much land to allocate depends on the existing knowledge base of current and future
physical conditions like soil and climate, yields and prices. Modeling of the various processes in
the system helps us to understand its flow and intricacies. An important issue in agricultural
environmental modeling is that all the basic units (water, soil and chemicals) have a spatial
distribution, and since this distribution does affect the processes and dynamics of their
interaction considerably, geographic information system (GIS) is emerging as an important tool
in modeling.
Till date, there have been a lot of studies on agricultural potential productivity. To relate actual
crop productivity, however, only model-based simulations are not sufficient. Biophysical spatial
based model is still lacking to compute agricultural productivity at regional or national level
although the estimates of farm productivity are being done using experimental/plot based model.
Site-specific management, or precision farming, is a strategy in which cropping inputs such as
fertilizer are applied at varying rate across a field in response to variations in crop needs.
Modeling within a GIS offers a mechanism to integrate the many scales of data developed in
and for agricultural research. Irrespective of the scale at which various crops, agriculture
environment models operate, it is known that management practices geared towards
conservation and productivity are initiated at the field level. At present, however, few
agricultural producers are utilizing the true analytical power of GIS and computer simulation
models, partly because the loose or no linkages developed to-date between GIS and mostly
public-domain modeling software are extremely cumbersome to use or are esoteric. Thus a need
exists for an integrated, GIS modeling system to allow agricultural producers as well as policy
makers to know the impact of differences between input and output spatially from one
place/region to other from better management, productivity and profitability viewpoint.
The objective of this study was to expand the use of crop simulation models through a linkage
with GIS and spatial physical databases. Also it was aimed to apply the computer system and to
study distribution of yield as a function of applied physical and management practices of spatial
variability on a regional level.
Material and Methods
Model Selection
Proper model selection is the most important step in any modeling exercise. As a evident, a
plethora of environmental models exist, each with unique characteristics and capabilities. In
addition to this, optimistic claims by model developers, and conflicting modeling objectives, all
create a dilemma for the model user in selecting model for a particular objective.
Therefore, based on the literature reviewed and expert opinion gathered from model developers,
EPIC (Williams, J. R. and Sharpley, A.N.,1989) was selected for further development under the
defined framework of this study. Some additional model features that favored the selection of
EPIC are (Dumesnil 1993):
- EPIC is a continuous, field scale agricultural management/water
quality model.
-
The EPIC model is broad-based in terms of its components to model major
biophysical processes which include weather, hydrology, erosion, nutrients (nitrogen and
phosphorus) cycling, pesticide fate, soil temperature, crop growth, tillage, plant environmental
controls and economics.
-
The data required by EPIC are minimal and was made available
after deriving the concept of generators.
-
The model provides parameter data files for major
crops, soils, and tillage practices.
-
EPIC is also equipped with a stochastic weather generator.
-
EPIC is capable of simulating the long-term effects of cropping systems on soil erosion
and productivity in specific environments.
Development of “Spatial-EPIC” Model
To understand what these crops needs are from point to point/pixel to pixel, it is necessary to
understand the relationship between crop yield and both controllable (such as fertilizer nutrients)
and uncontrollable (such as soil, topography) factors. The effect of these factors on yield is
complex and may change from point to point within a field. Recently, one of the many
challenges facing regional, national or global agricultural research is the simple understanding
of potential solutions to the constrains for achieving its solutions. Identification of opportunities
and constraints is the task of characterization. Modeling within a GIS offers a mechanism to
integrate the many scales of data developed in and for agricultural research. Data access,
including modeling results, expands to a "decision system" or decision tool which uses a mix of
process models (where appropriate/possible) and biophysical data (growing season climate
characteristics, soils, terrain). An accurate spatial (and temporal) database enables the
characterization of agricultural systems. This ability is vital in the developing world for efficient
resource allocation in agricultural research. Agroecosystems are complex entities, which span
several levels or scales, with different processes dominating each scale. Therefore, a dynamic
agricultural system characterization requires biophysical characterization integrity to be
maintained by addressing particular objectives with specific information – information which
may aggregate up - or down - scale (e.g. the aggregate description of a complex of soils would
deliver a sensible "regional" characterization). With spatially interpolated climate data, digital
elevation models, and low resolution soils data in place, agroecosystem characterization
commences with simple models used to differentiate growing season and off season
characteristics. These "climate analog" models here used as a "Weather Generator" serve to
describe the initial domain or target area for a range of priority setting. Other information -
usually much more difficult to acquire - becomes critical in refining target domains as resource
access, land tenure, cropping system, labor availability etc. dominate the land use system at
higher resolutions.
Therefore, 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 production in addition to soil and water resources. As we saw in above model
selection section, at present, most of the crop models are location specific (point based) in nature,
but to understand the impacts on the agricultural systems, it is necessary to have spatially
explicit information. Therefore, development of spatially or raster based biophysical crop model
took long way in helping us to understand many intricacies of modeling of large areas at coarse
and fine resolution. To do this, Spatial Erosion Productivity Impact Calculator, [Spatial-EPIC]
(Satya et.al.,1998) was developed which gave us a new direction to simulate crop production at
regional scale from microscopic simulation at each small piece of land in an efficient way,
enables us to incorporate the environmental issues. “Spatial-EPIC” is a crop simulation model
developed to estimate the relationship between soil erosion and crop productivity which has
been implemented in GIS environment at 50km and 10 km grid size for a nation and region
respectively to have spatial distribution of crop output then the classical point based method.
Biophysical Computation
The model is composed of physically based components for simulating plant growth, nutrient,
erosion, and related process for assessing crop productivity, determining optimal management
strategies, erosion and so on. Simultaneously and realistically, model simulates the physical
processes involved using readily available inputs. Commonly used input data are weather, crop,
tillage, soil-attributes and management parameters. The model runs on defined rather derived
cell size data layers provided by the user depending on their availability. All physical processes
and plant management are being computed using mathematical model to find the effects of crop
productivity coming from different processes and how all these different processes affects
overall crop productivity. "Spatial-EPIC" is composed of physically based submodels for
simulating weather, hydrology, erosion, plant nutrients, plant growth, soil tillage and
management, and plant environment control. The model runs on daily time-step therefore, each
model is linked subsequently and interactively with other sub models as explained below. In
brief, the each sub module are dealt with their computation procedure. Weather: daily rain,
maximum and minimum temperature, solar radiation, wind and relative humidity can be based
on measured and data and/or generated stochastically. Hydrology: runoff, percolation, lateral
subsurface flow are simulated. Erosion: it simulates soil erosion by wind and. Nutrient Cycling:
the model simulates, nitrogen and phosphorus fertilization, transformations, crop uptake and
nutrient movement water (for this paper the erosion part has and nutrient cycling not been
included). Nutrient can be applied as mineral fertilizers, in irrigation water, or as animal
manures. Soil: soil temperature responds to weather, soil water content and bulk density. It is
computed daily in each soil layer. Tillage: the equipment used affects soil hydrology and
nutrient cycling. The user can change the characteristics of simulated tillage equipment, if
needed. Crop Growth: A single crop model capable of simulating major agronomic crops. Crop-specific
parameters are available for most crops. The model also simulates crop grown in
complete rotations. Plant Environment: It is capable of variety of cropping variables,
management practices, and other naturally occurring processes. These include different crop
characteristics, plant population, dates of planting and harvest, fertilization, irrigation, tillage
and many more those are normally practiced in the field.
