Geoscience Reference
In-Depth Information
SUBSTOR models for root crops
The root crop or
SUBSTOR (SUBterranean STORage) models included cas-
sava, aroid and potato.
Other crop models
Other crop models included in DSSAT
are for sugarcane, tomato, sunflower and pasture.
InfoCrop model
InfoCrop is a decision support system (DSS)
that has been developed by the National Agricultural Technology
Project (NATP) by the Indian Council of Agricultural Research
(ICAR). It is based on a generic crop model that has been devel-
oped to provide a platform to scientists to build their applica-
tions around it and to meet the goals of stakeholders need for
information. The models in this DSS have a similar structure
and are designed to simulate the effects of weather, soils, agro-
nomic management, including planting, nitrogen, residues, irri-
gation and major pests on crop growth and yield. In particular,
it is based on MACROS, WTGROWS, ORYZA 1 and SUCROS
models. It is user-friendly and is targeted to increase applications
of crop models in research and development, and also has simple
and easily available input requirements. InfoCrop has been devel-
oped for 12 crops, namely, rice, wheat, sorghum, millet, sugar-
cane, chickpea, pigeon pea, cotton, mustard, groundnut, potato
and, of course, maize. The flowchart of input and output files
of the InfoCrop model and other characters have been depicted
in Figure 3.3. It is a dynamic crop yield model, developed by
Aggarwal and his coworkers from the Centre for Application of
Systems Simulation, IARI, New Delhi. It is a mechanistic and
dynamic crop simulation model, which can deal with the interac-
tion among weather, crop/variety, soils and management, besides
major pests. It has the capacity to evaluate the production of
major annual crops, namely, rice, wheat, sorghum, millet, sug-
arcane, chickpea, pigeon pea, cotton, mustard, groundnut, potato
and maize, and has a built-in database of Indian soils.
The InfoCrop model provides several outputs relating to
growth and development, water use, N uptake, soil carbon,
greenhouse gas emissions and yield losses due to various pests.
It can be used to accelerate the application of available knowl-
edge at field, farm and regional levels. This model also has
the capability of analyzing experimental data, estimating the
potential yield and yield gaps and also assessing the impacts of
climatic variability and climate change. The model also works
efficiently for management optimisation and assesses the envi-
ronmental impact study. Thus, this model is most versatile and
has many agricultural applications used for DSSAT.
