A simple DSS for potato crop scheduling in Nilgiri hills of Western Ghats
Downloads
Published
Keywords:
Decision support system, Infocrop-potato model, planting date, potential yield.Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract
A decision support tool has been developed for providing information on the optimum time of planting and the likely consequences of early or late planting of potato in about 173 locations of Nilgiris region of Tamil Nadu state in India. This DSS was developed for the most popular variety of the region i.e., Kufri Jyoti, and the choice to select right time of harvest is also incorporated in this DSS by simulating the yields at 100 and 120 days. The tool consists of a database of simulated yield at 100 and 120 days after planting derived through InfoCrop-potato model. This DSS is developed with the database generated using the daily weather data developed through weather generators and the potential yields estimated with the help of InfoCrop-Potato model. These data were generated by running the model under rainfed conditions for five dates of planting using the weather data for each location generated through weather generators. A user interface was developed in Visual Basic to access this database. The DSS developed for the purpose of potato crop scheduling is of great significance, which enables the farmers as well as extension functionaries for taking right decisions on timing the planting and harvesting of potato crop on which the major fraction of Nilgiri's economy depends. The simulated results when compared with the actual data and a good degree of correlation was observed between two.
How to Cite
Downloads
References
Govindakrishnan, P.M., Singh, J.P., Lal, S.S.
and Panigrahy, S. 2007. A methodology for preharvest predictionof mean potato yield at regional
scale using INFOCROP-POTATO model. Potato
J. 34: 125-26.
Loague, K. and Green, R.E. 1991. Statistical and
graphical methods for evaluating solute transport
models. Over view and application. J. Contam.
Hydrol. 7: 51-73.
Magarey, R.D., Travis, J.W., Russo, J.M.,
Seem, R.C. and Magarey, P.A. 2002. Decision
support systems: Quenching the thirst. Pl. Dis.
: 1-14.
Mayer, D.G. and Butler, D.G. 1993. Statistical
validation. Ecol. Modlg. 68: 21-32.
Nelson, L.A. 1991. Choosing corn hybrids.
NebGuide G79-471, Cooperative Extension
Service, Institute of Agriculture and Natural
Resources, University of Nebraska, NebraskaLincoln, Lincoln NE.
Shashi Rawat, Govindakrishnan, P.M. and
Chauhan, R.K. 2012. Climate based delineation
of areas suited for production of ware and
processing potato in India. Potato J. 39: 75-80.
Singh, J.P., Govindakrishnan, P.M., Lal, S.S. and
Aggarwal, P.K. 2005. Increasing the efficiency
of Agronomy experiments in potato using
INFOCROP-POTATO model. Potato Res. 48:
-52.
Singh, J.P., Govindakrishnan, P.M., Lal, S.S. and
Aggarwal, P.K. 2008. INFOCROP-POTATO A
model for simulating growth and yield of potato
in the Sub-Tropics. CPRI, Shimla, Technical
Bulletin, 2008, 30 p.
Singh, J.P., Govindakrishnan, P.M., Lal, S.S.
and Aggarwal, P.K. 2005. Infocrop-potato: a
simulation model for growth and yield in tropics
and Subtropics. SWCACIP, New Delhi, Newslett.
: 1-2.
Singh, J.P., Praharaj, C.S., Lal, S.S.,
Govindakrishnan, P.M. and Aggarwal, P.K.
Parameterization and validation of the
INFOCROP-POTATO model. J. Indian Potato
Assoc. 30: 57-58.
Thomas, R.S. and No'am, Seligman. 2000.
Criteria for publishing papers on crop modelling.
Field Crop Res. 68: 165-72.
