Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-12

AUTHORS

M. Jayakumar, M. Rajavel, U. Surendran

ABSTRACT

A study on the variability of coffee yield of both Coffea arabica and Coffea canephora as influenced by climate parameters (rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH)) was undertaken at Regional Coffee Research Station, Chundale, Wayanad, Kerala State, India. The result on the coffee yield data of 30 years (1980 to 2009) revealed that the yield of coffee is fluctuating with the variations in climatic parameters. Among the species, productivity was higher for C. canephora coffee than C. arabica in most of the years. Maximum yield of C. canephora (2040 kg ha-1) was recorded in 2003-2004 and there was declining trend of yield noticed in the recent years. Similarly, the maximum yield of C. arabica (1745 kg ha-1) was recorded in 1988-1989 and decreased yield was noticed in the subsequent years till 1997-1998 due to year to year variability in climate. The highest correlation coefficient was found between the yield of C. arabica coffee and maximum temperature during January (0.7) and between C. arabica coffee yield and RH during July (0.4). Yield of C. canephora coffee had highest correlation with maximum temperature, RH and rainfall during February. Statistical regression model between selected climatic parameters and yield of C. arabica and C. canephora coffee was developed to forecast the yield of coffee in Wayanad district in Kerala. The model was validated for years 2010, 2011, and 2012 with the coffee yield data obtained during the years and the prediction was found to be good. More... »

PAGES

1943-1952

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00484-016-1181-4

DOI

http://dx.doi.org/10.1007/s00484-016-1181-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019878065

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27378280


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