International Journal of Climatology · 2004 · 431 citations · 20 references
EngineeringAgricultural EconomicsSustainable AgricultureClimate-smart ProductionPublic HealthClimate-smart AgricultureClimate ChangeClimate VariabilityHydrometeorologyMeteorologyAgricultural ImpactGeographyClimate Change VulnerabilityMonsoon RainfallAggregate Food GrainAgricultureAbstract AgricultureClimate DynamicsClimatologyClimate ImpactsDroughtAgricultural ModelingSummer Monsoon
Agriculture, the backbone of India’s economy, depends heavily on the spatial and temporal distribution of monsoon rainfall. This study analyzes crop–climate relationships across India using historic production data for major crops and aggregate food grain, cereal, pulses, and oilseed production. Correlation analysis links monsoon rainfall and its potential predictors—Pacific and Indian Ocean sea‑surface temperatures and Darwin sea‑level pressure—to crop production. All‑India production of most crops and seasonal outputs were significantly correlated with summer monsoon rainfall, with strong associations for monsoon‑season crops to the three predictors, and state‑level analyses revealed spatial variations, especially a pronounced influence of El Niño and Indian Ocean SSTs in the western to central peninsula. © 2004 Royal Meteorological Society.
Abstract Agriculture (arguably the backbone of India's economy) is highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper presents an analysis of crop–climate relationships for India, using historic production statistics for major crops (rice, wheat, sorghum, groundnut and sugarcane) and for aggregate food grain, cereal, pulses and oilseed production. Correlation analysis provides an indication of the influence of monsoon rainfall and some of its potential predictors (Pacific and Indian Ocean sea‐surface temperatures, Darwin sea‐level pressure) on crop production. All‐India annual total production (except sorghum and sugarcane), and production in the monsoon (except sorghum) and post‐monsoon seasons (except rice and sorghum) were significantly correlated to all‐India summer monsoon rainfall. Monsoon season crops (except sorghum) were strongly associated with the three potential monsoon predictors. Results using state‐level crop production statistics and subdivisional monsoon rainfall were generally consistent with the all‐India results, but demonstrated some surprising spatial variations. Whereas the impact of subdivisional monsoon rainfall is strong in most of the country, the influence of concurrent predictors related to El Niño–southern oscillation and the Indian Ocean sea‐surface temperatures at a long lead time seem greatest in the western to central peninsula. Copyright © 2004 Royal Meteorological Society
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Numerical recipes in C: the art of scientific computing
Choice Reviews Online · 1993 · 18K citations · Full text
Numerical Recipes in C: The Art of Scientific Computing.
Chris Birchenhall, William H. Press, Saul A. Teukolsky et al. · The Economic Journal · 1994 · 4.4K citations
Numerical Analysis, Second Edition, Numerical Computation +9