Regression model for estimation of indian foodgrain production from summer monsoon rainfall

Parthasarathy, Balaji; Munot, A. A.; Kothawale, D. R.

doi:10.1016/0168-1923(88)90075-5

Cited by 107 publications

(83 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rainfall, which occurs towards the end of the summer monsoon, provides soil moisture for the Rabi crop, which is sown in the post-monsoon season. Therefore, the summer monsoon is mainly responsible for both Kharif and Rabi foodgrain production in India (Parthasarathy et al, 1988a). A small region in the extreme of peninsular India also benefits from the winter monsoon (October-December) rainfall for Rabi foodgrain production.…”

Section: Foodgrain Productionmentioning

confidence: 99%

“…The Indian summer monsoon, which provides 75 to 90% of the total annual rainfall over most parts of India during the 4 months from June to September, is vital to India's agriculture (Parthasarathy et al, 1992). The Indian monsoon circulation influences more than 60% of the Earth's population (Webster et al, 1998) due to its large-scale impact on food production, power generation, drinking water supply and overall economy (Mooley et al, 1981;Mooley and Parthasarathy, 1983;Parthasarathy et al, 1988a).…”

Section: Introductionmentioning

confidence: 99%

“…In India, the foodgrain production was estimated based on observed SMR (Parthasarathy et al, 1988a(Parthasarathy et al, , 1992Gadgil et al, 1999). Though several studies have shown the relationship between ENSO and rainfall, the link between agricultural production in India and ENSO has not been examined in detail.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of El Niño–southern oscillation on Indian foodgrain production

Selvaraju

2003

Intl Journal of Climatology

135

View full text Add to dashboard Cite

The impact of El Niño-southern oscillation (ENSO) on Indian foodgrain production was analysed for the period 1950-99. The inverse relationship between sea-surface temperature (SST) anomalies from June to August (JJA) over the NINO3 sector of the tropical Pacific Ocean and Indian foodgrain production anomalies (r = −0.50) was significant at the 1% level. During the warm ENSO phase, the total foodgrain production frequently decreased (12 out of 13 years) by 1 to 15%. In 10 out of 13 cold ENSO-phase years, the total foodgrain production increased from normal. The relationship between the SST-based NINO3 ENSO index and the Kharif season (June-September) foodgrain production anomalies (r = −0.52) was greater than for the Rabi season (October-February) foodgrain production (r = −0.27). The ENSO impact on rice production was greatest among the individual crops. The average drop in rice (Kharif season crop) production during a warm ENSO-phase year was 3.4 million tonnes (7%). In a cold ENSO-phase year the average production increase was 1.3 million tones (3%). Wheat (Rabi season crop) production was also influenced by ENSO, as it depends on the carryover soil water storage from the Kharif season. Sorghum and chickpea production are not significantly influenced by ENSO extremes.Inter-annual fluctuation of the gross value of Indian foodgrain production was very large, reducing up to US$2183 million in a warm ENSO year and increasing up to US$1251 million in a cold ENSO year. On average, a warm ENSO year costs US$773 million and a cold ENSO year had a financial gain of US$437 million from normal.The cumulative probability distributions of foodgrain production anomalies during cold and warm ENSO phases are shifted positively or negatively, relative to the neutral distribution. The warm ENSO forcing significantly (1% level) reduced the probability of above-average production. The cold ENSO forcing moderately increased the above-average foodgrain production over the neutral ENSO phase (5% level). A simple conditional probability forecast based on annual and JJA NINO3 SST predicted the category of foodgrain production in 11 of the 14 years. The results demonstrated that the relationship between NINO3 ENSO index and foodgrain production could be used for agricultural applications and policy decisions on food security for the rapidly growing population in India.

show abstract

Section: Foodgrain Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of El Niño–southern oscillation on Indian foodgrain production

Selvaraju

2003

Intl Journal of Climatology

135

View full text Add to dashboard Cite

show abstract

“…Researchers have attempted to isolate these short-term fluctuations by fitting and removing trends with polynomial and other parametric functions, and several forms of smoothing filters. Parthasarathy et al (1988Parthasarathy et al ( , 1992 used an exponential function to filter the all-India food production statistics for use in a regression-based food grain production-forecasting model. We detrended each time series by taking the difference, z i = z i − z i−1 between the value z in each year i, and the value in the previous year i − 1 (Box and Jenkins, 1976).…”

Section: Analysesmentioning

confidence: 99%

“…The southwest monsoon is critical to the kharif crop, which accounts for more than 50% of the food-grain production and more than 65% of the oilseeds production in the country. Year-to-year fluctuations in summer monsoon rainfall over India have a strong impact on the variability of aggregate kharif food-grain production (Parthasarathy et al, 1988(Parthasarathy et al, , 1992Gadgil, 1996;Webster et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Climate impacts on Indian agriculture

Kumar

Ashrit

et al. 2004

Intl Journal of Climatology

332

View full text Add to dashboard Cite

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.

show abstract

Sensitivity of the mean and variability of Indian summer monsoon to land surface schemes in RegCM4: Understanding coupled land‐atmosphere feedbacks

2015

View full text Add to dashboard Cite

A relatively simple land surface model, the Biosphere‐Atmosphere Transfer Scheme (BATS) and the more complex Community Land Model (CLM3.5) coupled to RegCM4 are used to investigate land‐atmosphere feedback processes during the Indian summer monsoon. Model simulations for 27 years show that the mean and interannual variability of rainfall and surface air temperature are affected significantly due to differences in the formulation of evapotranspiration and hydrological processes between BATS and CLM3.5, prescribed land use, land cover data and changes in net radiation. RegCM4 with CLM3.5 (RCLM) shows a reduction in surface moisture flux and precipitation but an increase in surface air temperature over most parts of India as compared to RegCM4 with BATS (RBAT). In terms of the mean and interannual variability of rainfall over central India, RBAT performs better than RCLM. Evapotranspiration over central India is found to be less (more) sensitive to soil wetness variations in RCLM (RBAT) compared to the multimodel estimate from the Global Soil Wetness Project, that is mainly attributed to the differences in ground evaporation and transpiration. Changes in evaporation efficiency between the models also lead to a reduction in the land‐atmosphere coupling strength for precipitation in RCLM. Furthermore, such changes decrease the convective instability over central and eastern India in RCLM leading to weakened convection, reduced large‐scale moisture convergence and precipitation over land. Observations of soil moisture, surface fluxes, and radiation are needed for better understanding and improvement of coupled land‐atmosphere feedbacks in models during the Indian summer monsoon.

show abstract

Regression model for estimation of indian foodgrain production from summer monsoon rainfall

Cited by 107 publications

References 10 publications

Impact of El Niño–southern oscillation on Indian foodgrain production

Impact of El Niño–southern oscillation on Indian foodgrain production

Climate impacts on Indian agriculture

Sensitivity of the mean and variability of Indian summer monsoon to land surface schemes in RegCM4: Understanding coupled land‐atmosphere feedbacks

Contact Info

Product

Resources

About