Abstract
Dryland agriculture in India is practiced on 97 million ha of the cultivated area that supports 40% of the human population and 60% of livestock population by producing 44% of the food and fodder requirements. Even if India can achieve the full potential of irrigation in 139.5 million ha, still 75 million ha drylands would continue to depend on rainfall from southwest (SW) and northeast (NE) monsoons, characterized by high rainfall variability that cause most of production uncertainties. Thus dryland agriculture continues to play a crucial role in India’s food security. However, productivity gains have been relatively insignificant and risk-averse dryland farmers have to improve agricultural productivity with suitable management options and matching application of farm inputs to maximize crop productivity and income, while minimizing crop failure and input losses against uncertainties of seasonal weather to feed the booming population.
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References
Carberry P, Hammer G, Meinke H, Bange M (2000) The potential value of seasonal climate forecasting in managing cropping systems. In: Hammer GL, Nicholls N, Mitchell C (eds) Applications of seasonal climate forecasting in agricultural and natural ecosystems. Kluwer Academic Publishers, Dordrecht, The Netherlands
Gadgil S, Seshagiri Rao PR, Narahari Rao K, Savithri K (1999) Farming strategies for a variable climate. Center for Atmospheric and Oceanic Sciences, Indian Institute of Sciences, Bangalore, India (CAOS Report 99AS6)
Gadgil S, Seshagiri Rao PR, Narahari Rao K (2002) Use of climate information for farm-level decision making: rainfed groundnut in southern India. Agr Syst 74(3):431–457
Hammer GL, Holzworth DP, Stone R (1996) The value of skill in seasonal climate forecasting to wheat crop management in a region with high climate variability. Aust J Agr Res 47:717–737
Hansen JW, Jones JW, Irmak A, Royce F (2001) El Niño-Southern Oscillation impacts on crop production in the southern United States. In: Rosenzweig C (ed) Impacts of El Niño and climate variability on Agriculture. American Society of Agronomy (ASA), Madison, WI, USA (ASA Special Publication 63, pp 55–76)
Hastenrath S (1987) On the prediction of Indian monsoon rainfall anomalies. J Clim Appl Meteorol 26:847–857
Mc Cown RL, Hammer GL, Hargreaves JNG, Holzworth DP, Freebairn DM (1996) APSIM: a novel software system for model development, model testing and simulation in agricultural research. Agr Syst 50:255–271
Meinke H, Hochman Z (2000) Using seasonal climate forecasts to manage dryland crops in northern Australia. In: Hammer GL, Nicholls N, Mitchell C (eds) Applications of seasonal climate forecasting in agricultural and natural ecosystems. Kluwer, Dordrecht, The Netherlands, pp 149–165
Nageswara Rao V, Singh P, Balaguravaiah D, Dimes JP, Carberry P (2004) Systems modeling and farmers’ participatory evaluation of cropping options to diversify peanut systems in Anantapur region, India. I: APSIM simulations to analyze constraints and opportunities. In: New directions for a diverse planet. Handbook and Abstracts for the 4th International Crop Science Congress, Brisbane, Australia
Nelson RA, Holzworth DP, Hammer GL, Hayman PT (2002) Infusing the use of seasonal climate forecasting into crop management practices in North East Australia using discussion support software. Agr Syst 74:393–414
Ropelewski CF, Halpert M (1987) Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon Weather Rev 115:1606–1626
Ropelewski CF, Halpert M (1996) Quantifying Southern Oscillation-precepitation relationships. J Climate 2:268–284
Shukla J, Mooley DA (1987) Empirical prediction of the summer monsoon rainfall over India. Mon Weather Rev 115:695–703
Shukla J, Paolino DA (1983) The Southern Oscillation and long range forecasting of the summer monsoon rainfall over India. Mon Weather Rev 111:1830–1837
Smith TM, Reynolds RW (2003) Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997). J Climate 16:1495–1510
Stone R, Smith I, McIntosh P (2000) Statistical methods for deriving seasonal climate forecasts from GCMs. In: Hammer GL, Nicholls N, Mitchell C (eds) Applications of seasonal climate forecasting in agricultural and natural ecosystems. Kluwer, Dordrecht, The Netherlands, pp 135–147
Virmani SM, Sivakumar MVK, Reddy SJ (1982) Rainfall probability estimates for selected locations of semi-arid India. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Andhra Pradesh, India (Research Bulletin 1)
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Rao, V.N., Singh, P., Hansen, J., Giridhara Krishna, T., Krishna Murthy, S.K. (2007). Use of ENSO-Based Seasonal Rainfall Forecasting for Informed Cropping Decisions by Farmers in the SAT India. In: Sivakumar, M.V.K., Hansen, J. (eds) Climate Prediction and Agriculture. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44650-7_17
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DOI: https://doi.org/10.1007/978-3-540-44650-7_17
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