Info

Miracle Farm Blueprint

Organic Farming Manual

Get Instant Access

0 IR 36 with improved tolerance to temperature stress

3.11 Mitigation Efforts: Use of Simulation Studies

The global climate change is not a new phenomenon and every new anthropogenic driving force can affect the rate of the climate change. The changes, which might be observed over a geologic time period, could happen over a shorter time span, since the start of Industrial Revolution. Apparently, human activities are causing rapid climate change. The concentrations of key anthropogenic greenhouse gases, such as carbon dioxide (CO2), methane, nitrous oxide and tropospheric ozone, have reached their highest levels, primarily due to the combustion of fossil fuels, agriculture, and land-use changes (Korner and Bazzaz. 1996; Rosenzweig and Hillel 1998). These greenhouse gases stay in the atmosphere for a long time, as the atmospheric life time for these chemicals vary (5-200 years for CO2, 12 years for CH4 and 114 years for N2O). If the current greenhouse gas emission rates continue into the future, agriculture and crop production will face enormous pressure from the stresses caused by these heat trapping gases.

Rice paddy fields are one of the major sources of atmospheric CH4 and N2O. There is a need for careful evaluation of the source strength of this ecosystem, and of the influence of soil, water and crop management practices on both grain yield and greenhouse gas fluxes. A number of models have been developed in recent years to predict the rate of CH4 emission from rice fields, each model having its own strategy or philosophy. Some models tried to use the least number of input parameters and more empirical equations to capture basic pattern of gas fluxes, so that these models could be easily used at the regional or global scale. Several models, such as DNDC (Li et al. 2004), Expert-N, CASA, CENTURY, NLOOS, MERES, MEM and DAYCENT have been developed. Recently, a process-based model INFOCROP (Aggarwal et al. 2006a, b) has been developed for scaling-up gas emission estimates from tropical agriculture. These models can be used to demonstrate a number of mitigation options for reducing methane emissions from rice soil, based on the significant influence of soil texture and pH, moderate influence of organic C content, little effect on short-term seasonal simulations due to the variation in the quantity of aboveground biomass returning to the soil, increasing the length of mid-season aeration, and addition of sulphate fertilizer reducing CH4 emissions from rice soil.

The climate models indicate that greater warming can occur in the next century; all land areas will warm more rapidly than the global average, particularly at high northern latitudes in the winter season. The projected climate change will have beneficial and adverse effects on both environmental and socio-economic systems, but the larger and more abrupt changes in climate will cause more adverse effects on crop production and thus affecting the regional food security. Hence, there is a continual need to identify and develop mitigation options to minimize the rate of adverse climate changes.

3.12 Crop Models in Decision Making

The major challenge in crop modeling is to develop user friendly and economically viable technology that is readily adoptable by the decision makers as well as farmers. More interactive communication between the model developers and all the stakeholders including extension staff, consultants, farmers, researchers and policy decision makers is required for improving the transferability of models from laboratories to various modes of applications. The crop modeling can be best used as an aid for on-farm decisions. The crop growth simulator, when incorporated with important physiological process and appropriately addressed various physical process, such as water uptake, sunlight etc into one package, would correctly predict growth and yield under varying climate change conditions. For practical use, the farmers require less complicated decision support systems. Hence, any model based on reasoning system, which simplifies the information input and provides a user friendly output format for crop management decisions, such as when to irrigate and how much fertilizer to apply, the extent of land to lease, etc., is very useful to the farmers. But, an extensive consultation with farmers, while testing and validation of the model before it is released for public use, is necessary (Whisler et al. 1986). Every positive feed back from farmers during various stages of model building can help to its successful adoption in various locations and wider use by farmers.

3.13 Natural Resource Management Using Crop Models

The crop simulation models are increasingly available as a part of Decision Supporting Systems (DSS). These DSS can be used to extrapolate results for strategic decision making tasks, such as regional planning, policy analysis and poverty alleviation. The crop systems models have played a useful role in agricultural planning, more commercially in developed countries like USA, France and Australia, and in developing countries as a part of internationally funded projects in the last two decades. Regional or national planning involves analysis of information that covers different crop production systems for making decisions like best land use to meet the specific development goals. One of the successful approaches is coupling of crop simulation models with GIS containing land and water characteristics of a region. In India, Selvarajan et al. (1997) used the ORYZA1 and WTGROW models for analyzing trade offs between water use, farm income, and adoption risks at the district level. Jansen (2001) developed a methodology called SOLUS (Sustainable Options for Land Use) in which crop simulation model MACROS was coupled with a Geographic Information Systems (GIS) and a Linear Program Model (LP) to define crop options and associated management practices in Costa Rica. Using the same approach, Schipper et al. (2001) evaluated the policy issues, such as taxing chemicals to reduce environmental contamination and maintenance of forests through subsidies. The SARP and SYSNET projects of IRRI, involved scientists from India, Malaysia, Vietnam and the Philippines to develop and evaluate methodologies and tools for land use analysis and apply them at regional levels to support agricultural and environmental policy making. The land units of India were defined by agro-ecological zones based on soil and weather characteristics and WTGROW and CERES-RICE models were used to explore possible combinations of integrated farming to achieve the goals of maximizing food, minimizing water use and controlling environmental degradation and soil salinity (Aggarwal 2000).

The researchers and the decision makers have begun to apply the results of crop simulation models to strategic policy analysis. The integration of crop simulation models with GIS and expert systems facilitated it as a useful tool for investment decisions. Beinroth et al. (1998) described the development and use of AEGIS (Agricultural and Environmental GIS) for application with DSSAT type models in land use analysis, as a part of rural development in Columbia. The outputs from crop models were used as inputs to other models of second order effects. Other examples in India (Singh and Thornton 1992) and Putero Rico (Hanson et al. 1999) are also reported. Parry et al. (1985) used CERES models to study the first-order effects of climate on cereal yields. Then, the farm level profitability was investigated as a second order effect of the climate induced yield change by balancing the gross return per unit of production. The implications of changes in crop yields and production for agricultural policy were examined both at the national and international levels. The crop models in combination with spatial analysis tools have the potential to develop rural poverty reduction strategies and evaluate changes in government support programs in different agro-climatic zones. The use of crop simulation models in this way could help to reduce poverty in Kenya (McCown et al. 1994). Due to increasing population pressure, nutrient depletion, soil degradation, low crop yields, and income reduction, were described as a spiraling "poverty trap." The use of a crop simulation model, complimented by a small set of on-farm trials, showed that a use of small amount of fertilizer was an efficient strategy to break the poverty cycle. As the farmers in that area never used fertilizer for crop production, extensive field research conducted past several years did not consider this as an option.

There are many potential uses of crop simulation models to support strategic policy decisions at the regional as well as national level (Anbumozhi et al. 2003). The policy makers and aid agencies like World Bank can greatly benefit from the use of crop simulation models in evaluating the type of interventions, including the conservation of the natural resource base. Some specific areas where the systems analysis will help in this endeavor are (i) to assess the changes in natural resource base because of new policies; (ii) to evaluate advantages and disadvantages of different policy packages, such as changing cropping pattern, shifting production basins, etc; (iii) to analyze farmer responses to policy changes; and (iv) to design new policies based on sustainable rural development by determining acceptable level of trade-off between development and natural resource depletion. Any well informed decision for natural resource management has the potential to reduce rural poverty and for this purpose, crop simulation models will be of high relevance and useful for developmental assistance programs.

3.14 Integration of Climate Prediction and Agricultural Models

The interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modeling may help to address some obstacles to the effective agricultural use of climate information. Firstly, the modeling can address the mismatch between farmers' needs and available operational forecasts. The probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Secondly, the credible evidence of livelihood benefits, using integrated climate-crop-economic modeling in a value-of information framework, may assist in the challenge of obtaining institutional, financial and political support; and targeting for greatest benefit. Thirdly, the integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors and enhance site-specific interpretation of recommendations when driven by spatial data. The modelbased "discussion support systems" contribute to learning and farmer-researcher dialogue. The integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arise from model-based decision analysis.

3.15 Pertinent Issues to be Considered while Using Simulation Models

While interpreting results from the scenarios predicted by the GCMs, some considerations are necessary. The most significant limitations are their poor resolution, inadequate coupling of atmospheric and oceanic processes, poor simulation of cloud processes and inadequate representation of the biosphere and its feedbacks. The poor resolution is likely to be significant in north-eastern parts of India where the relief is varied and local climate may be quite different from the average across the area used by a GCM. Most GCMs have difficulty in even describing the current climate adequately (Bachelet et al. 1995). The current GCMs are able to predict neither the changes in the variability of the weather nor the frequency of catastrophic events, such as hurricanes, floods or even the intensity of monsoons, all of which can be important in determining crop yields as the average climatic data. The GCMs can, at best, be used to suggest the likely direction and rate of change of future climates.

According to Long et al. (2005), fertilization effect of CO2 has probably been overestimated. The omission of O3 effects from the most models could have led to 20% overestimation of crop production in the Northern Hemisphere. The database of chamber studies is the mechanistic basis for crop yield models. Hence, these models overestimate the yield gain due to elevated CO2 compared to those observed under fully open-air condition (FACE) experiments in the field. The current FACE experiments are, however, not adequate enough to reparameterize the existing models (Long et al. 2005). In a recent study, Bannyayan et al. (2005) evaluated ORYZA 2000 (Bouman and Van Laar 2006) against the observed growth and yield of rice in a 3-year field experiment in Japan where rice plants were subjected to the elevated CO2 in FACE under varying N fertilizer rates. The simulation results showed that the model overestimated the increases in green leaf area indices due to the elevated CO2 concentration, but the enhancement of total biomass was only a minor overesti-mation. While the model was successful in simulating the increase in rice yield due to the CO2 enrichment, it failed to reproduce the observed interaction with N in the rice yield response to elevated CO2 concentration. The lack of complete understanding of the effects and the potential interactions of environment variables on plant processes preludes the definitive predictions of the effects of global climate change. Despite the limitations imposed by the assumptions made in both the GCM and the crop simulation models, the models provide significant progress in our understanding of how future climates are likely to affect crop production. Nevertheless, the use of simulation models to predict the likely effects of climate change on crop production is an evolving process.

3.16 Future Research Priorities Using Simulation Studies

Assessment on agriculture and policy response to manage climate change impacts will not be complete unless the biophysical, environmental and socio-economic sectors of agro-ecosystems are studied together. The global integrated impact assessment models provide such a framework, but they are inadequate for regional policy planning. Often, these are not validated at that scale due to their inherent inter-and intra-sectoral conflicts. There is an urgent need to develop the integrated assessment simulation models in which cropping systems; water use and socio-economic parameters are brought together for assessing the impact of climate change in diverse regions of the country. The collaboration with several stakeholders including policy makers, agricultural and environmental scientist, climatologist, economist, administrators, industry and farmers organization, is very much essential. In future studies, unless the uncertainties and limitations discussed above are considered in the crop simulation modeling and climate change scenarios, the assessment of climate change on agriculture cannot provide sound basis for regional policy planning.

3.17 Conclusion

The crop simulation models offer many opportunities and can enhance natural resource management decisions in several ways. At the field level, these models can investigate the long-term changes in the environmental quality of air, soil and water, and yield stability. At the farm level, the applications of the models can include selection of new cropping systems that adapt to micro-climate change, socio-economic viability, and analysis of yield gaps between experimental stations and field production. At the regional level, these models allow the aggregation of crop production responses to various environmental changes.

Many uncertainties exist in modeling studies, partly due to the quality of the predictions by the models, from the use of limited sites for which historical weather are available, due to the quality of the crop simulation models, especially when applied under the rain-fed conditions (Bachelet et al. 1995), and due to the quality of the climate models used to predict future weather scenarios. These uncertainties may be reduced only when a large number of scenarios for different locations are compared and evaluated. Improvement in models has been a continuous process and more scientific understanding is needed to deal with sensitivity of crop production to dynamic changes taking place in the natural resource base. The global, regional and local information sharing can be highly complementary and the information generated from such efforts will serve as a sound basis to make refined models, to develop policy interventions, and to attain food security at the regional as well as the national level.

References

Abrol YP, Bagga AK, Chakravorty NVK, Wattal PK (1991) Impact of rise in temperature on the productivity of wheat in India, In: Abrol YP et al. (eds) Impact of Global Climate Changes in Photosynthesis and Plant Productivity, New Delhi, pp 787-789 Achanta AN (1993) An assessment of the potential impact of global warming on Indian rice production, In: Achanta AN (ed) The Climate Change Agenda: An Indian Perspective, TERI, New Delhi

Acock B, Acock M (1991) Potential for using long-term field research data to develop and validate crop simulators. Agro J 83:56-61 Aggarwal PK (2000) Application of systems simulation for understanding and increasing yield potential of wheat and rice. Ph.D. Thesis, Wageningen University, The Netherlands, p 176 Aggarwal PK (2003) Impact of climate change on Indian agriculture. J Plant Biol 30(2):189-198 Aggarwal PK, Banerjee B, Daryaei MG, Bhatia A, Bala A, Rani S, Chander S, Pathak H, Kalra N (2006a) InfoCrop: A dynamic simulation model for the assessment of crop yield, losses due to pest, and environmental impact of agro-ecosystem in tropical environments: II. Performance of the Model. Agric Syst 89:47-67 Aggarwal PK, Kalra N (1994) Simulating the effect of climatic factors, genotype and management on productivity of wheat in India, Indian Agricultural Research Institute Publication, New Delhi, India, p 156

Aggarwal PK, Kalra N, Chander S, Pathak H (2006b) InfoCrop: A dynamic simulation model for the assessment of crop yield, losses due to pest, and environmental impact of agro-ecosystem in tropical environments: I. Model Description Agric Syst 89:1-25 Aggarwal PK, Kropff MI, Cassman KG, Ten Berge HFM (1997) Simulating genetic strategies for increased yield potential in irrigated. Trop Environ Field Crops Res 51:5-18 Aggarwal PK, Mall RK (2002) Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Clim Change 52(3):331-343

Aggarwal PK, Sinha SK (1993) Effect of probable increase in carbon dioxide and temperature on productivity of wheat in India. J Agric Meteorol 48(5):811-814

Anbumozhi VR, Reddy E, Lu Y, Yamaji E (2003) The Role of simulation models in agricultural research and rural development: A review. Int Agric Eng J 12:1-18 Attri SD, Rathore LS (2003) Simulation of impact of projected climate change on wheat in India.

Int J Climatol 23:693-705 Bachelet D, Kern J, Tolg M (1995) Balancing the rice carbon budget in China using spatially-

distributed data. Ecol Model 79(1/3):167-177 Baker JT, Allen Jr LH (1993) Effects of CO2 and temperature on rice: A summary of five growing seasons. J Agric Meteoro 48(5):575-582 Bannyayan M, Kobayashi K, Kim H, Lieffering M, Okada M, Mirza S (2005) Modelling the interactive effects of atmospheric CO2 and N on rice growth and yield. Field Crops Res 93:237-251 Beinroth FH, Jones JM, Knapp EB, Papajorgji P, Luyten J (1998) Evaluation of land resources using crop models and a GIS. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding Options for Agricultural Production. Systems Approaches for Sustainable Agricultural Development. Kluwer, Dordrecht, pp 293-311 Bhaskaran B, Mitchell JFB, Lavery JR, Lal M (1995) Climatic response of Indian subcontinent to doubled CO2 concentrations. Int J Climatol 15:873-892 Bouman BAM, Van Laar HH (2006) Description and evaluation of rice growth model ORYZA

2000 under nitrogen limited conditions. Agric Syst 87:249-273 Chatterjee A (1998) Simulating the impact of increase in temperature and CO2 on growth and yield of maize and sorghum, M.Sc. Thesis (Unpublished), Indian Agricultural Research Institute, New Delhi

Claire (1996) Climate change and agriculture in Europe: Assessment of impacts and applications. In: Harrison P, Butterfield R, Downing T (eds) Research Report No. 9, Environmental Change Unit. University of Oxford, p 411 Delecolle R, Ruget F, Ripoche D, Gosse G (1996) Possible effects of climate change on wheat and maize crops in France. Climate Change and Agriculture: Analysis of Potential International Impacts. ASA Special Publications 59:241-257 Francis M (1999) Simulating the impact of increase in temperature and CO2 on growth and yield of rice. M.Sc. Thesis (Unpublished), Indian Agricultural Research Institute, New Delhi Gangadhar Rao D, Katyal JC, Sinha SK, Srinivas K (1995) Impacts of climate change on sorghum productivity in India: Simulation study. Am Soc Agro, 677S. Segoe Rd., Madison, WI 53711, USA, Climate Change and Agriculture: Analysis of Potential Internatinoal Impacts. ASA Spa-cial Publ No 59, pp 325-337 Gangadhar Rao D, Sinha SK (1994) Impact of climate change on simulated wheat production in India. In: Rosenzweig C, Iglesias I (eds) Implications of Climate Change for International Agriculture: Crop Modelling Study. USEPA230-B-94-003. USEPA, Washigton, DC, pp 1-17 Godwin D, Ritchie JT, Singh U, Hunt L (1989) A User's Guide to CERES-Wheat- V2.10, Muscle

Shoals: International Fertilizer Development Center Hanson JD, Rojas KW, Shaffer MJ (1999) Calibrating the root zone water quality model. Agro J 91:171-177

Hundal SS, Kaur P (1996) Climate change and its impact on crop productivity in the Punjab, India. In: Abrol YP, Gadgil G, Pant GB (eds) Climate Variability and Agriculture. New Delhi, India, p 410

IPCC (Intergovernmental Panel on Climate Change) (2001) Third Assessment Report of the Intergovernmental Panel on Climate Change: The Scientific Basis (Working Group I). Cambridge University Press, United Kingdom and New York, NY, USA, p 881 IPCC (Intergovernmental Panel on Climate Change) (2007) Fourth Assessment Report of the Intergovernmental Panel on Climate Change: The Impacts, Adaptation and Vulnerability (Working Group III). Cambridge University Press, United Kingdom and New York, NY, USA Jansen HGP (2001) A decade of interdisciplinary land use research in Costa Rica by the Research Program on Sustainability in Agriculture (REPOSA): Achievements and lessons. In: Proceedings - Third International Symposium on Systems Approaches for Agricultural Development, Lima, 8-10 November 1999. International Potato Centre (CIP), Lima

Knutson TR, Delworth TL, Dixon KW, Stouffer RJ (1999) Model assessment of regional surface temperature trends (1949-97). J Geophys Res 104:30981-30996 Korner C, Bazzaz FA (eds) (1996) Carbon Dioxide, Populations, and Communities. Academic

Press, San Diego, California, USA, p 465 Krishnan P, Surya Rao AV (2005) Effects of Genotypic and Environmental on Seed Yield and

Quality of rice. J Agric Sci (Cambridge) 143:283-292 Krishnan P, Swain DK, Baskar C, Nayak SK, Dash RN (2007) Simulation studies on the effects of elevated CO2 and temperature on rice yield in Eastern India. Agric Ecosys Environ 122(2):233-242

Kropff MJ, van Laar HH, Matthews RB, Goudriaan J, Berge ten HFM (1994) Description of the Model ORYZA1 (Version 1.3). In: Kropff MJ, Van Laar HH, Matthews HH (eds) An Ecophys-iological Model for Irrigated Rice Production, SARP Research Proceedings. IRRI, Los Banos, Philippines, pp 5-41

Lal M, Nozawa T, Emori S, Harasawa H, Takahashi K, Kimoto M, Abe-Ouchi A, Nakajima T, Takemura T, Numaguti A (2001) Future climate change: Implications for Indian summer monsoon and its variability. Curr Sci 81:1196-1207 Lal M, Singh KK, Srinivasan G, Rathore LS, Naidu D, Tripathi CN (1999) Growth and yield response of soybean in Madhya Pradesh, India to climate variability and change. Agric For Meteorol 93:53-70

Lal M, Singh KK, Srinivasan G, Rathore LS, Saseendran AS (1998) Vulnerability of rice and wheat yields in NW-India to future change in climate. Agric For Meteorol 89:101-114 Li C, Li C, Mosier A, Wasmann R, Cai Z, Zheng X, Huang Y, Tsuruta H, Boonjawat J, Lantin R (2004) Modeling greenhouse gas emissions from rice-based production systems: Sensitivity and upscaling. Global Biogeochem Cycles 18:GB1043, doi:10.1029/2003 GB002045 Long SP, Ainsworth EA, Leakey ADB, Morgan PB (2005) Global food insecurity. Treatment of major food crops with elevated carbon dioxide or ozone under large-scale fully open-air conditions suggests recent models may have over estimated future yields. Phil Trans R Soc B 360:2011-2020

Mall RK, Aggarwal PK (2002) Climate change and rice yields in diverse agro-environments of

India. I. Evaluation of impact assessment models. Clim Change 52(3):315-331 Mall RK, Lal M, Bhatia VS, Rathore LS, Singh R (2004) Mitigating climate change impact on soybean productivity in India: A simulation study. Agric For Meteorol 121:113-125 Mandal N (1998) Simulating the impact of climatic variability and climate change on growth and yield of chickpea and pigenonpea crops. M.Sc. Thesis (Unpublished), Indian Agricultural Research Institute, New Delhi Mathauda SS, Mavi HS (1994) Impact of climate change in rice production in Punjab, India. In

Climate Change and Rice Symposium, IRRI, Manila, Philippines Matthews R, Wassmann R (2003) Modelling the impacts of climate change and methane emission reductions on rice production: A review. Europ J Agron 19:573-598 May W (2002) Simulated changes of the Indian summer monsoon under enhanced greenhouse gas conditions in a global time-slice experiment. Geophys Res Lett 29(7):22.1-22.4 McCown RL, Coc PG, Keating BA, Hammer GL, Carberry PS, Probert ME, Freebairn DM (1994) The development of strategies for improved agricultural system and land use management. In: Goldsworthy P, Penning de Vries FWT (eds) Opportunities, Use and Transfer of Systems Research Methods to Developing Countries. Systems Approaches for Sustainable Agricultural Development. Kluwer, Dordrecht, pp 81-96 Mearns LO, Hulme M, Carter TR, Leemans R, Lal M, Whetton P (2001) Climate Scenario Development, Chapter 13 in Climate Change 2001: The Scientific Basis, Contribution of WGI to the Third Scientific Assessment Report of Intergovernmental Panel on Climate Change (WMO/UNEP), pp 739-768 Mitchell JFB, Johns TC, Senior CA (1998) Transient response to increasing greenhouse gases using models with and without flux adjustment. Hadley Centre Technical Note 2. Available from Met. Office, London Road, Bracknell, RG12 2SZ, UK

Mohandass S, Kareem AA, Ranganathan TB, Jeyaraman S (1995) Rice production in India under current and future climates. In: Matthews RB, Kropff MJ, Bachelet D, Laar van HH (eds) Modeling the Impact of Climate Change on Rice Production in Asia. CAB International, UK, pp 165-181

Parry ML, Carter TR, Konjin NT (1985) Climate change, how vulnerable is agriculture? Environment 27:4-5

Rimmington GM, Charles-Edwards DA (1987) Mathematical descriptions of plant growth and development. In: Wisiol K, Hesketh JD (eds) Plant Growth Modeling for Resource Management: Current Models and Methods. CRC Press, Boca Raton, 1:3-15 Ritchie JT, Alagarswamy G (1989) Modelling the growth and development of Sorghum and Pearl

Millet. ICRISAT Res Bull 12:24-26 Rosenzweig C, Hillel D (1998) Climate Change and the Global Harvest: Potential Impacts of the

Greenhouse Effect on Agriculture. Oxford University Press, New York, p 324 Rupakumar K, Ashrit RG (2001) Regional aspects of global climate change simulations: Validation and assessment of climate response over Indian monsoon region to transient increase of greenhouse gases and sulphate aerosols. Mausam 52:229-244 Rupakumar K, Kumar K, Prasanna V, Kamala K, Desphnade NR, Patwardhan SK, Pant GB (2003) Future climate scenario. In: Climate Change and Indian Vulnerability Assessment and Adaptation. Universities Press (India) Pvt Ltd, Hyderabad, p 462 Russell G, Rind D (1999) Response to CO2 transient increase in the GIS coupled model - Regional cooling in a warming climate. J Clim 12:531-539 Saarikko Riitta A, Carter Timothy R (1996) Estimating the development and regional thermal suitability of spring wheat in Finland under climatic warming. Clim Res 7:243-252 Sahoo SK (1999) Simulating growth and yield of maize in different agro-climatic regions. M.Sc.

Thesis (Unpublished), Indian Agricultural Research Institute, New Delhi Saseendran ASK, Singh KK, Rathore LS, Singh SV, Sinha SK (2000) Effects of climate change on rice production in the tropical humid climate of Kerala, India. Clim Change 44:495-514 Selvarajan S, Aggarwal PK, Pandey S, Lansigan FP, Bandyopadhyay SK (1997) Systems approach for analyzing tradeoffs between income, risk and water use in rice-wheat production in northern India. Field Crops Res 51(1-2):147-161 Schipper RH, Jansen GP, Bouman BAM, Hengsdijk H, Nieuenhuyse A, Saenz F (2001) Integrated bio-economic land-use models: An analysis of policy issues in the Atlantic Zone of Costa Rica. In: Lee DR, Barrett CB (eds) Trade offs or Synergies? Agricultural Intensification, Economic Development, and the Environment. CAB International, Wallingford, UK, pp 267-284 Singh U, Ritchie JT, Godwin DC (1993) A Users Guide to CERES-Rice V2.10, Simulation manuaIIFDC-SM-4, IFDC, Muscle Shoals, Al, USA, p 131 Singh U, Thornton PK (1992) Using crop models for sustainability and environmental quality assessment. Outlook on Agriculture 21:209-218 Sinha SK, Swaminathan MS (1991) Deforestation climate change and sustainable nutrients security. Clim Change 16:33-45 Stephenson DB, Douville H, Rupa Kumar K (2001) Searching for a fingerprint of global warming in the Asian summer monsoon. Mausam 52:213-220 Swain D, Chandrabaskar B, Krishnan P, Rao KS, Nayak SK, Dash RN (2006) Variation in yield, N uptake and N use efficiency of medium and late duration rice varieties. J Agric Sci (Cambridge) 144(1):69-83

Swain D, Heathi S, Chandrabaskar B, Krishnan P, Rao KS, Nayak SK, Dash RN (2007) Developing ORYZA 1N for medium- and long-duration rice: Variety selection under non-water-stress conditions. Agro J (US) 99:428-440 Uprety DC, Chakravarty NVK, Katiyal RK, Abroal YP (1996) Climate variability and Brassica.. In: Abrol YP, Sulochana Gadgil, Pant GB (eds) Climate Variability and Agriculture. Narosa Publishing House, New Delhi, India Whisler FDB, Acock DN, Baker RE, Fye HF, Hodges JR, Lambert HE, Lemmon JM McKinion, Reddy VR (1986) Crop simulation models in agronomic systems. Adv Agro 40:141-208

Was this article helpful?

0 0
Renewable Energy Eco Friendly

Renewable Energy Eco Friendly

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.

Get My Free Ebook


Post a comment