An operational medium range local weather forecasting system developed in India

A forecasting system for objective medium range location specific forecasts of surface weather elements was evolved at the National Centre for Medium Range Weather Forecasting (NCMRWF). The basic information used for this is the output from a general circulation model (GCM). The two essential components of the system are statistical interpretation (SI) forecast and direct model output (DMO) forecast. These are explained in brief. The SI forecast is obtained by using dynamical-statistical methods like model output statistics (MOS) and the perfect prog method (PPM) in which prediction of upper air circulation from a GCM around the location of interest is used. The DMO forecast is obtained from the prediction of surface weather elements from the GCM. The procedure for preparation of final forecast by using these two components and prevailing synoptic conditions is also explained. This is essentially a man-machine-mix approach. Finally, an evaluation of the forecast skill for the 1996 monsoon and some of the future plans are presented. Copyrigh

Optimum sowing dates for soybean in Central India using CROPGRO and ClimProb symbiosis

The optimum sowing dates for soybean cv. Gaurav were derived for Jabalpur, Raipur and Gwalior in the state of Madhya Pradesh in central India. Dates were derived based on two strategies: (a) probabilities of rainfall and temperature events using ClimProb, a PC based software package, and (b) the CROPGRO Soybean v3.0 crop growth simulation model. In Madhya Pradesh, the optimum sowing dates for multiple cropping, with the first crop as soybean under rainfed conditions, are between weeks 25 and 27, while the optimum sowing dates for rainfed mono-cropping are between weeks 28 and 29

Evaluation of the CERES-Rice version 3.0 model for the climate conditions of the state of Kerala, India

The CERES-Rice version 3.0 crop growth simulation model was calibrated and evaluated for the agroclimatic conditions of the state of Kerala in India. Genetic coefficients were developed for the rice crop variety Jaya and used for the model evaluation studies. In four experiments using different transplanting dates during the virippu season (June to September) under rainfed conditions (i.e. no irrigation), the flowering date was predicted within an error of four days and date of crop maturity within an error of two days. The model was found to predict the phenological events of the crop fairly well. The grain yield predicted by the model was within an error of 3 for all the transplanting dates, but the straw yield prediction was within an error of 27. The high accuracy of the grain yield prediction showed the ability of the model to simulate the growth of the crop in the agroclimatic conditions of Kerala. It can be concluded from this study that the model can be used for making various strategic and tactical decisions related to agricultural planning in the state

Biophysical interactions in tropical agroforestry systems

sequential systems, simultaneous systems Abstract. The rate and extent to which biophysical resources are captured and utilized by the components of an agroforestry system are determined by the nature and intensity of interac-tions between the components. The net effect of these interactions is often determined by the influence of the tree component on the other component(s) and/or on the overall system, and is expressed in terms of such quantifiable responses as soil fertility changes, microclimate modification, resource (water, nutrients, and light) availability and utilization, pest and disease incidence, and allelopathy. The paper reviews such manifestations of biophysical interactions in major simultaneous (e.g., hedgerow intercropping and trees on croplands) and sequential (e.g., planted tree fallows) agroforestry systems. In hedgerow intercropping (HI), the hedge/crop interactions are dominated by soil fertility improvement and competition for growth resources. Higher crop yields in HI than in sole cropping are noted mostly in inherently fertile soils in humid and subhumid tropics, and are caused by large fertility improvement relative to the effects of competition. But, yield increases are rare in semiarid tropics and infertile acid soils because fertility improvement does not offse

Operational model for forecasting location specific quantitative precipitation and probability of precipitation over India

The National Centre for Medium Range Weather Forecasting was established in early 1988 with the major objective to develop operational medium range weather forecasting capability and agricultural meteorological advisory services (AAS) for each of the 127 agricultural climatic zones for the farming community in India. At present, medium range weather forecast of six surface weather parameters namely, average cloud cover, 24 h accumulated precipitation, average wind speed, predominant wind direction, maximum/minimum temperature trends (up to 4 days) is provided to 83 units in different agricultural climatic zones. In addition the forecast of weekly cumulative rainfall is also provided. An objective system for obtaining location specific forecast, in the medium range, of surface weather elements is evolved at NCMRWF. The basic information used for this is the output from the general circulation model (GCM). A T80L18 model operational at the centre since 1994 has been recently upgraded to a T170L28 model. However, it is well known that in spite of higher resolution, the global models are unable to account for the small-scale effects (e.g. of topography, local environmental features) important in predicting surface weather parameters like rainfall, temperature etc. This necessitates the development of statistical-dynamical models. Hence an operational system for forecasting rainfall (quantitative, probability of precipitation (PoP)) has been developed at the centre and implemented since 1994. A Perfect Prog Method (PPM) approach is followed for statistical interpretation (SI) of Numerical Weather Prediction (NWP) products. PPM model equations are developed by using analysis data obtained from European Centre for Medium Range Weather Forecasts (ECMWF) for a period of six years (1985-1990). Rainfall forecasts are subsequently obtained from these equations by using T80 model output. A comparative study of the skill of SI forecast and the direct model output (DMO) forecast has indicated that SI forecast improves over the DMO considerably and hence can be developed as a fully automatic operational weather forecasting system

Mitigating climate change impact on soybean productivity in India: A simulation study

Field experiments with soybean were conducted over a period of 1990-1998 in diverse Indian locations ranging in latitude, longitude, and elevation. These locations provided a wide range of environments for testing and validation of the crop growth (CROPGRO) model considered in this study with observed changes in soils, rainfall and other weather parameters. Model predicted satisfactorily the trends of days to flowering, maturity and grain yields. The deviations of simulated results were within ±15 of the measurements. Validated CROPGRO model has been used to simulate the impact of climate change on soybean production in India. The projected scenarios for the Indian subcontinent as inferred from three state-of-the-art global climate models (GCMs) have been used in the present study. There was a decrease (ranging between about 10 and 20) in soybean yield in all the three future scenarios when the effect of rise in surface air temperature at the time of the doubling of CO2 concentration was considered. The results obtained on the mitigatory option for reducing the negative impacts of temperature increases indicate that delaying the sowing dates would be favorable for increased soybean yields at all the locations in India. Sowing in the second season would also be able to mitigate the detrimental effects of future increases in surface temperature due to global warming at some locations

Record of insect pollinators on the inflorescence of Murraya koenigii in Rajasthan

Observations were recorded on the insect pollinators visiting the blossom of Murraya Koenigii in April-May. Flowers are white, funnel shaped with terminal cymes. Flower serves as nectar and pollen resource for the insect pollinators. Three species of order Lepidoptera and five species of order hymenoptera were recorded during the present study