Products/ Agromet products for Agromet Advisory Services

Spatial Daily, Weekly, Fortnightly, Monthly and Seasonal Weather Parameters

Weather information at different temporal and spatial scale is useful for preparation of Agromet Advisories. In view of that spatial Daily, Weekly, Fortnightly, Monthly and Seasonal scales are being generated utilizing the realized weather observations for the parameters Temperature (Maximum temperature, Minimum temperature, Diurnal temperature), Maximum and Minimum Relative Humidity, Cloud and Wind speed. Synoptic observatory data received in GTS is used to generate maps.

Daily Spatial Rainfall

Gridded rainfall data generated daily over a country at a grid resolution ° of measured rainfall from the large number rain-gauge stations distributed over the country is interpolated and spatial rainfall is generated..

Agromet Products

Realized & forecast Soil Moisture. Mode

Soil moisture information is used in preparation of agromet advisories in respect of sowing, irrigation, mulching etc. Daily soil moisture is computed by Soil Water Balance (SWB) technique based on the method of Thornthwaite and Mather (1955). Spatial rainfall values extracted in GIS are used as data source to generate daily realized soil moisture. In addition to daily soil moisture data, cumulative difference between two consecutive days for soil moisture are also made.

The Normalized Difference Vegetation Index (NDVI) is an index of plant “greenness” or photosynthetic activity, and is one of the most commonly used vegetation indices. Many factors affect NDVI values like plant photosynthetic activity, total plant cover, biomass, plant and soil moisture, and plant stress. Weekly NDVI products are being operationally generated from NOAA VHRR. This dataset contains gridded daily Normalized Difference Vegetation Index (NDVI) derived from the NOAA Climate Data Record (CDR). The data are projected on a 0.05 degree x 0.05 degree global grid. In addition to country as a whole, NDVI is also extracted state-wise. Besides, difference of NDVI between the two successive weeks is also computed.

Soil moisture Forecast is made based on quantitative rainfall forecast. NWP Model output is used to generate soil moisture forecast Soil moisture information along with forecast can be used for irrigation scheduling, early warning of dry spells, and crop yield forecasting etc.

Satellite Product: Normalized Difference Vegetation INDEX (NDVI)

1. Standard Precipitation index: The Normalized Difference Vegetation Index (NDVI) is an index of plant “greenness” or photosynthetic activity, and is one of the most commonly used vegetation indices. Many factors affect NDVI values like plant photosynthetic activity, total plant cover, biomass, plant and soil moisture, and plant stress. Weekly NDVI products are operationally generated from satellite data. This dataset contains gridded daily Normalized Difference Vegetation Index (NDVI) .

2. Aridity Anomaly maps: Aridity Anomaly Map gives information about the moisture stress experienced by growing plant. This analysis would indicate qualitatively retardation in the plant’s growth and so poor yields. Indirectly, this may also be helpful for irrigation scheduling, the amount and the time at which the water is badly needed by the plant. The Aridity Index is worked out on weekly / biweekly basis

More Agromet products

1. Anomaly for weather parameters: Anomaly of different weather parameters from long term average arealso generated on different temporal and spatial scales.

2. Growing Degree days: : Growing degree days (GDD) is a weather-based indicator for assessing crop development. It is a calculation used as a measure of heat accumulation used to predict plant and pest development rates such as the date that a crop reaches maturity. GDD units can be used to: assess the suitability of a region for production of a particular crop; estimate the growth-stages of crops, weeds or even life stages of insects; predict maturity and cutting dates of forage crops; predict best timing of fertilizer or pesticide application; estimate the heat stress on crops; plan spacing of planting dates to produce separate harvest dates. These maps is prepared on weekly and seasonal scale. Maximum and minimum temperatures obtained from synoptic observations are used for computation of GDD.

3. Soil temperature& its anomaly at different depths
The soil layer of the earth is critical in maintaining plant life, offering mechanical support, and supplying water and nutrients. It affects plant growth directly, i.e. all crops practically slow down their growth. It is useful tool during germination and can also give information of soil borne pest and disease information.

4. Evaporation from Evaporation network and anomaly
Evaporation information can be useful to decide water management practices particularly in respect of irrigation scheduling) which is ultimately improve crop productivity. While plants need water to grow properly, water is often a scarce resource in many agricultural areas. By reducing the effects of evaporation through mulching etc., farmers can better utilize the water from rainfall and other natural sources and use less water while irrigating agricultural crops.

5. Standardized Precipitation Evapotranspiration Index (SPEI)
Evaporation information can be useful to decide water management practices particularly in respect of irrigation scheduling) which is ultimately improve crop productivity. While plants need water to grow properly, water is often a scarce resource in many agricultural areas. By reducing the effects of evaporation through mulching etc., farmers can better utilize the water from rainfall and other natural sources and use less water while irrigating agricultural crops.

Other Satellite derived Agromet Products

Satellite-based information are used widely in monitoring agriculture. Satellite image data have the potential to provide real-time analysis for large areas of attributes of a growing crop that can assist in making timely management decisions that affect the outcome of the current crop. Hence in addition to NDVI other siatellite products like Potential Evapotranspiration, Land Surface Water Index(LSWI), LST, Insolation and Soil wetness Index isalso used.

Drought Indices
Aridity anomaly index Aridity is the Thornthwaite’s concept to describe water deficiency experienced by plants. Thornthwaite gave the following formula for computing aridity index
AI = (PE – AE) / PE × 100PE denotes the water need of the plants (which is called potential evapotranspiration). AE denotes the actual evapotranspiration and (PE-AE) denotes the water deficit. PE is computed by Penman’s equation. AE is obtained from the water balance procedure which takes into account the water holding capacity of the soil at the place.

Anomaly of Aridity Index	Agricultural Drought Intensity
1 – 25	Mild
26 – 50	Moderate
> 50	Severe

Aridity Anomaly Map gives information about the moisture stress experienced by growing plant. This analysis would indicate qualitatively retardation in the plant’s growth and so poor yields. Indirectly, this may also be helpful for irrigation scheduling, the amount and the time at which the water is badly needed by the plant.

Monitoring Meteorological Drought using Standardized Precipitation Index (SPI)
Computation of SPI was done at a weekly and monthly time scale. The computation was carried out based on the two parameters gamma distribution function. The rainfall data were transformed into log normal values followed by computation of u statistics, shape and scale parameters of the gamma distribution. The resulting parameters were then used to find the incomplete gamma cumulative probability of an observed precipitation event. The incomplete gamma cumulative probability was then converted to gamma probabilities after including the occurrences of zero precipitation events. The gamma probabilities were transformed into standardized normal distribution using equi-probability transformation techniques (Abramowitz and Stegun, 1965).
Besides, more agromet products will be made available