During the Kharif season of 2023, the Marathwada region faced a significant mid adverse drought marked by a prolonged dry spell in August. Accordingto theGovernmentof India's drought manual,are spell acting 21 days during the critical growing period of Kharif crops qualifies an area for mid adverse drought compensation under the Pradhan Mantri FasalBima Yojana (PMFBY) scheme.

To assess the impact of this dry spell, a comprehensive analysis was conducted. This analysis evaluated the extent of crop damage and loss caused by the adverse climatic conditions, particularly focusing on the affected regions within Marathwada. Utilizing the guidelines of PMFBY, which includeobjectivecriteriasuchasNDVI-basedassessmentsandcropyielddata, compensation was meticulously calculated and disbursed to eligible farmers.

The crop coefficient (Kc) values provided are essential for estimating the water requirement of soybean crops during different growth stages in the Marathwada region. Here’s how these values are typically used :

Initial Stage(1-28days) : Kc=0.64

This stage covers the early growth phase ofsoybean plants when they are Establishing root systems and initial vegetative growth is occurring.

Mid Stage (29-84 days) : Kc = 1.31

The mid stage represents the period of active vegetative growth and flowering in soybean plants. This is when the crop is utilizing more water to support biomass production and reproductive processes.

EndStage(85-110days) : Kc=0.66

During the end stage, soybean plants are nearing maturity, and there is a reduction in vegetative growth. The crop is focusing more on seed development and filling.

Application in Estimating Crop Water Requirement:

To estimate the crop water requirement (ETc) at any given time during the growing season, the Kc value corresponding to the growth stage is multiplied by the reference evapotranspiration (ETo) for the location.

ETo is typically estimated using weather data and represents the evaporative demand of the atmosphere.

ETc=Kc*ETo

Significance for Marathwada:

Marathwada region experiences specific climatic conditions that influence crop water needs. These Kc values are tailored for local conditions, helping farmers and policymakers make informed decisions regarding irrigation scheduling and water management practices.

Using these Kc value sensures that soybean crops receivead equate water during critical growth stages, optimizing yield potential while conserving water resources

The crop coefficient (Kc) values provided are crucial for estimating thewater requirements of Bt. cotton crops during various growth stages across Maharashtra. Here’s a breakdown of how these Kc values are typically applied :

InitialStage(1-30days):Kc=0.51

This stage corresponds to the earlydevelopment phase of Bt. cotton, where the plants are establishing and undergoing initial vegetative growth.

MidStage(31-111days): Kc=1.24

The mid stage covers the period of active vegetative growth and flowering in Bt. cotton. This is when the crop requires the highest amount of water for biomass production and reproductive processes.

EndStage(112-164days):Kc=0.95

During the end stage, Bt. cotton plants are maturing, and there is a decrease in vegetative growth. Water requirement decreases as the crop transitions to focusing on boll development and maturation.

ApplicationinEstimatingCropWater Requirement:

To calculate the crop water requirement (ETc) at any given time during the growing season, multiply the Kc value corresponding to the growth stage bythe reference evapotranspiration (ETo) for the location.

ETo represents the evaporative demand of the atmosphere and is typically estimated using weather data.

ETc=Kc*ETo

Significancefor Maharashtra:

Maharashtra’s diverse agro-climatic zones influence Bt. cotton water requirements. These specific Kc values are tailored for the state's conditions, aiding farmers and agricultural authorities in efficient irrigation management and water resource planning.

By using these Kcvalues, stake holder scan optimize water use efficiency, enhance crop productivity, and mitigate risks associated with water stress during critical growth stages of Bt. cotton.

Ifthesoilmoistureisreducedduetodryspell,theyieldofsoybeancropis decreases as follows this is recommended for Marathwada region

6. For improved sugarcane yield prediction, we recommend integrating optical (Sentinel-2) and SAR (Sentinel-1) data using advanced machine learning techniques like Bayesian methods, bagging, boosting, and model stacking. This approach can effectively predict biomass, even when optical data is unavailable due to cloud cover. The model demonstrated good accuracy in predicting sugarcane yield at the micro-zone level in Maharashtra, with forecasts available 1-2 months before harvest, helping sugar mills plan better for regional trading.