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Groundwater is the most preferred source of water resource for the human needs. Over-exploitation of the groundwater has been led to the tremendous effect on the groundwater drought. Assessment of groundwater drought is difficult to understand due to its complexity, non-linearity feature. In this study, groundwater drought indices of the state of Bihar, India, have been modeled using machine learning technique. The prediction of SGI was done by using Artificial Neural Network (ANN) and Random Forest (RF) machine learning models. The best input combinations of Gravity Recovery and Climate experiment (GRACE) satellite water equivalent data, rainfall data and groundwater level data was used to predicted the SGI. In this study, SGI of 38 districts of Bihar was calculated using groundwater data from 2002 to 2019. The accuracy and efficiency of the RF and ANN models were measured based on the mean square error (MSE) and correlation coefficient value (r). Compared to two models are shown the RF model is a performs superior as compare to ANN model, which model superior is decided based on the correlation coefficient value (r) as 0.95 and MSE value of 0.11. The results show both ML models such as ANN and RF is showed best results with the input combination of GRACE satellite water equivalent data, rainfall and groundwater data. • Estimation of GW drought indices using ML techniques of Bihar, India. • ANN and RF models were used to predict SGI. • GRACE, rainfall and GW level data were used to predict the SGI. • SGI of 38 districts of Bihar was calculated using the GW data from 2002 to 2019. [ABSTRACT FROM AUTHOR]

• We present impact estimates of the National Dairy Plan-I on household and member welfare. • The study uses experimental and survey data from 28 villages and 7 sub-districts from Bihar, India • The market based interventions with links to value chain use two channels: income & female agency. • The income channel dhousehold outcomesbut didn't improve female & child outcomes. • Female agency channel magnified household welfare & extended program benefits to women & children. We combine the results of a laboratory experiment and survey of agricultural households to estimate the welfare impacts of a market-based intervention with links to value-chain. We investigate whether increased participation by women in such value-chains improves their relative bargaining power and therefore their ability to contribute to household welfare. We utilize the National Dairy Plan-I as an example to estimate pathways through which such interventions may affect household decision-making. We find that the program design significantly increased women's relative bargaining power within the household, which acts as an important channel for enhancing women's ability to contribute to household welfare through decision-making processes related to food, nutrition, branded food items, and child education. The instrumental variable estimates show that if value-chains are gender-neutral then direct program effects are significant but small. Participation in National Dairy Plan-I, on the other hand, improved women's relative bargaining power, allowing them to make substantial contribution to welfare. We show that when women's bargaining power mediates participation in value-chains, the nutrition elasticity rises from 0.26 to 0.94. While the impact on analytical ability (i.e., mathematics Z-score) is negligible in the absence of female agency, performance improves by 0.35σ when gendered element(s) of the program are allowed to act as a channel. [ABSTRACT FROM AUTHOR]