Modelling dynamics of institutional credit to agriculture in India

Not AvailableCredit is considered as one of the most important and basic input in agricultural production process. The prime source of agricultural credit in India has drastically shifted from non-institutional (money lenders) to institutional source in the last five decades due to various policy initiatives of Government of India. Grass root level analysis of the dynamic helps in further policy framework. Hence in this study based on district wise average outstanding agricultural credit by scheduled commercial banks (SCBs) for the TE ending 2017-18, three districts from each state indicating high, medium and low exposure categories is selected using clustering technique. For these study districts outstanding agricultural credit by SCBs was extracted (1976-2017) and analysed. From the Bai-Perron test years viz., 1983, 1990, 1997, 2004 and 2011 are identified to be most common structural breaks in the time series data of each district owing to various policy reforms in the field of agricultural finance. Based on these breaks the time series further subdivided into six phases viz., phase-I (1976-1982), phase-II (1983-1989), phase-III (1990-1996), phase-IV (1997-2003), phase-V (2004-2010) and phase-VI (2011-2017). Phase-wise CAGR was calculated for all the districts and Garrett ranking technique is employed for further ranking of phases across six regions of the country. Phase-I is identified as the phase with high rate of growth in agricultural advances in selected districts across all regions except southern where it is ranked second. The policy initiatives of that period i.e. setting of priority sector lending targets and establishment of Regional Rural Banks have played crucial role in this growth phenomenon of agricultural advances. Further recent policies like doubling agricultural package and ground level credit policies have also played crucial role in the growth of agricultural advances at grass root level in all regions except eastern and north-eastern regions. Whereas in the eastern and north-eastern region districts the growth in initial phases was relatively better than in the recent phases indicating the effectiveness of initial policy measures in those regions. Institutional credit to agriculture is influenced by various drivers. Hence factors like number of scheduled commercial bank branches, share of GIA in GSA, share of AUC in GSA and annual rainfall are regressed on district wise outstanding agricultural credit by SCBs. To explore the variability panel dataset was created with the above mentioned variables and the impact of these important drivers on institutional credit to agriculture is quantified at different levels (region level, credit exposure category wise and at national level) by employing panel data regression technique. The consistency and suitability of fixed effect model over random effect model is highlighted by Hausman test. Number of operating branches in the district is one of the important variables with positive influence indicates the institutional credit to agriculture is found to be more responsive for branch expansion especially in Andhra Pradesh, Karnataka, Chhattisgarh, Tamil Nadu and Paducherry. In this study, an attempt was made to evaluate the performance of models like ARIMA, ARIMAX and ARIMA intervention on district level agricultural credit series. In the ARIMAX model number of SCB branches in the district is used as explanatory variable and in the ARIMA intervention model year 2004 is used as intervention point. District wise best model was identified and forecasted the institutional credit supply to agriculture at district level for the next five years. We have also made an attempt to estimate the direct credit requirement for agriculture of the district under certain assumptions. Short term and term credit requirement of the district is arrived separately by using the district level data on area under crops, scale of finance and unit cost. Term credit requirement of southern region districts like Guntur and Belgaum is relatively high and in districts of north eastern region viz, West Tripura and Papumpure it is very low. Hence there is need for counterproductive policy of first estimation of agricultural credit requirements depending on crop patterns and later meeting the requirements through effective policies.Not Availabl

Impact of conservation agriculture on humic acid quality and clay humus complexation under maize (Zea mays)-wheat (Triticum aestivum) and pigeon pea (Cajanus cajan)-wheat cropping systems

An attempt was made to study the humic acid (HA) quality and clay humus complex in order to generate valuable information regarding soil carbon (C) and recalcitrant carbon variations under conservation agriculture (CA) practices. It is worthwhile to mention that CA has got wider acceptance among researchers and farmers nowadays. A field experiment was conducted in an Inceptisol with three treatments, namely conventional tillage (CT), zero tillage (ZT) without residue and zero tillage with residue (ZT+R) in a maize (Zea mays L.)-wheat (Triticum aestivum L.) (M-W) and pigeon pea (Cajanus cajan L.)-wheat (P-W) cropping system at ICAR-Indian Agricultural Research Institute, New Delhi, with a view to characterize the HA by E4/E6 ratio and total acidity, and to specify the functional groups of clay humus complex. In ZT+R based treatments, lower E4/E6 ratio and total acidity of extracted HA showed higher degree of humification and stability of humic acid carbon (HA-C). The FTIR spectroscopy of the clay-humus complex (as extracted from soil) displayed the presence of a large number of functional groups in ZT+R treatment followed by ZT and CT. It was also observed that the yield of crops was also significantly higher in ZT+R than CT in both the cropping systems except in wheat crops in the M-W system. Therefore, it can be concluded that ZT+R has the potential to enrich the organic carbon (C) quality in soil and increase the aromaticity of HA, leading to carbon stabilization in soils

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The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304- 4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304- 4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.Not Availabl

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Not AvailableAgricultural commodity price is very volatile in nature due to its nonlinearity and nonstationary character. The volatility behaviour of the commodity price creates a lot of problems for both producer and consumer. The steady forecast of the price may reduce the problems and increase the profit for the stakeholders. In this study, an ensemble hybrid machine learning model based on empirical mode decomposition (EMD) has been proposed to forecast the commodity price. EMD decomposes the nonstationary and nonlinear price series into different stationary intrinsic mode functions (IMF) and a final residue. Then Machine learning techniques like Artificial neural network (ANN) and Support vector regression (SVR) were used to forecast each of the decomposed components. Finally, all the forecasted values of the decomposed components were aggregated to produce the final forecast. Two R modules were developed for the application of the proposed methodology. The proposed methodology has been applied to the monthly wholesale price index of vegetables. The results indicated that the ensemble hybrid machine learning model based on empirical mode decomposition has superior performance compared to generic modelsNot Availabl

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Not AvailableAccurate and timely price information and forecasting help in making efficient plans and strategies. Non-linearity and non-stationarity behaviour of price data create problems in price forecasting. In this paper, variational mode decomposition (VMD) based optimised genetic algorithm (GA) hybrid machine learning (ML) models have been proposed. The VMD algorithm is employed to decompose the price data into intrinsic mode functions (IMFs) which is further forecasted using ML models namely support vector regression (SVR) and random forest (RF). The practical use of the SVR and RF models is limited because the accuracy of ML models heavily depends on a proper setting of hyper-parameters. Therefore, these model hyper-parameters are optimized using GA. Further, the forecasted values of IMFs through the GA optimised SVR and RF are aggregated for the final forecast. The results of the proposed model are benchmarked with the comparative models. The proposed VMD-GA-RF and VMD-GA-SVR models are tested on the weekly onion price of the Delhi and Nashik market. The results clearly demonstrate that the combination of VMD and GA optimized models can improve the performance of the prediction of the dataset.Not Availabl

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Not AvailableThis cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.Not Availabl

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Not AvailableEMDSVRhybrid: Hybrid Machine Learning Model. Researchers can fit Empirical Mode Decomposition and Support Vector Regression based hybrid model for nonlinear and non stationary time series data using this packageNot Availabl

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Not AvailableThe researchers can use this package to fit Empirical Mode Decomposition and Artificial Neural Network based hybrid model for nonlinear and non stationary time series data. It will also provide you with accuracy measures along with an option to select the proportion of training and testing data sets.User can get to choose appropriate lag with tuning parameter like maximum iterations for training the neural modelNot Availabl

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Not AvailableMultivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.Not Availabl

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Not AvailableMultivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.Not Availabl