Burning of crop residues results in immense loss to soil fertility, in addition to this burning causes greenhouse gas emissions into atmosphere thereby disturbs the environment. A Field experiment was conducted to ascertain the crop residue and fertilizer management practices on yield and nutrient uptake of mustard at Regional Agricultural Research Station (RARS), Polasa, Jagtial situated in Northern Telangana Zone of Telangana State during rabi, 2020-21. Grain yield, nutrient content and nutrient uptake were significantly influenced by crop residue and fertilizer management practices. Highest grain and stover yield of 1194 and 2654 kg ha-1 were recorded, when 20 % excess nitrogen was applied than RDN along with residue incorporation. Nutrient uptake was significantly higher in burning treatment during initial stages of crop growth later more uptakes were observed in residue incorporation treatments. Among the treatments lowest yields and nutrient contents (N and P) were recorded in residue burning treatments. This study concluded that, instead of burning, paddy straw was to be incorporated along with 20% excess nitrogen to obtain better yields and nutrients uptake in succeding mustard crop.
We consider a wireless communication system in which N transmitter-receiver pairs want to communicate with each other. Each transmitter transmits data at a certain rate using a power that depends on the channel gain to its receiver. If a receiver can successfully receive the message, it sends an acknowledgement (ACK), else it sends a negative ACK (NACK). Each user aims to maximize its probability of successful transmission. We formulate this problem as a stochastic game and propose a fully distributed learning algorithm to find a correlated equilibrium (CE).In addition, we use a no regret algorithm to find a coarse correlated equilibrium (CCE) for our power allocation game.We also propose a fully distributed learning algorithm to find a Pareto optimal solution. In general Pareto points do not guarantee fairness among the users, therefore we also propose an algorithm to compute a Nash bargaining solution which is Pareto optimal and provides fairness among users. Finally, under the same game theoretic setup, we study these equilibria and Pareto points when each transmitter sends data at multiple rates rather than at a fixed rate. We compare the sum rate obtained at the CE, CCE, Nash bargaining solution and the Pareto point and also via some other well known recent algorithms.
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