Abstract
The development of robust nutrient management strategies have played a crucial role in improving crop productivity, profitability and nutrient use efficiency. Therefore, the implementation of efficient nutrient management stratigies is important for food security and environmental safety. Amongst the essential plant nutrients, managing nitrogen (N) and potassium (K) in wheat (Triticum aestivum L.) based production systems is citically important to maximize profitable production with minimal negative environmental impacts. We investigated the effects of different fertilizer-N (viz. 0-240 kg N ha(-1); N-0-N-240) and fertilizer-K (viz. 0-90 kg K ha(-1); K-0-K-90) application rates on wheat productivity, nutrient (N and K) use efficiency viz. partial factor productivity (PFPN/K), agronomic efficiency (AE(N/K)), physiological efficiency (PEN/K), reciprocal internal use efficiency (RIUEN/K), and profitability in terms of benefit-cost (B-C) ratio, gross returns above fertilizer cost (GRAFC) and the returns on investment (ROI) on fertilizer application. These results revealed that wheat productivity, plant growth and yield attributes, nutrients uptake and use efficiency increased significantly (p<0.05)with fertilizer-N application, although the interaction effect of N x K application was statistically non-significant (p<0.05). Fertilizer-N application at 120 kg N ha(-1) (N-120) increased the number of effective tillers (8.7%), grain yield (17.3%), straw yield (15.1%), total N uptake (25.1%) and total K uptake (16.1%) than the N-80. Fertilizer-N application significantly increased the SPAD reading by similar to 4.2-10.6% with fertilizer-N application (N-80-N-240), compared with N-0. The PFPN and PFPK increased significantly with fertilizer-N and K application in wheat. The AE(N) varied between 12.3 and 22.2 kg kg(-1) with significantly higher value of 20.8 kg kg(-1) in N-120. Fertilizer-N application at higher rate (N-160) significantly decreased the AE(N) by similar to 16.3% over N-120. The N(120)treatment increased the AE(K) by similar to 52.6% than N-80 treatment. Similarly the RIUEN varied between 10.6 and 25.6 kg Mg-1 grain yield, and increased significantly by similar to 80.2% with N-120 as compared to N-0 treatment. The RIUEK varied between 109 and 15.1 kg Mg-1 grain yield, and was significantly higher in N-120 treatment. The significant increase in mean gross returns (MGRs) by similar to 17.3% and mean net returns (MNRs) by similar to 24.1% increased the B-C ratio by similar to 15.1% with N-120 than the N-80 treatment. Fertilizer-N application in N-120 treatment increased the economic efficiency of wheat by similar to 24.1% and GRAFC by similar to 16.9%. Grain yield was significantly correlated with total N uptake (r = 0.932**, p<0.01), K uptake (r = 0.851**), SPAD value (r = 0.945**), green seeker reading (r = 0.956**), and the RIUEN (r = 0.910**). The artificial neural networks (ANNs) showed highly satisfactory performance in training and simulation of testing data-set on wheat grain yield. The calculated mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) for wheat were 0.0087, 0.834 and 0.052, respectively.
The well trained ANNs model was capable of producing consistency for the training and testing correlation (R-2 = 0.994**, p<0.01) between the predicted and actual values of wheat grain yield, which implies that ANN model succeeded in wheat grain yield prediction.