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A multi-season research trial entitled 'crop residue management effects on yield and water productivity of summer mung bean (Vigna radiata L.) under different irrigation regimes in Indian Punjab' was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Bathinda, during rabi 2020 and 2021. The field experiment was conducted in a split-plot layout with nine treatment combinations and replicated thrice. The treatments consisted of T 1 (no wheat residue along with tillage), T 2 (leftover wheat residue with zero tillage), and T 3 (incorporated wheat residue along with tillage) in main plots and irrigation regimes viz., I 1 (vegetative growth and flowering stage), I 2 (vegetative growth, flowering, and pod filling stage) and I 3 (vegetative growth, flowering, pod formation and pod filling stage) in sub-plots, respectively. The growth and yield attributing characters were significantly higher under T 3 than T 1 but statistically at par with T 2 during both years. An increase of 24.1% and 19.0% in grain yield was found in residue incorporation (T 3 ) and residue retention (T 2 ) over residue removal (T 1 ), respectively. Maximum crop and irrigation water productivity was observed under T 3 due to reduced water use and increased yield. Among the irrigation regimes, the I 3 recorded significantly higher grain yield (0.70 and 0.79 t ha - 1 ) than I 1 . It was at par with I 2 during both years due to higher irrigation frequency at the pod formation and pod filling stage. Crop water productivity (CWP) was higher under I 3 , whereas irrigation water productivity (IWP) was higher under I 1 during both years. Additional irrigation at the pod-filling stage increased the grain yield by 36.5%, and two additional irrigations at the pod-formation and pod-filling stage further increased yield by 46.2% compared to only two irrigations at the vegetative and flowering stages. The treatment combinations of T 2 I 2 and T 3 I 2 outperformed T 1 I 3 in terms of growth and yield attributing characters viz. plant height, dry matter accumulation (DMA), leaf area index (LAI), pods plant - 1 , seeds pod - 1 , and 1000-seed weight, which resulted in higher grain yield in these treatment combinations over T 1 I 3 . Applying crop residue can help minimize water use and increase crop water productivity. So, retaining crop residue in summer mung bean resulted in saving irrigation water due to lesser evapotranspiration from the soil surface., (© 2024. The Author(s).)

Accurate prediction of suspended sediment concentration (SSC) carried by a river or watershed basin is essential for understanding the hydrology of basin in terms of water quality, river bed sustainability and aquatic habitats. In this study, four heuristic methods, namely, radial basis neural network (RBNN), self-organizing map neural network (SOMNN), least square support vector regression (LSSVR), and multivariate adaptive regression spline (MARS) were employed for daily SSC modeling at Ashti, Bamini, and Tekra stations located in Godavari River basin, Andhra Pradesh, India. The Gamma test (GT) was utilized for identifying the most significant input variables for the applied heuristic approaches. The results obtained by RBNN, SOMNN, LSSVR, and MARS models were compared with those of the traditional sediment rating curve (SRC). The performance of the models was evaluated based on the root mean square error (RMSE), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), Willmott index (WI), and pooled average relative error (PARE) indices, as well as the visual inspection using line diagram, scatter diagram, and Taylor diagram (TD). The results of comparison revealed that the four heuristic methods gave higher accuracy than the SRC model. Among the heuristic models, the RBNN-3 (RMSE = 0.045, 0.062, 0.131 g/l; COE = 0.884, 0.883, 0.914; PCC = 0.955, 0.961, 0.958; and WI = 0.970, 0.963, 0.976) outperformed the other models in simulating daily SSC records in the studied stations.