The decrease in water resources due to the excessive use of water for irrigation purpose and climatic changes represents a serious world-wide threat to food security. In this regards, 50 wheat accessions were analyzed, using completely random factorial design at the seedlings stage under normal and drought stress conditions. Significant variation was detected among all accessions under both conditions. All characters studied showed variations in the mean values in water deficit environments in studied gemplasm at seedling stage. As seedling fresh weight, dry weight, relative water content, cell membrane thermo-stability, chlorophyll a & b were positively associated among themselves under drought conditions which showed the significance of these attribute for water deficit areas in future wheat breeding programs. Based on their performance, five accessions namely Aas-11, Chakwal-86, Pasban-90, Chakwal-97 and Kohistan-97 were selected as drought tolerant and three accessions namely Mairaj-08, Lasani-2008 and Gomal-2008 were selected as drought susceptible genotypes. The choice of wheat accessions based on the characteristics of the seedlings is informal, low-priced and less hassle. Likewise, the seedlings attributes exhibit moderate to high variation with an additive genetics effects on the environments. Best performance accessions under water deficit environment will be beneficial in future wheat breeding schemes and early screening for the attributes suggested in current experiment will be useful for producing best-yielded and drought-tolerance wheat genotypes to sustainable food security.

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Background Numerous biotic and abiotic factors are responsible for losses of grains quality and quantity during storage. Insecticides used to control stored grain insect pests are not only hazardous to mammals and environment but also induce resistance in insect pests towards these synthetic chemicals. A current trial was conducted, during 2020, in a stored grain laboratory at the College of Agriculture, BZU, Bahadur Sub Campus, Layyah, Punjab, Pakistan. A diatomaceous earth (DE) formulation enhanced with bitterbarkomycin (DEBBM) and combined with Beauveria bassiana (Balsamo) Vuillemin was evaluated against Cryptolestes ferrugineus, (Stephens) (Coleoptera: Laemophloeidae), Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae) and Tribolium castaneum (Coleoptera: Tenebrionidae) under laboratory conditions. Results DEBBM was applied at the rate of 50 mg/kg, DE (150 mg/kg) and Beauveria bassiana (1.5 × 108 and 1.5 × 1010 conidia/kg) alone as well as in combination with wheat, rice and maize. Treated adult mortality was recorded at 7, 14 and 21 days after exposure. Results of the current study showed that insect pest mortality was maximum in the case of combined application of DEBBM with B. bassiana (high concentration) at prolonged exposure time as compared to their alone application. Mortality of C. ferrugineus was maximum in wheat and rice (100%) over maize (97%), while, R. dominica exhibited high mortality in wheat (100%) followed by rice (97%) and maize (94%) at combined application of DEBBM with B. bassiana (high concentration) after 21 days. Regarding T. castaneum mortality was high in wheat (100%) followed by rice (93%) and maize (88%) in case of combined application of DEBBM with B. bassiana (high concentration) at prolonged exposure time (21 days). Conclusion In crux, the current trial showed that a mixture of DEBBM and B. bassiana is helpful in controlling tested insect pests.

Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level.

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.