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The intensive study of an individual watershed is required to develop effective and efficient watershed management plans. Identification of critical erosion-prone areas of the watershed and implementation of best management practices (BMPs) is necessary to control the watershed degradation by reducing the sediment and nutrient losses. The present study evaluates and recommends the BMPs in an agriculture-based Marol watershed (5092 km 2 ) of India, using a hydrologic model, Soil and Water Assessment Tool (SWAT). After successful calibration and validation, the model simulated daily/monthly discharge and sediment were found satisfactory throughout the simulation period. The model was then applied with a calibrated set of parameters for evaluating the effectiveness of various management practices for sediment and nutrient loss control. Keeping in mind the existing agricultural practices, socio-economic aspects and geography of the study area, the management practices were focused on four crops (Maize, Rice, Soybeans and Ground nut), three fertilization levels (high, medium and low), four tillage treatments (Field cultivator, Conservation tillage, Zero tillage and Mould board plough), and two conservation operations (Contour farming and Filter strips). The simulated annual average sediment yield from the watershed was found to be 12.2 t.ha -1 yr -1 . The water balance analysis revealed that, the evapotranspiration is predominant over the watershed (approximately 46.3% of the annual average rainfall). Reduction in sediment yield and nutrient loss was observed with alternate cropping treatments of Groundnut and Soybean, as compared to Paddy and Maize cultivation. Overall, based on simulated results, the field cultivator tillage practice and conservation practices viz., contour

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India is facing the worst water crisis in its history, and major Indian cities which accommodates about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information (MI), genetic algorithm (GA), artificial neural network (ANN), and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate, and were pre-processed using mutual information, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre

HighlightsIrrigation water use efficiency was consistently higher under deficit irrigation as compared to full irrigation.Irrigation water use was always less than the annual allowable pumping limit under deficit irrigation.The first/second week of September was ideal for terminating irrigation under full/deficit irrigation in normal years.Ideal irrigation termination periods in wet/dry years were a week earlier/later than those in normal years.Abstract. Cotton ( L.) production in the Texas High Plains (THP) region relies heavily on irrigation with groundwater from the underlying Ogallala Aquifer. However, rapidly declining groundwater levels in the aquifer and increasing pumping costs pose challenges for sustainability of irrigated cotton production in this region. Adoption of efficient irrigation strategies, such as terminating irrigation at an appropriate time in the growing season, could enable producers to increase irrigation water use efficiency (IWUE) while maintaining desired yield goals. The objective of this study was to determine optimum irrigation termination periods for cotton production in the THP under full and deficit irrigation conditions using the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Cotton model, which was evaluated in a prior study in the THP using measured data from an IWUE field experiment at Halfway, Texas. The treatment factors in the field experiment included irrigation capacities of 0 mm d-1 (low, L), 3.2 mm d-1 (medium, M), and 6.4 mm d-1 (high, H), applied during the vegetative, reproductive, and maturation growth stages. This study focused on a full irrigation (HHH) treatment and three deficit irrigation (LMH, LHM, and LMM) treatments. Eight irrigation termination dates with a one-week interval between 15 August and 30 September were simulated, and the impact of irrigation termination date on cotton IWUE and seed cotton yield were studied by dividing the 39-year (1978 to 2016) simulation period into dry, normal, and wet years based on the precipitation received from 1 April to the simulated irrigation termination date. Results indicated that the simulated IWUE was consistently higher under the LHM, LMH, and LMM treatments when compared to the HHH treatment. Based on the simulated average seed cotton yield and IWUE, optimum irrigation termination periods for cotton were found to be the first week of September (about 118 days after planting, DAP) for the HHH and LMH treatments and the second week of September (125 DAP) for the LHM and LMM treatments in normal years. In wet years, optimum irrigation termination periods were a week earlier than those in normal years and a week later in dry years for the HHH, LHM, and LMM treatments. For the LMH treatment, the optimum irrigation termination period in wet years was the same as that in normal years and two weeks later in dry years. The results from this study along with field-specific, late-season information will assist THP cotton producers in making appropriate irrigation termination decisions for improving economic productivity of the Ogallala Aquifer and thereby ensuring water security for agriculture. However, the recommendations from this study should be used with caution, as the optimum irrigation termination periods could potentially change with changes in cultivar characteristics, soil type, climate, and, crop management practices. Keywords: CROPGRO-Cotton, Deficit irrigation, DSSAT, Full irrigation, Irrigation water use efficiency, Seed cotton yield.

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