The exploration, exploitation, and unscientific management of groundwater resources in the National Capital Territory (NCT) of Delhi, India have posed a serious threat of reduction in quantity and deterioration of quality. The objective of the study is to determine the groundwater quality and to assess the risk of groundwater pollution at Najafgarh, NCT of Delhi. The groundwater quality parameters were analyzed from the existing wells of the Najafgarh and the thematic maps were generated using geostatistical concepts. Ordinary kriging and indicator kriging methods were used as geostatistical approach for preparation of thematic maps of the groundwater quality parameters such as bicarbonate, calcium, chloride, electrical conductivity (EC), magnesium, nitrate, sodium, and sulphate with concentrations equal or greater than their respective groundwater pollution cutoff value. Experimental semivariogram values were fitted well in spherical model for the water quality parameters, such as bicarbonate, chloride, EC, magnesium, sodium, and sulphate and in exponential model for calcium and nitrate. The thematic maps of all the groundwater quality parameters exhibited an increasing trend of pollution from the northern and western part of the study area towards the southern and eastern part. The concentration was highest at the southernmost part of the study area but it could not reflect correctly the groundwater pollution status. The indicator kriging method is useful to assess the risk of groundwater pollution by giving the conditional probability of concentrations of different chemical parameters exceeding their cutoff values. Thus, risk assessment of groundwater pollution is useful for proper management of groundwater resources and minimizing the pollution threat.
A B S T R A C TAlthough soil conservation practices are being promoted as better environmental protection technologies than traditional farmers' practice, limited information is available on how these practices affect soil water balance and root water uptake. The root water uptake (RWU) patterns of cotton grown under soil conservation practices and soil water balance in cotton (Gossypium hirsutum L.) fieldsunder a cotton-wheat (Triticum aestivum L.) cropping system were analyzed using the Hydrus-2D model. The treatments were: conventional tillage (CT), zero tillage (ZT), permanent narrow beds (PNB), permanent broad beds (PBB), ZT with residue (ZT + R), PNB with residue (PNB + R) and PBB with residue (PBB + R). Results in the third year of the cotton crop indicated that the surface (0-15 cm layer) field saturated hydraulic conductivity in both PNB and PBB plots were similar and were significantly higher than in the ZT plots. Computed potential transpiration rates (T rp ) under CT were lower than in other treatments, due to less radiation interception and lower Leaf Area Index (LAI). Both PNB and PBB plots had higher T rp and crop yields than CT plots, which were further improved by residue retention. Predicted soil water content (SWC) patterns during the simulation periods of third and fourth years showed strong correlation (R 2 = 0.88, n = 105, P < 0.001, the root mean square error (RMSE) = 0.025, and the average relative error (AVE) = 7.5% for the third year and R 2 = 0.81, n = 105, P < 0.001, RMSE = 0.021, and AVE = 9% for the fourth year) with the actual field measured SWCs. Cumulative RWU (mm) were in the order: ZT (143) < CT (157) < PNB (163) < ZT + R (174) < PBB (188) < PNB + R (198) < PBB + R (226). Thus, PBB + R and PNB + R practices could be adopted for cotton cultivation, as these enhanced root growth and improved radiation interception and LAI. The Hydrus-2D model may be adopted for managing efficient water use, as it can simulate the temporal changes in SWC and actual transpiration rates of a crop/cropping system.
Variability maps of Hydraulic conductivity (K) were generated by using geo statistical analyst extension of ARC GIS for delineating compact zones in a farm. In the initial exploratory spatial data analysis, K data for 0 - 15 and 15 - 30 cm soil layers showed spatial dependence, anisotropy, normality on log transformation and linear trend. Outliers present in both layers were also removed. In the next step, cross validation statistics of different combinations of kriging (Ordinary, simple and universal), data transformations (none and logarithmic) and trends (none and linear) were compared. Combination of no data transformation and linear trend removal was the best choice as it resulted in more accurate and unbiased prediction. It thus, confirmed that for generating prediction maps by kriging, data need not be normal. Ordinary kriging is appropriate when trend is linear. Among various available anisotropic semivariogram models, spherical model for 0 - 15 cm and tetra spherical model for 15 - 30 cm were found to be the best with major and minor ranges between 273 - 410 m and 98 - 213 m. The kriging was superior to other interpolation techniques as the slope of the best fit line of scatter plot of predicted vs. measured data points was more (0.76) in kriging than in inverse distance weighted interpolation (0.61) and global polynomial interpolation (0.56). In the generated prediction maps, areas where K was <12 cm?day–1 were delineated as compact zone. Hence, it can be concluded that geostatistical analyst is a complete package for preprocessing of data and for choosing the optimal interpolation strategies
This study aims in linking the biophysical and socioeconomic data base layers with the technical coefficients or simulation models for agri-production estimates and land use planning under normal and extreme climatic events, and exploring the resource and inputs management options in village Shikohpur, Gurgaon district located in the northwest part of India. The socioeconomic profile of Shikohpur is highly skewed with mostly small and marginal farmers. Though the areas under wheat in Shikohpur are increasing, the productivity is declining or remaining stagnant over the years. Most of the area during kharif season (June-September) remains fallow. Pearl millet based cropping systems (pearl millet-mustard and pearl millet-wheat) are predominant. Soils are mostly loamy sand to sandy loam with average of 70-80% sand content. Organic C content in soil is less than 0.3%, due to high prevailing temperature with little rainfall and also intensive agriculture followed in this region. Though the annual average seasonal rainfall in Gurgaon did not have much variation over the years, occurrence of extreme climate events has increased in the last two decades. The crop intensity is low and the water table is declining. Water and nitrogen production functions were developed for the important crops of the region, for their subsequent use in scheduling of the inputs. InfoCrop, WTGROWS and technical coefficients were used for crop planning and resource management under climate change and its variability, extreme events, limited resource availability and crop intensification. These will help in disseminating necessary agro-advisories to the farmers so that they will be able to manipulate the inputs and agronomic management practices for sustained agricultural production under normal as well as extreme climatic conditions.
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