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Dr. Rajendra Prasad Central Agricultural University, Pusa
In the imperial Gazetteer of India 1878, Pusa was recorded as a government estate of about 1350 acres in Darbhanba. It was acquired by East India Company for running a stud farm to supply better breed of horses mainly for the army. Frequent incidence of glanders disease (swelling of glands), mostly affecting the valuable imported bloodstock made the civil veterinary department to shift the entire stock out of Pusa. A British tobacco concern Beg Sutherland & co. got the estate on lease but it also left in 1897 abandoning the government estate of Pusa.
Lord Mayo, The Viceroy and Governor General, had been repeatedly trying to get through his proposal for setting up a directorate general of Agriculture that would take care of the soil and its productivity, formulate newer techniques of cultivation, improve the quality of seeds and livestock and also arrange for imparting agricultural education. The government of India had invited a British expert. Dr. J. A. Voelcker who had submitted as report on the development of Indian agriculture. As a follow-up action, three experts in different fields were appointed for the first time during 1885 to 1895 namely, agricultural chemist (Dr. J. W. Leafer), cryptogamic botanist (Dr. R. A. Butler) and entomologist (Dr. H. Maxwell Lefroy) with headquarters at Dehradun (U.P.) in the forest Research Institute complex.
Surprisingly, until now Pusa, which was destined to become the centre of agricultural revolution in the country, was lying as before an abandoned government estate. In 1898. Lord Curzon took over as the viceroy. A widely traveled person and an administrator, he salvaged out the earlier proposal and got London’s approval for the appointment of the inspector General of Agriculture to which the first incumbent Mr. J. Mollison (Dy. Director of Agriculture, Bombay) joined in 1901 with headquarters at Nagpur
The then government of Bengal had mooted in 1902 a proposal to the centre for setting up a model cattle farm for improving the dilapidated condition of the livestock at Pusa estate where plenty of land, water and feed would be available, and with Mr. Mollison’s support this was accepted in principle. Around Pusa, there were many British planters and also an indigo research centre Dalsing Sarai (near Pusa). Mr. Mollison’s visits to this mini British kingdom and his strong recommendations. In favour of Pusa as the most ideal place for the Bengal government project obviously caught the attention for the viceroy.
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ThesisItem Open Access STUDY OF GROUND WATER BALANCE AND CARBON EMISSION DUE TO GROUND WATER ABSTRACTION IN RPCAU CAMPUS PUSA(Dr.RPCAU, Pusa, 2021) SINGH, SACHINDRA KUMAR; Chandra, RavishGroundwater is the most preferred water source in various user sectors in India due to its near universal availability, reliability and low capital cost. Groundwater resource estimation is essential for planning and management and distribution of precious ground water resource and gives us insight to allocate groundwater to various sectors like agriculture, water supply, drinking water judiciously. There is an urgent need to study the annual ground water draft, annual ground water recharge to compute the complete evaluation of ground water resource and ground water balance for future possible corrections. Keeping the above things in mind a study in “Study of Ground Water Balance and Carbon Emission due to Groundwater Abstraction in RPCAU Campus Pusa” was undertaken to estimate annual ground water recharge, annual ground water draft, and annual ground water balance for Dr Rajendra Prasad Central Agricultural University Pusa Campus. The study was conducted for three years (2018 to 2020). The GEC norms 1997 was used to estimate annual ground water recharge, annual ground water draft and ground water balance for the study area. This methodology uses the water table fluctuation technique and empirical formula for recharge calculation. The data collected for this investigation were water table fluctuation, annual rainfall, normal rainfall, number of tubewells, brand of tubewells, Power rating of tubewells, tubewell discharge, operating hours and other details of pumping system, hydrology of the area, specific yield, ground water draft, pond area etc. In the present study, the energy consumption and carbon emission through groundwater abstraction in the RPCAU Pusa campus were also studied. The energy required for groundwater abstraction was estimated as per the methodology provide by Rothausen and Conway, 2011. The carbon emission through pumping of groundwater was calculated by using the methodology given by Nelson and Rothausen, 2008.The annual ground water draft used for water supply was found to be 118.2 ha-m, 122.9 ha-m and 111.9 ha-m respectively for the year 2018, 2019 and 2020 and the annual ground water draft used for irrigation water supply was found to be 104.8 ha-m, 105.9 ha-m and 84.6 ha-m respectively for the year 2018, 2019 and 2020 The total annual ground water recharge for the year 2018, 2019 and 2020 was found to be 108.43 ha-m, 140.49 ha-m and 194.1 ha-m respectively. The stage of ground water development for the year 2018, 2019, and 2020 was found to be 205.7 %, 162.9 % and 101.2 % respectively. The energy requirement for municipal water supply was found to be 239186.5 kWh, 243770.2 kWh and 223198.8 kWh respectively for the year 2018, 2019 and 2020. The energy requirement for irrigation water supply was found to be 155571.6 kWh, 157235 kWh, and 125622.05 kWh respectively for the year 2018, 2019, and 2020. The total carbon emission due to ground water pumping was found to be 97.2 ton, 99 ton and 90.7 ton respectively for the year 2018, 2019 and 2020. The total carbon emission due to irrigation water was found to be 63.2 ton, 63.9 ton and 51 ton respectively for the year 2018, 2019 and 2020.ThesisItem Open Access ASSESSMENT OF SURFACE WATER RESOURCE IN SAMASTIPUR DISTRICT OF BIHAR USING RS AND GIS(DRPCAU, PUSA, 2021) G M, RAJESH; Prasad, SudarshanThe study region, Samastipur district of Bihar surrounded by 5 km buffer zone was divided into 67 square grids of 8 km × 8 km spatial resolution using (ArcGIS) software version 10.7.1. The monthly rainfall images (TRMM_3B43) for the period of 20 years from the years of 2000 to 2019 and the monthly dataset of LST (GLDAS_NOAH025_M_EP) products of 0.25o × 0.25o grid size for the period of 21 years from 2000 to 2020 were downloaded and used for analysis. The climatic variables viz. monthly rainfall and LST values were extracted for all grid points (GP-1 to GP-67) using the model builder tool of ArcGIS. Following the recommendation of WMO, the 14 grid points between GP-44 and GP-66 falling under the circumferential coverage of 3000 km2 (radius of 30.90 km) in flat area from MS, Pusa were considered for comparison and validated with ground-based climatic variables measured at MS, Pusa. The graphical technique and statistical techniques like Pearson correlation coefficient (PCC), mean error (ME), root mean square error (RMSE), bias (B), and Percent bias (PB) were used for comparison. Bias in extracted climatic variables was identified and was corrected using linear scaling. The Landsat-8 imageries were used to develop LULC using supervised classification technique in ArcGIS. The accuracy assessment was carried out using visual observation, Google Earth image, mathematical analysis and the kappa coefficient. The validated soil map of the study area was procured from NBSS and LUP, Nagpur, India and reclassified into soil textural classes. The available water capacity (AWC) of the soil was computed based upon the land use, soil texture and rooting depth following the suggestion of Thornthwaite and Mather (1957). The surplus and deficit water for all the grid points area was estimated using computed monthly PET, AET and AS as input parameters. Thematic maps of potential evapotranspiration, actual evapotranspiration and availability of surplus and deficit water over the study area were developed using inverse distance weighted interpolation technique of ArcGIS. The study investigated that estimated PET was progressively increasing from January to June and thereafter gradually decreasing from July to December. PET was found maximum (120.7 mm) for the month June and minimum (5.5 mm) for the month January and similar pattern were observed in case of AET. During the months of July (85.3 mm), August (83.9 mm) and September (81.1 mm), AET and PET were found to be equal. The LULC map depicted the five types of land use feature classes viz. agricultural land, barren land, forest land, settlement and water body in the region. Silt loam, clay loam and clay were observed as major soil textural classes distributed in the study region. The study area undergoes an annual water deficit of 121.2 mm distributed during the months of February to May, November and December whereas, the annual water surplus of 523.8 mm during the months of January, July to September.ThesisItem Open Access Assessment of land use/land cover changes in Samastipur district of Bihar using RS and GIS(DRPCAU, PUSA, 2021) KUMAR, JITENDRA; Sahu, R. K.The assessment and analysis of land use/land cover (LULC) changes are required to identify the land use changes from year to year which plays a critical role in planning and implementation of developmental activities. The present study assesses LULC changes in Samastipur district of Bihar using remote sensing and geographical information system. The inventory map of land resources and water bodies have been prepared using satellite data of the year 2020 and the ground truth data. The LULC maps were prepared using LANDSAT-5 (2000, 2005 and 2010) and LANDSAT-8 (2015 and 2020) images by adopting object based image classification technique. Total five classes of LULC- agriculture land, settlement, natural vegetation, sand/barren land and water-bodies were identified for the present study. Accuracy percentage of the classification was assessed based on the error matrix and kappa coefficient. Assessments of LULC changes were done @ 5 years, @ 10 years and @ 20 years during 2000-2020. The developed inventory map indicated that the total area of Samastipur district is 290000 ha out of which 284689 ha (98.17%) has been occupied by land resources and 5311 ha (1.83%) by water bodies. The results on LULC indicated that the agriculture land coverage increased at high rate during 2000-2005 and 2005-2010; and after that it is increasing at slow rate. The natural vegetation coverage is continuously decreasing during years 2000-2020 while settlement is continuously increasing during this period with notable increase during 2000-2005 and 2015-2020. In the time interval of 10 years (2000-2010), the agriculture land area increased by 22.17% (41295 ha); natural vegetation area decreased by 38.04% (22905 ha); the water-bodies decreased by 46.69% (3683 ha); sand and barren land decreased by 61.27% (16151 ha) and settlement area increased by 15.62% (1444 ha). Over the next 10 years (2010-2020), area covered by agriculture land, settlement, water-bodies and sand and barren land increased by 18320 ha (8.05%), 4093 ha (38.30%), 1105 ha (26.27%) and 4558 ha (44.65%) respectively while area covered by natural vegetation decreased by 28076 ha (75.24%). During time interval of 20 years (2000-2020), agriculture land area and settlement area increased by 32% (59615 ha) and 59.91% (5537 ha) respectively while natural vegetation, sand and barren land and water-bodies decreased by 84.66% (50981 ha), 43.98% (11593 ha) and 32.68% (2578 ha) respectively. The analysis of the results indicates that the natural vegetation has decreased at fast rate in the recent years. Therefore, proper attention is required towards stopping of cutting of natural vegetation in the district to save the environment.ThesisItem Open Access Study on Water and Nutrient Movement and Root Growth Pattern in Tomato Crop under Drip Irrigation(DRPCAU, PUSA, 2022) Kumar, Abhinav; Nirala, S.K.The research work entitled “Study on Water and Nutrient Movement and Root Growth Pattern in Tomato Crop under Drip Irrigation” was conducted with ten treatments. The treatments comprised with differentreplicationwith different RDF level.Tomato crop of varietyAnsal (Hybrid) was selected for experiment. The field layout was done by using RBD with three replications. The tomato plants planted in experiment field with drip irrigation and fertigation. The water movement at the depth of 0-5 cm varies from 16 -31 %. At depth of 5-10 cm, water content varies from 12 to 26 %. At depth of 10-15 cm, maximum moisture content was found 21 % in treatment T2. At depth of 15-20 cm moisture content 20 % in treatment T2. The results suggest that with drip irrigation, the majority of the nitrates were detected in the layer of soil 0-20 cm, as compared to flood irrigation, where more nitrates leached down 40-50 cm. In treatment T8(60 % RDF+4lph dripper) maximum nitrogen, phosphorous, and potassium content were recorded as 140.90, 92.83, and 169.11 kg ha-1 respectively whereas in T7 (60 % RDF+2lph dripper)), these values were 137.98, 90.41and 167 kg ha-1 at depth of 15-20 cm,0-5 cm,0-5 cm, respectively. In treatment T5(80 % RDF + 4 lph emitter)maximum nitrogen, phosphorous, and potassium content were recorded as 165.54,93.26 and 168.25 kg ha-1 at depth of 15-20 cm, 0-5 cm, 0-5 cm respectively, where as in treatment T4(80 % RDF + 2 lph emitter) found as 155.90, 92.69 and 168.25 kg ha-1 respectively at depth of 15-20 cm,0-5 cm,0-5 cm respectively. Similarly, in treatment T2(100 % RDF + 4 lph emitter)maximum nitrogen, phosphorous, and potassium content were recorded 165.44, 93.12, and 169 kg ha-1 at depth of 15-20 cm,0-5 cm,0-5 cm respectively. In treatment T1(100 % RDF + 2 lph emitter)recorded as 137.98, 90.41and 167 kg ha-1 respectively. Whereas in treatment T3(100 % RDF+6 lph emitter) was 197.81, 97.26, and 180.56 kg ha-1 at depth of 15-20 cm,0-5 cm, 0-5 cm respectively. In flood irrigation maximum nitrogen, phosphorous and potassium contentwas found 277.9, 209.03, and 264 kg ha-1 at depth of 40-50 cm,0-5 cm,10-15 cm respectively. The maximum yield 83.77tons ha-1was recorded in treatment T5(80 % RDF + 4 lph emitter).ThesisItem Open Access Hydrological Modeling of Burhi Gandak River Catchment, Bihar using HEC-HMS(DRPCAU, PUSA, 2022) PAL, SOUVIK; Kumar, AmbrishHydrologic simulation has become essential tools for understanding environment and human influences on river flows and designing ecologically sustainable water management approaches. The present study was carried out in the Burhi Gandak catchment having an area of about 10913.1 sq. km up to Samastipur gauging site (outlet) using SRTM DEM. This flat and elongated watershed had a mains stream of 6th order and experiences less discharge in a short period. The length of overland flow and the constant of channel maintenance had high values, which suggests that the river catchment streams experienced delayed discharges. Due to a high percolation rate, a high chance of groundwater recharge is anticipated on mountainous terrain with a low drainage density (0.57 km-1). In the HEC-HMS, under the loss method, SCS-CN method was chosen, while in the transform method clark’s unit hydrograph and SCS unit hydrograph were chosen. For the flood routing method, the lag method was applied in the HEC-HMS model. The calibration of the model was done with Monsoon data -2020 and the validation was done with Monsoon data - 2021. During the years 2017 and 2020 Waterbodies, flooded vegetation, agricultural land and rangeland increased by 30.02%, 7.96%, 6.88% and 27.7%, respectively. There was a 16.33%, 4.6%, and 21.34% decline in the built-up area, forest land and barren land, respectively. The computed peak discharge was 1683.3, 1910, and 2292 m3/sec and the RMSE was found 0.5 in all cases – calibration, optimization and validation periods, respectively. The NSE value was 0.70, 0.78, and 0.765, the percent bias was 14.01%, 3.74%, and -3.13%, and R2 was 0.77, 0.79, 0.77, respectively for Calibration, optimization and validation. The maximum discharges of five extreme events predicted for monsoon season-2017 were found to be 1.68%, 1.98%, 2.48%, 5.05%, and 4.23% higher for monsoon season-2020, respectively. While the fiveminimum simulated discharges (2020) were higher than the minimum simulated discharge (2017) by 20%, 240%, 225%, 245.16%, and 296.23%, respectively. Due to changes in both land use and land cover during 2017 and 2020, there was an increase in the catchment's flow and a noticeable difference between the discharge in 2017 and 2020. The amount of runoff increased by 18.58% between 2017 and 2020.ThesisItem Open Access Evaluation of infiltration rate on various compaction levels in climate smart villages adopted by RPCAU(DRPCAU, PUSA, 2022) DAS, RIMA; Kumar, AmbrishThe current study entitled "Evaluation of infiltration rate on various compaction levels in climate smart villages adopted by RPCAU" was conducted in the farmer's villages of Samastipur district, Bihar, in the year 2022. In this research, an overall 40 soil samples from different villages were collected for laboratory analysis at 0 to 60 cm soil depth. The soil texture of the study area ranged from loam soil to sandy loam soil to sandy clay loam soil under various cropping systems. The sand, silt, and clay content varied from 47.20 to 65 %, 8.25 to 50 %, and 10 to 40 %, respectively. Bulk density ranged from 1.05 to 1.42 g cm-3. At 0.3 bar suction, FC ranged from 15.10 to 34.58 %, and at 15 bar suction, PWP of soil ranged from 12.33 to 23.41 %. The WHC of soils ranged from 32.09 to 84 %, and OC ranged from 0.15 to 0.89 %, generally decreasing with depth and the percentage of water holding capacity also decreased. A field study was carried out to establish the infiltration rate using an instrument called double-ring infiltrometer. The results showed that the values of initial infiltration rate (mm h-1) for Kalyanpur, Akbarpur, Tira, Rampura, Ladaura, Mirzapur, Basudeopur, Birsingpur, Kabargama, Phulhara were obtained as 12.99, 10.19, 10.11, 5.91, 9.77, 6.79, 6.96, 7.39, 9.73 and 7.13, respectively. Meanwhile, the values of final steady infiltration rate (mm h-1) were 0.18, 0.12, 0.12, 0.12, 0.10, 0.12, 0.12, 0.12, 0.10 and 0.12, respectively. The four infiltration equations, namely, the Green-Ampt equation, Philip equation, Kostiakov equation and Horton equation, were used in this study. The performance evaluation of all developed equations between observed and predicted infiltration rate were analyzed based on the highest values of r ranged from -0.59 to 0.99, R2 ranged from 0.52 to 0.99, and NSE ranged from -28579.30 to 0.99, with WI ranging from 0 to 0.99 and the lowest values of RMSE ranged from 0.1 to 441.08, MAE ranged from 0.08 to 281.43. The results showed that the Green Ampt, Kostiakov, and Philip equations were more appropriate than the Horton equations. The results reveal that Horton's parameters do not fit the conditions for given locations. The average values of penetration resistance for given locations were observed 2067.20, 121290, 1859.68, 1904.32, 109681, 80863, 2432.83, 2196.45, 1524.75 and 1642.65 kPa at 0 to 60 cm depths, respectively. Linear relationship between (weightage mean of PR and constant infiltration rate) shows the value of R2 was 0.45 and significant correlations were obtained. A graphical nomograph was framed to represent three interrelating variables viz. soil texture, final infiltration rate and weighted mean of penetration resistance. A regression analysis was done between infiltration rate as dependent variables and soil parameters as independent variables including soil strength data in developing Pedotransfer Function (PTF). A regression equation had generated to show good relationship between them as R2 = 0.91.ThesisItem Open Access Analysis of Morphometric Characteristics and Land use/Land cover Dynamics of Indravati Catchment Using Geospatial Technique(DRPCAU, PUSA, 2022) SINHA, MADHUSUDAN; Suresh, RamThe present research work was conducted in the Indravati catchment which is an integral part of Godavari basin. ArcGIS software and Google Earth Engine, a cloud-based platform, were primarily used to determine the morphometric parameters and assess the dynamics of land use and cover change. The study area was first delineated using ArcSWAT tool with SRTM DEM (1-Arc-Global with 30 m resolution) in ArcGIS followed by computation of areal, relief and linear aspects along with all sub-catchments. Three sets of time periods, 2011, 2016, and 2021, were examined using LANDSAT pictures to assess the classification of land use and land cover with changing dynamics. Total six classes- waterbodies, forested areas, agricultural land, built-up areas, barrenland, and rangeland were used in the LULC classification scheme. The supervised classification scheme, Random Forest algorithm was used by the Google Earth Engine to analyse this LULC categorization. Assessment of LULC dynamics were determined by Quantum-GIS using semi-automatic classification plugin. Watershed delineation was accomplished using ArcSWAT, in which maximum and minimum elevation were found as 1361 m and 78 m with total area 40533.24 sq. km. The morphometric parameters were determined for the Indravati catchment and its nine sub-catchments such as drainage related aspects like stream order, drainage density, stream frequency, form factor, circulatory ratio etc were computed. This research showed that the 7th order was the trunk order stream, with 10810 total streams, of which the first four orders contributed 98.77 percent of the streams with a mean bifurcation ratio of 11.792 for the Indravati catchment. For sub-catchments, these parameters were also calculated. The overall accuracy and Kappa statistic for LULC classification were found to be 76.43 % and 70.38 %, 77.05 % % 71.05 % and 83.78 % and 80.16 % for year 2011, 2016 and 2021 respectively. The LULC change dynamics were observed between the years of 2011 and 2021, and it was found that, as a result of industrialization and population growth, the area extent of waterbodies, agricultural land, and rangeland decreased by -35.43 percent, -7.63 percent, and -1.32 percent, respectively, while settlement and barrenland increased by 39.34 and 55.92 percent. Since, there are tropical dense forests in this study area that become more extensive without human intervention, there has been an 8.40 percent (631.959 km2), increase in the area covered by forests. For the time periods 2011-2016, 2016-2021, and 2011-2021, it was observed that the total area of 25788.07 km2, 26844.78 km2, and 26471.19 km2 remained unchanged, whereas the areas of 14054.62 km2, 14737.74 km2, and 13681.78 km2 got considerable changes.ThesisItem Open Access Study on different Machine Learning Techniques (MLT) in forecasting Crop Water Requirement(DRPCAU, PUSA, 2022) Dinkar, Humbare Mrunalini; Bhagat, I. B.Crop water requirements must be accurately determined for irrigation scheduling, water resources management, and environmental analysis. There are several locations where different climatic data may not be accessible for its determination. In these circumstances, crop water requirement modelling is a suitable method for predicting crop water requirements. To predict the crop water requirements of rice, wheat, maize, and sugarcane in the Samastipur area of Bihar, Machine Learning Techniques such as Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM) and Multiple Linear Regression (MLR) were utilised in the current study. The Meteorological department of RPCAU, Pusa, Bihar provided the data including relative humidity (maximum and minimum), and temperature (maximum and minimum) while data about solar radiation and wind speed was obtained from Prediction of Worldwide Energy Resources (NASA/POWER) website. For this study, data spanning 20 years (2001–2020) was collected. The FAO-56 Penman-Monteith method and crop coefficient approach were used to determine the water requirement for the selected crops. The Gamma test was utilized for the best input determination. The entire dataset was divided into training (80%) and testing (20%) datasets. The assessment of the models was by the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). It can be inferred that the MLR and MARS models predicted rice crop water requirements finer than they did for other crops. The SVM model accurately predicted the water requirement of the wheat crop compared to other crops. More precisely than other crops, the RF model forecasted the water requirement for sugarcane. For all crops, the overall models' performance-wise rankings were RF, MARS, SVM, and MLR. From the results attained, the RF model beat the other three models throughout training and testing for all crops and can be highly recommended for crop water requirement prediction.ThesisItem Open Access Development of drainage plan for waterlogged areas of Darbhanga district using remote sensing and GIS approach(DRPCAU, Pusa, 2020) D, Sudhakar Raj; Bhagat, I. B.In India, rainfall is variable which causes flood and drought frequently. In Bihar, out of 38 districts, 28 districts will become flooded and 15 districts among them will get extremely affected in every consecutive year. Nearly, 73.63% of the geographical area of North Bihar is considered to be prone to floods. Next to flood, congestion in drainage of surface water resulting into water-logging is another major serious problem in the Bihar state. The combined use of remotely-sensed images and GIS based environmental applications in this work is needed for systematic ideas and scientific planning in the Darbhanga district of Bihar. Image processing and water-logged area assessment was done using ArcGIS and eCognition software. The index-based classification method was followed in an ArcGIS software and as a result, various indices such as NDVI, NDWI, MNDWI (Esri), MNDWI (IHS) had been obtained to assess the water-logged portions. Among the various indices used in different months False Colour Composite image, it was found that the month of September (2017) is the best month to indicate, assess and delineate the water-logged areas. Comparison with the other indices, MNDWI was highlighting the water-logged portions in the better way. The object-based classification was carried out in eCognition software which will convert the pixels into objects. The water-logged boundary layer was extracted by using the threshold value of -0.175 in the September month MNDWI raster. The total area subjected to water-logging in the Darbhanga district was found to be 42,282 ha. The area of water-logging was found in the two sub-divisions of the Darbhanga district. The blocks of Darbhanga and Biraul sub-division, holding the water-logging area of 21,899 ha and 20,383 ha respectively. The maximum expected rainfall at 60 per cent, 70 per cent and 80 per cent probability levels were found using Weibull’s formula and 70 percent maximum rainfall of consecutive days was considered for the drainage characteristics which have been analysed with the formula of volume-balance method. The average pan evaporation loss was estimated as 0.385 cm per day. The average percolation loss was found based on the previous research works and considered for analysis of drainage characteristics. The drainage coefficient of consecutive days i.e., 1-,2-,3-,4-,5-,6- and 7- days for the blocks of Darbhanga sub-division was found to be 27.71, 16.93, 12.57, 10.42, 9.19, 8.35 and 1.60 cm per day respectively and for the blocks of Biraul sub-division it was computed as 18.12, 10.94, 8.07, 6.66, 5.85, 5.31 and 4.79 cm per day respectively. The surface drainage plan for the blocks of Darbhanga and Biraul sub-divisions have been expressed by showing the main drain lines and outlets which was based on the comparison of different thematic maps. The useful measures also have been suggested to execute the drainage plan in an active manner.
