^{1, 3, 4.} Water Technology Center, Prof. Jayashankar Telangana Agriculture University,Rajendranagar, Hyderabad-30.

²M S Swaminathan School of Agriculture, Paralakhemundi, Centurion University of Technology and Management

(CUTM) Gajapati – 761211, Odisha.

⁵College of Agricultural Engineering, Acharya N.G.Ranga Agricultural University, Bapatla, Andhra Pradesh

⁶Programme Planning and Evaluation Group (PPEG), NRSC, Hyderabad.

Article Publishing History

INTRODUCTION

India is amongst the largest irrigator countries in the world. In most of the existing irrigation schemes there is a serious lack of reliable and adequate information on system performance. Periodical assessment of irrigation project is needed to find out irrigation potential utilized vis-à-vis the potential created. System performance monitoring, evaluation, and diagnostic analysis are keys for the improvement of irrigation projects. A set of performance indicators describes the system performance of irrigation schemes in different dimensions for the benefit of decision-makers. The temporal and spatial variations of such indicators will help in improving the system performance.

Remote sensing provides an opportunity to measure the crop yield without field measurements and this opens the possibility to study the variability of water productivity within an irrigation system. Neelima et al.,2015 and Kumar et al. 2015 assessed the irrigation performance of Jurala irrigation project and Nagarjuna Sagar Project in 2008-10 using LISS III and AWiFS data using RS based crop inventory and NDVI values.  Ayat Elnmer et al. (2018) assessed the irrigation performance of the Nile Delta using remote sensing using indicators like adequacy index (AI), equity index (EI) and Dependability (PI), Water productivity (WP), water depleted fraction (DF), relative evapotranspiration (RET) with LANDSAT ETM+ ETM+ images and SEBAL approach.

Remote sensing is now playing a major role in acreage estimation and to forecast crop yields primarily based upon statistical– empirical relationships between yield and vegetation indices (Thenkabail et al., 2002, Casa and Jones,   2005).  .  Wheat acreage estimation for Karnal district using the LISS – III satellite data (Urmil Verma and Haryana, 2011), Boro rice in Nagaon district of Assam (Raihan Ahmed and Haroon Sajjad, 2015), and yield of soybeans crop in some districts of M.P (Abhishek Kumar Maurya, 2016) and area, production and productivity of potato for 5 major potato growing states of India i.e. Bihar, Gujarat, Punjab, Uttar Pradesh and West Bengal was done using RS and GIS, (Kumar 2019).

The Area under Janapad major  located at 90.5 km (16⁰ 56′ 12.8″ E& 79⁰ 42′ 0.7″ N) on Left Bank canal of Nagarjunasagar project with the length of 24 km and designed discharge of 555.13 cusecs was selected for the study of. The ayacut area spreads in Nereducherla, Garidepally and part of Mattampally mandals of the Nalgonda district (Fig 1). It has a total geographical area of 13216 ha with 11 Water Users Associations. It is located in Southern Telangana Agro-Climatic Zone of Andhra Pradesh. It comes under Agro-Eco region 7-Hot Semi-arid eco region with red and black soils (K₆ D₂) (NBSS&LUP, 1992). By using IRS P6 WiFS (Wide Field Sensor) and IRS-1C WiFS and LISS3 which have a good periodicity, it is possible to construct growth profiles and retrieve yield related parameters at region level (Menon, 2012). Satellite remote sensing and Geographic Information System (GIS) offer great promise for natural resources management (Shanmugapriya et al., 2019).

Figure 1. Location map of Janpad command area, Nagarjuna Sagar left canal, Andhra Pradesh.

Irrigation performance assessment is the systematic spatial and temporal evaluation of irrigation systems to diagnose problems and is considered as the most critical element for improving irrigation management (Abernethy and Pearce, 1987).Irrigation performance assessment was carried out for various projects in India. The irrigation performance assessment of Jurala (Neelima et al., 2013 ) and Krishna Western Delta in Andhra Pradesh was carried by using satellite data indicated that there was reduction in the total cropped area by 17,383 ha in 2005-06 compared to 1998-99 (Prasad et al., 2006). However, such information is not available intensively at irrigation major of Nagarjuna Sagar multipurpose irrigation project. Hence, the research project on irrigation performance assessment of Janpad major left bank canal, Nagarjuna Sagar Project using Remote Sensing and GIS was taken up to  assess the  irrigation potential utilized vis-à-vis the potential created, agricultural production and productivity achieved and how efficiently the irrigation being managed.

MATERIAL AND METHODS

The performance indicators are computed for the Janpad major command area. The main output considered is crop production and the major inputs are water and land. Six  basic comparative performance indicators namely irrigation intensity, overall consumption rate, water utilization index,  output per unit cropped area, output per unit command area, and water productivity were computed. Computation of these indicators require data on cropped area, command area, potential evapotranspiration, water diverted by the canal, rainfall and production. The multispectral data from LISS-III sensor of IRS –P6 (Resourcesat-1) were acquired on  21-2-09, 17-3-09 and 10-4-09 and geo-coded false colour composites (FCC) were utilized for delineation of major crops grown in the study area. The Supervised classification for the images taken on Feb, Mar and April 2009 was done.  The training windows were defined for various crops and other land use classes based on the ground truth information. Multiple training sites for each class were identified in order to represent the variability with in the same class.

The image was classified using maximum likelihood algorithm. Rice mask map was generated based on classified image. The pixels thus generated were multiplied with 24 m x 24 m to estimate paddy area. Crop cutting experiments were conducted (5 m × 2 m) randomly at selected points in the command area and the corresponding GPS reading were taken. Paddy grain weight at 14 per cent moisture was taken for developing yield model. Normalized Difference Vegetation Index (NDVI) proposed by Rouse et al., (1974) was used in this study. This index is very sensitive to the presence of green vegetation. It permits the prediction of agricultural crops in semi arid areas.

NDVI can be defined by following equation

NIR and R are the reflectance in the near infra red and red regions, respectively.

The red and infra red channel data pertaining to the IRS P6 LISS III image were transformed in to NDVI image in ERDAS IMAGINE Modeler panel by running the NDVI model. The resultant NDVI image of the paddy crop was used for yield estimation. Maximum NDVI image derived using 2008-09 rabi season satellite data was used to establish the relationship between NDVI and paddy yield observed at CCE plots through the development of regression model.

Data on command area and water diverted were collected from Divisional executive engineer, Left bank canal, Nagarjunasagar project, Miryalguda, Nalgonda district, A.P., India. For computation of PET and water requirement, the weather data of ARI Rajendranagar was considered.  Potential Evapotranspiration was computed using the Penman-Monteith method of CROPWAT computer model.  These indicators were calculated as shown below:

ETp is the potential evapotranspiration (m³) Pe is the effective rainfall (m³)

V is the total volume of water supplied to the command area (m³)

RESULTS AND DISCUSSION

Initial observations from field visits indicate that paddy constitute more than 96 per cent of the total cropped area. Other crops grown in the command area are chilies, cotton and citrus.  The satellite derived crop estimates in Janpad major in NSP left canal command indicate that paddy occupies 11,750 ha while water bodies, settlements, barren lands and shrubs occupy 9630.6 ha (Table 1). The area under other crops is negligible and this area was considered under shrubs. The paddy area is more in middle reach followed by head and tail end. Paddy is the major crop in Janpad major command area, left bank canal, Nagarjuna sagar. Out of 4493.5 ha of head region of the command area, 3179 ha is paddy. Compared to paddy area, other crops are negligible. The water bodies, settlement, barren land and shrubs occupied 3.7, 508.5, 11.3 and 791.0 ha respectively.In middle reach out of 10255.1 ha, the paddy crop occupied 7460.90 ha. The water bodies, settlement, barren land and shrubs occupied 92.0, 1221.2, 2.9, and 1892.0 ha respectively.

Table 1. Satellite derived crop area (ha) in head, middle and tail reaches of Janpad major, NSP left canal during rabi 2008-09

Crop	Head	Middle	Tail	Janpad Major command
Paddy	3179.0	7046.9	1525.0	11750.9
Water body	3.7	92.0	17.2	113.0
Settlement	508.5	1221.2	2988.2	4718.0
Barren land	11.3	2.9	568.6	583.9
Shrubs	791.0	1892.0	1532.8	4215.9
Total	4493.5	10255.1	6631.9	21381.5

In tail end area, out of 6631.9 ha, the paddy occupied 1525.0 ha while considerable area was occupied by settlement (2988 ha), followed by shrubs. The water bodies occupied considerably less area. Neelima et al., (2014) estimated the irrigated area under Jurala Project command area and found that there was an increase in the paddy area from 70.7 to 72.4% and from 76.3 to 83.5% in the right main canal and left main canal respectively.  Ahmed and Sajjad, (2015) estimated the crop acreage of Boro rice  using NDVI, RVI and supervised classification in Nagaon district of Assam in single date Landsat 5 TM digital data coinciding with maximum crop growth (March, 2009) has been used for identification and estimation of crop acreage.   Maurya, (2016) estimated the acreage and yield of soybeans crop in some districts of M.P. in the Kharif season 2007.  NDVI was performed on all the October images (2007, 2006, 2005) using hybrid classification techniques.

Irrigation intensity was estimated based on the satellite derived crop estimates as percent of the total culturable command area (CCA) i.e., ayacut in the command. The irrigation intensity of the head, middle and tail reach was observed to be 97, 97 and 90 % respectively (Table 2). On the other hand the irrigation intensity was only 96 % in Janpad major during rabi 2008-09. Further, in tail reach, decrease in irrigation intensity (90 %) was observed, this may be due to large area in the tail reach was under shrubs and rocky land. Prasad et al., (2006) has reported the irrigation intensity of 83 per cent under NIZ east canal command of Krishna western delta. Kumar, et al.., (2014) reported that the irrigation intensity varied from 92.39 to 123.19 for against target of 100 per cent in Nagarjuna sagar left canal command area during rabi 2008-09 and 2009-10, respectively.

Table 2. Irrigation intensity (%) at Head, middle and tail reach and entire Janpad major of NSP left canal during rabi 2008-09.

Head	Middle	Tail	Janpad major command
97	97	90	96

Overall Consumption Rate was derived by dividing water requirement estimated using Potential Evapotranspiration (ETp) minus Pe (effective rain fall) by volume of water applied to the Janpad command area.  The water requirement was derived through CROPWAT model.  The PET for paddy was 544.1 mm and for shrubs is 325 mm.  The effective rainfall was 9.9 mm.  The gross water requirement for paddy was observed to be 1584.4 mm.The Overall Consumed Rate (efficiency) estimated was 0.67 (Table 3). This is the Overall Consumed Rate (efficiency) which was derived without considering the conveyance and application losses. The higher OCR values in the selected distributary implies that insufficient water was supplied to that particular distributary.  The OCR values in the Jural project command area ranged from 0.06 to 0.15and 0.02 to 0.12 during rainy season 2009 and 2010 respectively and thus there was no shortage of water supply to the command area (Neelima et al., 2015) and for Janpad and Mudimanikyam majors in Nagarjunasagar left canal area during 2010 was 0.52 and 0.40 (Kumar et al., 2014).

Table 3. Water Utilization Index, Overall Consumption Rate and Water Productivity of Janpad command area.

Indicators	Janpad major command area
Water Utilization Index (ha/M m3)	124
Overall Consumption Rate	0.67
Water Productivity (kg/m3)	0.647

Water Utilization Index (WUI) defined as area irrigated per unit volume is a measure of water delivery performance and constitutes one of the important spatial performance indicators of an irrigation system. WUI also forms basis for evaluating the adequacy of seasonal irrigation supplies in an irrigation system (Table 3). It is estimated based on the equivalent wet area and amount of water released in to the various irrigation units. The paddy irrigated area was 11750.9 ha and the water delivered to the major was 94.752 M.m³.  The water utilization index was 124 ha/M.m³ or 3512 ha per one TMC which is less than the normal expected irrigation of 4000 ha of paddy irrigation (wetland) per TMC.  Neelima  et al. (2014) reported that in Jurala project command area  in Telangana during 2009-10, area irrigated was much less per unit of canal irrigation water supplied (68.8 ha irrigated million m^-3 water supplied) for the left main canal, compared to right main canal when better utilization of canal irrigation water was achieved (80.2 ha irrigated million m-3 water supplied).

Figure 2. Supervised classification image of Janpad major, NSP left canal, India of Feb21, Mar17 and Apri10, 2009

In Janpad major of  NSP left canal command during rabi the paddy yields were highly variable at different sites obtained through crop cut experiments (CCE) and the yields ranged from 4.30 to 9.25 t/ha. Maximum NDVI image derived using Feb 21,  2009 satellite image was used to establish the relationship between NDVI and paddy yield observed at CCE plots through the development of linear regression model (Fig. 3).   Yield prediction model Y= 8.5076(NDVI) + 2.8125 Y = Estimated yield (t ha^-1) NDVI: NDVI of paddy crop.

Figure 3: Paddy yield model developed for Janpad major command area, NSP, India

The yield prediction model was found to be significant R² = 0.7095, which explain 71 % of the variability in yield estimation which is reasonable for crop yield estimation studies. The Paddy yield model generated for Janpad major of NSP left canal command for rabi 2008-09 was applied to generate paddy yield map. Using the model, the average yield of paddy in Janpad command was estimated to be 4.44 t/ha. The total production of paddy in Janpad major command was calculated by using the relationship Production= area (ha) × yield (t/ha). The estimated production of paddy in Janpad major was 52,174 tons.

The out put  per cropped area for Janpad command area was computed by dividing the total production obtained by multiplying satellite derived yield (4.4 t/ha) with paddy area (11750.9 ha).  The area under other crops (4 %) which are included in shrubs were not considered because it was scattered and negligible.  The overall production of Janpad major was found to be 51704 tons. The out -put  per culturable command area for Janpad command was computed by dividing the total production obtained by multiplying satellite derived yield (4.44 t/ha) with paddy area (11750.9 ha) of the command. The culturable command area is 12194 ha.  The output per culturable command area for Janpad major was 4.27 t/ha.  Similar result was observed by Upadhyaya (2005) for Patna Main Canal Command. Productivity of water is one of the irrigation performance indicator used in irrigation performance assessment as it can establish the relation between the amount of water supplied to irrigation command and the production of paddy realized from the command. The water productivity observed in command area was 0.647 kg/m³. A similar trend was reported by Prasad et al., (2006) (0.7 kg/m³) for Krishna western delta during 2005-06.

Taghvaeian et al., (2018) reported that by using Remote Sensing and GIS Techniques for  assessing Irrigation Performance indicators based on the magnitude and uniformity of   ground water depth was found to be functioning at an optimal level. This suggests that a set of performance indicators like Supply Demand Ratio, Over all Consumption Rate (OCR) and Water Utilization Index (WUI) can describe performance of irrigation schemes in different dimensions for the benefit of decision-makers. . It is clear that the Janpad major supposed to irrigate more area, however it is falling short of 12 % of less area than expected. The estimated indicators provided information the performance of the Janpad major irrigation canal is under utilizing the irrigation water. Understanding the performance of irrigation by irrigation managers at farm levels has the potential to improve irrigation water use efficiently and contribute to more efficient use of water resources

CONCLUSIONS

A set of performance indicators describes the system performance of irrigation schemes in different dimensions for the benefit of decision-makers. Information generated on irrigation performance of the project can be used for improving better irrigation management.  Further, such periodical studies are required for taking remedial measures which ultimately help in efficient management of the most important and scarce natural resource water in increasing the water productivity. Satellite measurements provide near real time data and can be revived and updated.

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