Institute of Remote Sensing, Anna University, Chennai
Dr. M. Karmegam
Center for Water Resources,Anna University,Chennai
Dr. K. Venugopal
Institute of Remote Sensing, Anna University, Chennai
Satellite remote sensing (SRS) and geographic information (GIS) techniques for improved water management in canal irrigation schemes. The Bhadra command area was brought under NWMP(National water management project) to improve agricultural productivity and farm income through a predictable, equitable and reliable irrigation service. Satellite remote sensing technique has been applied to historic and 1995 Rabi season data by National Remote Sensing Agency to generate primary data on irrigated area, cropping pattern and crop yield at disaggregated level and to access the improvement in agricultural productivity and water management after MWMP implementation. The GIS technique helps in integration of satellite and ground information to evaluate the system performance and to diagnose the inequality in the performance to aid in improving the water management.
The primary objective is to diagnose the factors for poor performance of selected distributaries using satellite data and s ground data collected by specially designed sample survey and to improve performance by prescribing corrective measures. The distributaries 9A, 12 and 15 of Malabennur division of Bhadra project in Karnataka state covering about 2350 ha area, is considered for this study. The performance of 12th distributory is good whereas the other two were poor performing. Hence to find the causative factors for poor performance were studied in comparison with a good performing area.
The satellite inputs (crop map and condition map) geometrically corrected wit respect to topographic maps are generated with ARIES and EASI/PACE software and transported to PAMAP environment. The revenue survey map in 1:8000 scale is photographically reduced to 1:16000 scale, digitized, edited and corrected with respect to top map and satellite imagery. Conducting sample survey designed for this purpose and compiled in Dbase collects the ground data for the analysis of identified problem distributaries. The system performance of the problem distributaries are characterized based on satellite reported acreage and yield gaps and analyzed with reference to ground sample survey data. The diagnostic analysis of the problem distributaries is carried out on the aspects of irrigation, agriculture and socio-economic. A questionnaire to this effect was designed and the information collected from the ground was organized and analyzed in the GIS. The causative factors are identified and prioritized for corrective measures to ensure better performance within the framework of NWMP. The analysis is performed by comparing the good with the bad performing distributaries.
The physical and socio-economic data were interpolated with different considerations and methods and analyzed. A Conjunctive analysis of remote sensing- derived, irrigation, agricultural and socio-economic data was attempted. Various causative factors pertaining to all these three activities were identified, ranging from poor physical condition, improper fertilizer consumption to poor interaction between the officials and the farmers. Here GIS served as a platform to analyze the effect of each component of the system based on the results.
Need for Irrigation Water Management
Irrigation water use is by far the largest use of water by mankind worldwide. The ever-increasing water demand compared with the depleting water resources warrants refined water use practices in irrigated agriculture to attain improved socioeconomic benefits. In the past years the improvement in the irrigation system concentrated in the hardware component namely the physical aspects like structures but limited on the software part namely water usage for agricultural purpose.
Water is not a free commodity. With increasing standards of living and fast growing population, the available water resources may not be able to meet various demands of mankind. It becomes necessary to put the available resource more effectively for more benefits. It is unto the managers of water resources to devise ways and means of optimally using resources to meet the ever-increasing demand. The aim of efficient irrigation water management or precisely, maximum yield with available water. A good management, proper and timely application of water may result in better yield and reduction in drainage problems.
Fig 1: Match between water release and agriculture operations
Actually the irrigation system performance is characterized by the effectiveness of the irrigation throughout the season in a timely pattern averting the losses thereby improving the economic gains (Martin Hvidit, 1997). If the control over irrigation water and its distribution in time and space according to the cropping pattern can be exercised, the system output in terms of production and economic gains will be realized. On the other hand, the water availability in time and space will command the agricultural practices such as cropping pattern, calendar, etc. Such a synchronization of cultivation and irrigation is possible only when the water control in terms of adequacy, timeliness and uniformity is practiced in a command. For this purpose the irrigation system operation should be so devised to create a potential for high performance irrigation resulting in an application system that farmer can use to irrigate his field uniformly in right time, with right amount (Skewes M.A, 1998). With this viewpoint, government of India with assistance from World Bank launched the “National Water Management Program (NWMP) which aims at RELIABLE, PREDICTABLE AND EQUITABLE supply of water. The timely and reliable assessment and monitoring of water resources and systematic exploration and developing new ones is of paramount importance. Considering the stupendous task and the constraint of time, it is necessary to employ modern methods of surveying, investigations, design and implementation. Remote sensing and GIS are viewed as an efficient tool for irrigation water management.
Fig. 2: Analysis on the cultivation – irrigation practices
Remote sensing techniques are useful for Irrigation water management in the following areas (NRSA 1993)
- Assessment of water availability in reservoir for optimal management of water to meet the irrigation demand.
- Identifying, inventorying and assessment of irrigated crops.
- Determination of irrigation water demand over space and time
- Distinguishing lands irrigated by surface water bodies.
- Estimating crop yield
- Water logging and salinity problems in irrigated land
- Irrigation system performance evaluation
Fig. 3: Analysis on the crop performance
This primary information on the crop and water availability derived from remote sensing methods form a reliable databases for further investigation and analysis across space and time at desegregated levels of spatial parcels. This integration of remote sensing and ground inputs can be very effectively organized and analyzed in GIS environment. Such augmentation of the basic information of the system and the expert knowledge results in a system that addresses the present scenario of the system and future development in the system (Wolfe D.S , 1997). One of the advantages of GIS is that it gives a clear picture of effect of any action plan employed in the system through “if-then ” simulation.
Table 1. The performance report of three distributaries
|Performance indicators||9A distributary||12 distributary||15 distributary|
|Paddy yield (qtls / ha)||58.42||53.49||41.48|
|Paddy production / unit of water (kg / cu.m)||0.628||0.468||0.483|
Significance of the study
Here an attempt is made to integrate satellite-derived crop information with ground- collected inputs with respect to agricultural, irrigation and socioeconomic practices, in GIS environment for a diagnostic analysis of the irrigation system across space and time. Two systems were developed; one addressing the performance evaluation of the entire command at distributary level from satellite-derived crop information, yield model, water use efficiency, etc.; second addressing the diagnostic analysis of a few selected distributors to find the causative factors (Chari S.T et al, 1995,1996). Three distributor commands of Bhadra Irrigation system, Karnataka were selected for this study. This is an extension of system which addresses the performance evaluation of Irrigation command and compares the good and poor performance in distributaries reported by the first system. An analysis is carried out to analyze these distributories with respect to report practices add the performance to find out the causative factors for poor performance. This is to basically establish GIS as a tool aid analysis irrigation system at different levels and to help the time- manned location-wise decision-making process for enhancing the efficiency of the system.
Fig. 4: Analysis on fertilizer application
The Bhadra Dam is located across Bhadra river near Lakkavalli Village , Chikmagalur district of Karnataka state, at an elevation of 610m above MSL. Bhadra project comprises of a storage reservoir with a capacity of 2025 m3 a left bank canal and a Right Bank canal. The three distributories for the diagnostic analysis having no. 9A, 12 and 15 belonging to Malebennur branch canal of the right bank canal. The Malebennur branch canal is 48km in length and a full supply depth of 2.44m. The full supply discharge is 20.10 cusec and the irrigated area is 23,710 ha. The 9A distributory is in a very good condition with about 80% cement concrete lining. The minors are lined to some portion. The 12 and 15 distributaries are partially lined with about 10 m portion being lined on both sides of the PO. The 15 distributary is in a very poor condition with the entire portion of the channel being weeded and with lot of physical damages to the system. Farmers at the head reaches in all the three distributaries are going only for paddy and those at 15 distributary for crops like sugarcane and other semi-dry crops.
Fig. 5: Analysis on farmers involvement
Probable reasons for the contrasting performances of the distribution
Three distributaries selected for the study and their performance indicators are listed below in table 1
The following are the 15 probable reasons identified for the contrasting performances of the different distributaries under almost similar climatic and agro_ecological conditions:
- Realistic operational plan
- More water availability / unit area
- Better enforcement of the operational plan
- Better canal condition
- Less water stresses during the critical stages
- better soil fertility conditions
- Better water management
- Better application of fertilizers as per requirements
- Agricultural calendar in accordance with the water availability
- Better agricultural extension works
- Equitable distribution of water
- Pickup’s / alternate water sources
- Extent of NWMP works
- Better credit facilities
- Farmers NWMP involvement with regard to:
Design and organization of the detailed Questionnaire
To obtain all the important information for the diagnostic analysis, the questionnaire based on the experience gained from the reconnaissance survey is designed for the irrigation officials, agricultural authorities and the farmers soak s to obtain information from the relevant sources. The questionnaire to the irrigation authorities covers questions related to irrigation water management, canal conditions, interaction with the farmers and the agricultural officials. The questionnaire prepared to the agricultural authorities covers questions related to the crops to be grown in the study area, their interaction with the farmers and the irrigation officials, type of agricultural extension and other miscellaneous information regarding the soil fertility conditions in the study area. The questionnaire executed to The farmers covers apart from other information, information related to their identification, the details regarding the inputs and the production in the current season, their involvement in the various NWMP activities, their views about the irrigation and the agricultural authorities, and their knowledge about the various agricultural aspects.
Table – 2: Classes for farmers involvement index and their relative weightages
|Farmers awareness||Aware (1)||Not aware (1)|
|Farmers response to Irrigation||Schedule Good (1)||Not Good (2)|
|Status of Agricultural extension works||Good (1)||Not Good (2)|
|Farmers views in Irrigation department||Good (1)||Not Good (2)|
|Farmers views on Agricultural department||Good (1)||Not Good (2)|
Digital map base preparation
The digital map base preparation is the first step towards the presentation of a GIS module for the irrigation water management. The map base is prepared by scanning the topo maps and the available revenue survey maps of the three distributaries and are reduced to a scale of 1:16000 to 1:8000 and are digitized edited and corrected with respect to topo maps. From the base of revenue levels, the base maps of reach and distributary level are prepared. The three map bases are stored in 3 levels of GIS. The revenue survey numbers were given a reference numbers in the GIS map base so that the data transfer can be done. A total of 878 polygons was formed in the base map and was given sequential numbering. The distributory map base consists of 3 polygons, each representing one distributary. Each polygon was given a reference for data transfer. Similarly, the reach map base consists of 9 polygons and was also sequentially numbered.
Sampling procedure for the selection of the farmers
The questionnaire designed is to be executed for the farmers in the 3 distributaries to get the attribute data. However, the total number of farmers in the 3 distributaries sum up to 1000 forcing the sampling to ease the complexity involved in the collection of data. The sampling done for the present study is a sort of stratified random sampling based on the yield variability such that the reaches having high variability in yield have more samples. Once the number of farmers in a particular reach were selected, the further selection was totally based on random sampling so as to see that the farmers selected were having their evenly located within a reach.
Detailed field survey
A total of 100 farmers were selected for the detailed field survey. Apart from this, the questionnaires to the agricultural and the irrigation authorities were also executed to the concerned officials. The questionnaire to the farmers was executed by the team comprising of 4 scientists, two having water resources background and other 2 with agricultural background. During the interview, questions on farmer’s personal views and wishes were noted and the farmer’s personal experiences with the dept. people were also noted. The collection of data from the irrigation department was done by distributing the questionnaires containing field conditions and field operations, to various officials at different Levels. Office data in the form of discharge data, structural condition records and other relevant data were also collected. The questionnaire to the officials of the agricultural dept. was administered through personal interview to the asst. agricultural director. The data such as soil fertility tests records for the areas covering the 3 distributaries was also collected.
Interpolation of physical resources data
As a first step, the ground reported yield was interpolated using the weighted average interpolation technique to generate a yield map. This ground report yield map is cross checked with the satellite-driven yield to:
- Prove the validity of yield model and
- Estimate the error of interpolation
Ground report travel time of flow was also interpolated to get a general picture of the canal condition, the moisture stress to get the general picture of the moisture stress,the fertilizer application adequacy (fig. 3) to find out the spatial variability of fertilizer application which gives an idea about the effect on yield and the involvement of farmers in various extension works.
Cluster analysis for socio-economic data:
Socio-economic factors such as farmer’s awareness, response and involvement in various NWMP works and views on the various govt. agencies are spatialised by performing a cluster analysis using theissen polygon method.
Using third method, the following maps were generated
- Farmers awareness to the various program’s under NWMP
- Match between water delivery schedule and agricultural operation
- Farmers involvement in irrigation scheduling
- Status of agricultural extension works
- Farmers views on the responsiveness on the dept. of irrigation
These clustering enable the zonation of the study area into zones of farmer’s level of involvement which was studied for farmers awareness and responses to the various NWMP program’s. To obtain all the important information for the diagnostic analysis, the questionnaire based on the experience gained from the reconnaissance survey is designed for the irrigation officials, agricultural authorities and the farmers soak to obtain information from the relevant sources. Then systems such as digital base map preparation, sampling procedure for the selection of the farmers, detailed field survey, interpolation of physical resources data, cluster analysis for socio-economic data were performed.
Analysis on the primary and derived data
The data analysis was performed in two different environments. The primary or the first level analysis was performed in dBase III plus where the typical and most important trends for the performance of three distributaries were analyzed. The second level was in the GIS environment to cluster and group the inputs.
The following analysis were performed
- Water availability per unit area: Here the water at the take off point was taken as the conveyance and application losses were not measured. This analysis showed that water available per unit area for 15th distributory was less compared to the other areas. The further analysis was done now based on this as one of the main constraints.
- Principal Component Analysis: This was done to generate the indices showing the major causative component. Two types of indices namely Agricultural Productivity index (API), to study the effect of inputs on productivity and Farmers Involvement Index (FII) , to study the farmers participation In the irrigation activities.
- The API was derived based on the inputs related to water, seeds, etc. The Correlation of these factors with yield is taken weightage factor. API = W (w1) + F (w2) + s (w3) + P (w4)
API = Agricultural Productivity Index
W = Water Availability (moisture stress);
F = Fertilizer application adequacy
S = seeds quality;
P = Pesticides usage
w1 to w4 = respective weightages
A closer look at the API map (fig. 3) in conjunction with the yield map reveals that the inputs used by in the 15th distributary were not good resulting in the low yield.
- The FII was calculated by taking into consideration the awareness, response and involvement in the NWMP works. The weightages for various factors applying Boolean logic.
The above table 2 shows the classes for the various factors and the weightages for various factors.
The importance of this index, as shown by fig. 5 is related to the review of the farmers status and responsiveness Particularly with yield information, this would indicate the future coordination and education that has to be taken up for better results.
- Match between the agricultural calendar and the canal operation plans (fig. 1) were analyzed through the season for the three distributaries. From the graph below it can be observed that the two major and basic component of the system do not match in 12th and 15th distributory. Spatial mapping for this (fig 2) also done to examine the activities in the command.
- Water stress during the critical stages were also studied from the satellite and ground information. The table below gives an important conclusion that water stress in the 15th distributary was high which might have serious implications on the overall performance. The water stress in the 12th distributary were also high during flowering and the grain formation which could have had an impact on the yield.
- Spatial variability of the yield at different reaches of the distributaries were studied to locate the low yielding pockets and further analysis with respect to gap in structural and logistical components.
- The fertilizer application adequacy as informed by the field was mapped to understand the agricultural activities (fig 4) The fertilizer application was very varied from 20% to 140% suggesting the extent and the type of extension works.
- When analyzed in conjunction with yield details, the poor performance of the middle reach of the 15th distributary could be attributed to poor application.
- The agricultural operation reported from eh field were analyzed with respect to the canal delivery schedule and following observations made
- The match (fig 1) between water delivery scheduling and the agricultural operations was bad in the middle and tail reaches of 15th distributary.
- This was good in 12th distributary, could be attributed to the better yield figures.
Discussions on result
The timely and reliable assessment and monitoring of water resources and systematic exploration for development are of paramount importance. Considering the stupendous task and constraint of time in developing the ultimate irrigation potential, it is necessary to use the modern methods of surveying and analysis tools. Remote sensing and GIS with their capability of data collection and analysis are now viewed as efficient and effective tools for irrigation water management. The capability of GIS to analyze the information across space and time would help in managing such dynamic systems as irrigation systems. The study shows the efficacy of this tool to analyze the information on irrigation system in various domains in an integrated manner to understand the system and upgrade the activities. Much of the research has been done on augmenting the resource information and analyzing them for better management . It is also required to improve the methodologies to realize the field -important information in Gis environment.
In this study , an attempt has been made to analyze the irrigation system from the viewpoints namely, irrigation, agriculture and socio-economics. Any system needs to account for the human or socio-economic component to realize the fruit of the effort. The capability of SRS and GIS to address the complex spatial problems and to assist in spatial decision making process qualifies it to handle water resources problem. Water management will be successful only if the human part of the system is considered. This study shows that the operation plan and its adherence had a greater impact on the overall performance of the system. The analysis on the match between the operation plan and the agricultural operations (fig 1) illustrates the efficacy of the Gis to handle the data across space and time. The decision-making and statistical approaches used for formulating the indices that would have a positive relation to the performance within the system is one example of integration. The study also showed that various socio-economic patterns can be derived and studied for management purpose. One important observation is that the farmers views on the Government Agencies were better in the areas of good yield. This shows the direction to be taken by the governmental agencies to bring about higher and sustainable productivity.
The sampling procedure can be improved after studying various socio-economic patterns of the area under study to have faithful representation of the population. The stratification for sampling can be done on the basis of more than one criteria. More time and effort can be spent for the field survey and interview with farmers, as this is the basic input for the system. More time would have educated the farmers with respect to the importance of their response. More queries can be added to the questionnaire after consulting the experts in this particular field of study. The spatial statistical techniques applied to convert the farmers response to queries to surface pattern itself need to be studied in detail. This holds paramount importance in deriving the indices for diagnostic analysis. Also the indices derived have to refine with the help of socio-economic and statisticians. This particular area of spatial statistics has greater scope in the years to come.
I express my sincere thanks to Dr. S.T Chari, Group Director, Water resources division, National Remote Sensing Agency, who is the brain behind this study. I also appreciate the unabated enthusiasm shown by Mr. D.Srinivas to carry out the work as directed. I place on record my acknowledgement to Ms. Radhika and Ms. Shamla for their helping hand with ARC view software
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