Analytical Hierarchy Process Based Multi-Criteria Analysis And Influence Technique For Agricultural Development Of Adhala River Basin Village In Maharashtra (India)

Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 21 Analytical Hierarchy Process Based Multi-Criteria Analysis And Influence Technique For Agricultural Development Of Adhala River Basin Village In Maharashtra (India) Ravindra D. Gaikwad1, Pandharinath T. Karande2 1S. N. Arts, D. J. Malpani Commerce and B. N. Sarda Science College Sangamner, Department of Geography, Affiliated to Savitribai Phule Pune University, Pune, India 2Adv. M. N. Deshmukh Arts, Science and Commerce College Rajur, Department of Geography, Affiliated to Savitribai Phule Pune University, Pune, India Email: [email protected] Manuscript ID: JRD -2025-171105 ISSN: 2230-9578 Volume 17 Issue 11 Pp. 21-36 November. 2025 Submitted: 15 oct. 2025 Revised: 25 oct. 2025 Accepted: 10 Nov. 2025 Published: 30 Nov. 2025 Abstract Agricultural development is unique sign for development of agricultural base country. Multicriteria, Analytical Hierarchy Process (AHP) Based Multi-Criteria Analysis and Influence Technique is suitable for Agricultural Development (AD). Nine criterions Population (POP), Sex Ratio (SR), Total Irrigated Land Area (IL), Total Un-irrigated Land Area (UL), Forest (FOR), Culturable Waste Land (CWL), Area under Non-agricultural Uses (AUNA), Net Area Sown (NSA) and Rainfall (RF) were selected for development indicators of Adhala river basin village in Ahmednagar district, Maharashtra (India). Correlation Matrix use for ranking the criterion selected for influence. Total Un-irrigated Land Area, Net Area Sown and Population, show higher influences on Agricultural development of basin village arrangement in the study area. Further, Culturable Waste Land, Sex Ratio and Area under Nonagricultural Uses were show significant influence in basin. Using AHP techniques for influences were calculated based on weights estimated. Normalized and distribution of specific criteria using the values of influences within the basin village. Agriculture developments influence are classified into very low (< Mean-1STD), low (Mean-1STD to Mean), moderate (Mean to Mean + 1STD), high (Mean + 1STD to Mean + 2STD), and very high (>Mean + 2STD) and agricultural development are classified into high (25.02%), moderate (3.70%) and low (7037%) categories. The methodology is the effective tool for agricultural development of Adhala basin village. Keywords: AHP; Ranking; Multi-criteria; Influence; Weights. Introduction Agricultural development is one of the significant factors representing the overall development of the rural regions (Tschirley, 1998). Study area shows undulating surface and therefore varied agriculture and cropping pattern. The slope decreases towards the East from Western hilly region. Higher rainfall, steep slopes and dense forests are observed in western hilly which is the source of Adhala River. Paddy and Nachani are important crops in this area. Further, sugarcane, vegetables and fruits are observed in the eastern where the slopes are less. Gumma et al., 2016 have used weighted integration of multiple thematic layers, Gassman et al., 2007, Daloglu et al., 2014 have used Soil and Water Assessment Tool, Panhalkar, 2011 had use intersect overlay technique with GIS environment, Daloglu et al., 2014 have used agent-based models (ABM) with combination Soil and Water Assessment Tool, have used water balance of irrigation systems for Agricultural development (AD). Further, Analytical Hierarchy Process (AHP) based multi-criteria analysis and influence technique can be useful tool for quick AD prioritization of village. The criterions Population (POP), Sex Ratio (SR), Total Irrigated Land Area (IL), Total Un-irrigated Land Area (UL), Forest (FOR), Culturable Waste Land (CWL), Area under Non-agricultural Uses (AUNA), Net Area Sown (NSA) and Rainfall (RF) are useful parameters select for the period 1981 to 2011 for AD. Rice, Nachani and Varai is a rain fed crop grown in hilly slope and foothills area (Su et al., 2014). Quick Response Code: Website: https://jrdrvb.org/ DOI:: Creative Commons (CC BY-NC-SA 4.0) This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, which allows others to remix, tweak, and build upon the work noncommercially, as long as appropriate credit is given and the new creations ae licensed under the idential terms. Address for correspondence: Ravindra D. Gaikwad, S. N. Arts, D. J. Malpani Commerce and B. N. Sarda Science College Sangamner, Department of Geography, Affiliated to Savitribai Phule Pune University, Pune, India How to cite this article: Ravindra D. Gaikwad, Pandharinath T. Karande (2025). Analytical Hierarchy Process Based MultiCriteria Analysis And Influence Technique For Agricultural Development Of Adhala River Basin Village In Maharashtra (India) Journal of Research & Development, 17(11), 21-36. Original Article Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 22 However, due to the development of irrigation facilities in the area with reduced slope in the east cash crop have changed. River basin is a hydrological unit (Johnson et al., 2013). It is a unique bio-physical unit. of earth's surface including morphology, soil, surface water, surface geology, near surface atmosphere, vegetation influencing potentials of land use and result of past and present human activity, etc. (Bhagat, 2012; Wani et al., 2008). Watershed contours the natural resources including soil, water, vegetation as well as socio-eco-cultural resources (Ghanbarpour and Hipel, 2011). These resources are being exploited and degraded from last few decades due to over use for increasing population and there needs (UNSCO (2015), Perez and Tschinkel (2003), Iqbal and Sajjad, (2014), Joshi et al.,(2006), Wani et al.,(2011), Gajbhiye et al., 2014). Therefore, many governments, non-governmental agencies and personalities have invested their energies for conservation of these resources. Some of them have used watershed management techniques from decades for conservation of soils, groundwater, vegetation with increasing agricultural productivity (Giordano and Shah, 2014), increase soil moisture and protective irrigation (Bhagat, 2002, Pokharkar, 2011), increasing groundwater level (Pascual-Ferrer et al., 2013), reducing soil (Bhattacharyya et al., 2015) and vegetation degradation (Perez and Tschinkel, 2003, Bishop et al., 2012, Kaur et al., 2014) with public participation (Perez and Tschinkel, 2003, Montz, 2008, Ghanbarpour and Hipel, 2011, Swami et al., 2012, Giordano and Shah, 2014). Many projects have adopted the watershed management approach (Tiwari et al.,2008) for conservation for natural resources including soil, water, vegetation, etc. as well as socio-cultural resources for enriching the livelihood of rural peoples (Pangare, 1998, Willett and Porter, 2001, Ali et al.,2010). This is integration of protections and saving of resources (Rockstrom et.al. 2004). Morphometric analysis of river basin provides useful information for monitoring the groundwater (Kaushal and Belt, 2012, Swami et al., 2012 Jankar and Kulkarni, 2013), surface water (Li, 2009), degradation of soil and vegetation. Land use analysis is measurements and analysis of agricultural activy in relation of land surface, (Shing and Shing, 2011, Iqbal and Sajiad, 2014, Raja and Karibasappa, 2016). Theses parameters have been widely used for prioritization analysis of agricultural development at village and regional level. Study area: The basin Adhal River (19° 03' 41.8237'' N to 19º 33' 29.7577'' N and 73º 48' 19.2344'' E to 74º 11' 23.5511'' E)) in Ahmednagar district (India) distributed inside Akole, Sangamner tehsils was selected for agricultural development of Adhala basin (Figure 1). The River Adhala is main tributary of Pravara River and source region in Patta fort, near Kokanewadi village located in the Western Ghat. The height varies from 512 to 1472.7 m. and rainfall from 420 to 1620mm. Geologically the study area is the part of Deccan trap with compound pahoehoe, and som Aa flows, basaltic and Alluvium. Somewhat deep, drained, and calcareous soils on gentle sloping with moderate erosion. Rice is the main crop in the kharip season for the Western part whereas Grains like Ragi, Nagali, Varai, and Barly, Pulses like Pigeon Peas Skinned (Toor), Green Gram Split (Moong), Black Gram (Udid), Moth Bean (Matk)i, Horse Gram (Hulga),Pink Lentil (Masur), Pawta, Chauli Field Bean (Wal), Ghevda and Groundnuts are observed as major crops in the kharip season, Wheat, Maize and Sunflower, Vegetables like tomato, cabbage, green bean, cilantro, flowers, brinjal etc. in rabbi season for Eastern part. The Adhala basin has been covered 27 villages (Figure 1) for analysis and AD (Zende et al., 2013). Figure: 1 Study area Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 23 Methodology Analytical Hierarchy Process (AHP) based multi-criteria analysis and influence technique were used for AD of villages in Adhala River basin. The ranking (Table 7) of the criterion have been performed based on correlation matrix technique. The AD was performed through eight steps: 1) Delineation village boundary with help of Bhuvan shape file, 2) Data collection and analysis for selected criterion, 3) Ranking of the criterions, 4) Pairwise comparison matrix analysis, 5) Normalization of pairwise comparison matrix, 6) Calculations of weights, 7) Village wise normalization of calculated influences, and 8) Calculation of agriculture development according to the villages. 1 Data base and Software Data regarding selected criterion e.g. Population (POP), Sex Ratio (SR), Total Irrigated Land Area (IL), Total Un-irrigated Land Area (UL), Forest (FOR), Culturable Waste Land (CWL), Area under Non-agricultural Uses (AUNA), Net Area Sown (NSA) and Rainfall (RF) was procured from government censes records available at tehsil offices [Akole, Sangamner,] in the district for the year of 1981 and 2011 and used for multi-criteria and AHP analysis to calculate AD in the villages. GIS layers were prepared based on topographic maps (47E/14 and 47I/12) procured from SOI [survey of India]. NRSC, Bhuvan data was used for delineation of Village boundaries. The data and maps were loaded in GIS software for preparation of layers. 2 Criterions Population (POP), Sex Ratio (SR), Total Irrigated Land Area (IL), Total Un-irrigated Land Area (UL), Forest (FOR), Culturable Waste Land (CWL), Area under Non-agricultural Uses (AUNA), Net Area Sown (NSA) and Rainfall (RF) were used for multi-criteria analysis using AHP and influence technique to calculate the AD in the study area. The study area has naturally varies of rainfall, slope and soil. Rice, Sugarcane, vegetables, Grains, Pulses and fruits are economically important and principal crops in study area. Therefore, criterions (population, land use and rainfall) selects AD. 2.1 Population: The availability of human resources depends on composition of population. Characteristics of population in the region including density, number of child (age below 6 years) and old aged, population belongs to SC and ST, literacy and education and workforce were analyzed to understand the demography in study area (Fekete et al., 2019). The distribution of population characteristics were classified into five classes: Very low (< Mean-1STD), Low (Mean-1STD to Mean), Moderate (Mean + 1STD to Mean + 2STD), High (Mean + 1STD to Mean + 2STD) and Very high (>Mean + 2STD). Population change is significant demographic characteristic affects the AD in the region (Farley and Anna, 2014). Village wise population change has been calculated (Formula 1) and plotted on the map (Figure 2). The total population in the study area was 42410 in 1981 and 60111 in 2011 (Table 1). Total population in village 2011 (1 In 1981, about 16 villages were classified into the class, low population change (386.70 to1570.84), 5 villages into class Moderate change (1570.84 to 2755.44) (Table 2.4 and Figure 2.11), 03 into class high change (2755.44 to 3940.04), and 01 village into class very high (3940.04<) population change. The higher population change was observed in the areas belong to bank of rivers due to availability of water for irrigation and fertile soils. In 2011, 15 villages were classified into the class, low population change (349.76 to 2226.33), 08 villages into moderate (2226.33 to 4102.90) population change (Table 1 and Figure 2). The negative change in population from 1981 to 2011 was observed in the class <-196.86 and -196.86 to 655.59 wherever 14 villages show positive changes in population growth (Figure 2 and Table 1). Table 1: Distribution of population . Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <386.70 02 <349.76 01 <-196.86 02 Mean-1STD to Mean 386.70 to 1570.84 16 349.76 to 2226.33 15 -196.86 to 655.59 14 Mean to Mean + 1STD 1570.84 to 2755.44 05 2226.33 to 4102.90 08 655.59 to 1508.04 08 Mean + 1STD to Mean + 2STD 2755.44 to 3940.04 03 4102.90 to 5979.47 02 1508.04 to 2360.49 02 Mean + 2STD< 3940.04< 01 5979.47 < 01 2360.49< 01 Total Villages 27 27 27 Mean 1570.84 2226.33 655.59 STD 1184.60 1876.54 852.45 Maximum 5443 9449 4016 Minimum 0.00 368 -362 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 24 Figure: 2 Distribution of population 2.2 Sex Ratio: Sex ratio plays a significant role in tribal area binding human resources for development and growth of people’s life. The number of female workers is significantly related with the agricultural workers (Huisman et al., 2010). In 1981, 02 villages show very low (<381.63 female/1000 male) and 18 villages show low (381.63 to594.55 female/1000 male) sex ratio of population. The moderate sex ratio (594.55 to 807.47 female/1000 male) was observed in 05 villages and Dongargaon, Virgaon, Nagwadi, Poparewadi) show (>very high female/1000 male) sex ratio of. In these 30 years sex ratio in the region is slightly increased (Figure 3 and Table 2) in the areas of steep slopes, dry and shallow soils, greater erosion, heavy rainfall, low irrigation, lack of transport facility, lack of education awareness, etc. In the period of 1981 to 2011, 06 villages show very low changes <-208.24 female/1000 male) and 19 villages show low changes (208.24 to 690.5 female/1000 male) in sex ratio. The very higher changes (1657.02< female/1000 male) were observed in the western and parts of the Adhala basin village. Table 2: Distribution of Sex Ratio Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <381.63 01 <- 275.60 01 <-208.24 06 Mean-1STD to Mean 381.63 to 594.55 18 - 275.60to 1291.05 25 -208.24 to 690.5 19 Mean to Mean + 1STD 594.55 to 807.47 00 1291.05 to 2857.7 00 690.5 to 1172.76 01 Mean + 1STD to Mean + 2STD 807.47 to 1020.39 03 28.57.7 to 4424.35 00 1172.76 to 1657.02 00 Mean + 2STD< 1020.39< 05 4424.35 < 01 1657.02< 01 Total Villages 27 27 27 Mean 594.55 1291.05 690.50 STD 212.92 1566.65 482.26 Maximum 1063.69 9261.41 8798.81 Minimum 456.04 511.91 -72.03 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 25 Figure: 3 Distribution of Sex Ratio Distribution of Total Irrigated Land Area: The present analysis shows very low <-73.64 ha/100 ha) Irrigated Land Area in 05 villages, low (-73.64 to 120.96 ha/100 ha) in 13 villages, moderate (120.96 to 315.56 ha/100 ha) in 05 villages, high (315.56 to 510.16 ha /100 ha) in 03 villages and very high (510.16< ha/100 ha) in only 01 villages (Figure 4 and Table 3). In 2011, 01 village show very low (<-66.35 ha /100 ha) area under Irrigated Land, 25 villages show low (-66.3 to 114.18 ha/100 ha), 00 villages show moderate (114.18 to 294.71 ha/100 ha) and very high 9475.24< ha/100 ha) area under Irrigated Land (Figure 4 and Table 3). Further, the growth rate of area under Irrigated Land in 2011 is less than previous thirty years observed in Adhala basin villages from the western hilly part of the study area. 04 villages show very low (< -158.59 ha/100 ha) negative change from 1981 to 2011 and 03 villages show less (-145.01 to 296.81ha/100 ha) negative change. Table 3: Distribution of total irrigated land area Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <-73.64 05 <-66.35 01 < -158.59 04 Mean-1STD to Mean -73.64 to 120.96 13 -66.3 to 114.18 25 -158.59 to - 6.79 02 Mean to Mean + 1STD 120.96 to 315.56 05 114.18 to 294.71 00 -6.79 to 145.01 18 Mean + 1STD to Mean + 2STD 315.56 to 510.16 03 294.71 to 475.24 00 145.01 to 296.81 03 Mean + 2STD< 510.16< 01 475.24< 01 296.81< 00 Total Villages 27 27 27 Mean 120.96 114.18 -6.79 STD 194.60 180.53 151.80 Maximum 690 855 283.00 Minimum 0.00 0.00 -449.00 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 26 Figure: 4 Distribution of Total Irrigated Land Area Distribution of Total Un-irrigated Land Area: The present analysis shows very low <-209.57 ha/100 ha) Irrigated Land Area in 01 village, low (209.57 to 334.33 ha/100 ha) in 12 villages, moderate (334.33 to 878.23 ha/100 ha) in 07 villages, moderate (540.59 to 937.20 ha /100 ha) in 07 villages and very high (1422.13< ha/100 ha) in only 02 villages (Figure 5 and Table 4). In 2011, 03 village show very low (<143.98 ha/100 ha) area under unirrigated Land, 12 villages show low (143.98 to 540.59 ha/100 ha), 07 villages show moderate (114.18 to 294.71 ha/100 ha) and 01 village show very high (1333.81< ha/100 ha) area under unirrigated Land (Figure 5 and Table 4). Further, the growth rate of area under unirrigated Land in 2011 is less than previous thirty years observed in Adhala basin villages from the western hilly part of the study area. 04 villages show very low < -283.84 ha/100 ha) negative change from 1981 to 2011 and 05 villages show less 253.86 to 522.71 ha/100 ha) negative change. Table 4: Distribution of Total Un-irrigated Land Area Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <-209.57 01 <143.98 03 < -283.84 04 Mean-1STD to Mean -209.57 to 334.33 12 143.98 to 540.59 12 -283.84 to - 14.99 09 Mean to Mean + 1STD 334.33 to 878.23 07 540.59 to 937.20 07 -14.99 to 253.86 09 Mean + 1STD to Mean + 2STD 878.23 to 1422.13 05 937.20 to 1333.81 04 253.86 to 522.71 05 Mean + 2STD< 1422.13< 02 1333.81< 01 522.71< 00 Total Villages 27 27 27 Mean 334.33 540.59 -14.99 STD 543.90 396.61 268.85 Maximum 2217 1547 517 Minimum 7.19 0.00 -760 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 27 Figure: 5 Distribution of Total Un-irrigated Land Area Distribution of forest Forest is significant resource for human activities spatially in the tribal area for food and other economic activities. The forest cover has more impact on hydrological cycles, soil conservation, climate change and the biodiversity crisis (Rudela et al., 2005). The western part of the study area is hilly and covered by dense forest whereas eastern part shows sparse vegetation with thorny bushes and grass. In 1981, 06 villages were classified in the class, very low (<16.88ha), 11 villages were classified in the class, low (16.88 to 274.91 ha) area covered under forests. 05 villages were classified in the class, high and have good forest cover, (566.70 to 858.71ha) village like Devthan, Sawargaonpat, Muthalne and Nagwadi show high 566.70 to 858.71 ha) area covered by forest. In 2011, 04 villages were classified in the class, very low <13.98 ha), 11villagesin the class, low (13.98 to 228.42ha) and 05 villages in the class, moderate (228.42 to 470.82 ha) forest cover (Table 5 and Figure 6). Change in area under forest cover in 1981 to 2011 was observed as: 02 villages show very less < -175.67 ha) negative change and 19 villages show less (-46.48 to 82.71ha) change in the forest cover (Table 5 and Figure 7). Table 5: Distribution of area under forest Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <16.88 06 <13.98 04 < -175.67 02 Mean-1STD to Mean 16.88 to 274.91 11 13.98 to 228.42 11 -175.67 to - 46.48 05 Mean to Mean + 1STD 274.91 to 566.70 05 228.42 to 470.82 05 -46.48 to 82.71 19 Mean + 1STD to Mean + 2STD 566.70 to 858.71 04 470.82 to 713.22 06 82.71 to 211.90 01 Mean + 2STD< 858.71< 01 713.22< 01 211.90< 00 Total Villages 27 27 27 Mean 274.91 228.42 -46.48 STD 291.79 242.40 129.19 Maximum 1105.37 765 87 Minimum 0.0 0.00 -608.37 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 28 Figure: 6 Distribution of area under forest Distribution of Culturable Waste Land: The culturable waste land is potential land for agriculture. In the study area (1981), 11 villages showed <-58.91 ha area having potential of agriculture, 09 villages show -58.91 to 52.08 ha area under the class. 5 villages have higher percentage of culturable waste (Figure 7 and Table 6). In 2011, the farmers of the region have converted some portion of the land into agriculture therefore the number of watershed decreased from higher categories to lower one as: 13 villages from the class, very low <-17.49 ha) 08 villages from the class, low (-17.49 to 19.97 ha), (Figure 7 and Table 6). The negative change in the area under culturable waste was observed in the most of villages in the basin. However, 21 villages showed highly positive change in culturable waste lands (Figure 7 and Table 6). It means that the culturable waste lands are going to be converted to the agricultural use (Deepak et al., 2016). Table 6: Distribution of Culturable Waste Land Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <-58.91 11 <-17.49 13 < -148.94 03 Mean-1STD to Mean -58.91 to 52.08 09 -17.49 to 19.97 08 -148.94 to - 32.12 02 Mean to Mean + 1STD 52.08 to 163.07 05 19.91 to 57.43 02 -32.12 to 84.70 21 Mean + 1STD to Mean + 2STD 163.07 to 274.06 01 57.43 to 94.89 02 84.70 to 201.52 01 Mean + 2STD< 274.06< 01 94.89< 02 201.52< 00 Total Villages 27 27 27 Mean 52.08 19.97 -32.12 STD 110.99 37.46 116.82 Maximum 518.81 155 96 Minimum 0.0 0.00 -501.71 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 29 Figure: 7 Distribution of Culturable Waste Land Distribution of Net Area Sown: The quantity of land under net sown area for cultivation describes the impact of agriculture (Wooda et al., 2004). In the study area (1981), 01 village show in the class, very low (<184.37 ha), 18 villages showed in the class, low (184.37 to 720.28 ha), 03 villages showed in the class, moderate (720.28 to 1276.19 ha), 03 villages showed in the class, high (1276.19 to 18120.10 ha) and 2 villages showed in the class, very high (1812.10< ha) (Table 7 and figure 8). In 2011, 03 villages showed very less (<247.95ha) area in the class, 10 villages showed in the class, low (247.95 to 654.76ha),9 villages showed in the class, moderate (654.76 to 1061.57ha) and 04 villages observed in the class, high (1061.57 to 1468.28ha) of area available for cultivation (Table 7 and Figure 8). About 5 villages show the negative change in this land use type. 15 villages in class, moderate (-75.51 to 138.64ha) are available for cultivation. However, 3 villages showed highly (138.64 to 352.79ha) positive change, in land available for cultivation (Table 7 and Figure 8). The positive change in this area into agricultural land was observed in 3 villages near to river and very high conversion of this land was observed in 3 villages. Table 7: Distribution of Net Area Sown Classes 1981 2011 1981to 2011 Change Influence values No. of Villages Classes No. of Villages Classes No. of Villages < Mean-1STD <184.37 01 <247.95 03 < -289.66 05 Mean-1STD to Mean 184.37 to 720.28 18 247.95 to 654.76 10 -289.66 to - 75.51 04 Mean to Mean + 1STD 720.28 to 1276.19 03 654.76 to 1061.57 09 -75.51 to 138.64 15 Mean + 1STD to Mean + 2STD 1276.19 to 18120.10 03 1061.57 to 1468.28 04 138.64 to 352.79 03 Mean + 2STD< 1812.10< 02 1468.28< 01 352.79< 00 Total Villages 27 27 27 Mean 730.28 654.76 -75.51 STD 545.91 406.81 214.15 Maximum 2241.07 1861 303.20 Minimum 7.10 198 -691.26 Journal of Research and Development A Multidisciplinary International Level Referred and Double Blind Peer Reviewed, Open Access ISSN : 2230-9578 | Website: https://jrdrvb.org Volume-17, Issue-11| November2025 36 20. Giordano, M. and Shah, T. (2014). From IWRM Back to Integrated Water Resources Management. International Journal of Water Resources Development, 30(3), 364-376. 21. Gumma, M. K. G., Birhanu, B. Z., Mohammed, I. A., Tabo, R. and Whitbread A. M. (2016). Prioritization of Watersheds Across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning. Water, 8(260), 1-17. 22. Huisman, J. and Rani, U. S. (2010). School Characteristics, Socio-Economic Status and Culture as Determinants of Primary School Enrolment in India. Nijmegen Center for Economics (Nice) Institute for Management Research Radboud University Nijmegen, P.O. Box 9108, 6500 HK Nijmegen, The Netherlands. 23. Iqbal, M. and Sajjad, H. (2014). Watershed Prioritization Using Morphometric and Land Use/Land Cover Parameters of Dudhganga Catchment Kashmir Valley India Using Spatial Technology. Geophysics and Remote Sensing, 3(1), 1-12. 24. Iqbal, M. and Sajjad, H. (2014). Watershed Prioritization Using Morphometric and Land Use/Land Cover Parameters of Dudhganga Catchment Kashmir Valley India Using Spatial Technology. Geophysics and Remote Sensing, 3(1), 1-12. 25. Jankar, P. D. and Kulkarni, S. S. (2013). A Case Study of Watershed Management for Madgyal Village. International Journal of Advanced Engineering Research and Studies, 2(4), 69-72. 26. Johnson, J. N., Govindaradjane, S. and Sundararajan, T. (2013). Impact of Watershed Management on the Groundwater and Irrigation Potential: A Case Study. International Journal of Engineering and Innovative Technology, 2(8), 42-45. 27. Joshi, P. K., Vasudha, P., Shiferaw, B., Wani, S. P., Bouma, J. and Scott, C. (2006). Socioeconomic and Policy Research on Watershed Management in India Synthesis of Past Experiences and Needs for Future Research. SAT eJournal, 2(7), 1-81. 28. Kaur, M., Singh, S., Verma, V. K. and Pateriya, B. (2014). Quantitative Geomorphological Analysis and Land Use / Land Cover Change Detection of Two Sub-Watersheds in Ne Region of Punjab, India. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 49(8), 371-375. 29. Kaushal, S. S. and Belt, K. T. (2012). The Urban Watershed Continuum: Evolving Spatial and Temporal Dimensions. Urban Ecosystem, 15(2), 409-435. 30. Li Z., Fang, Y. G., Li, J., Zhang, Q., Yuan, Q. L., Wang, Y. and Ye, F. (2009). Temporal and Spatial Characteristics of Surface Water Quality by an Improved Universal Pollution Index in Red Soil Hilly Region of South China: A Case Study in Liuyanghe River Watershed. Environmental Geology, 58(1), 101-107.