EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 62 EXPLORING OPTIMAL CEREAL CROP SEQUENCE USING CULTIVATED LAND UTILIZATION INDEX AND YIELD IN KATIHAR DISTRICT, INDIA: A SUB DIVISION LEVEL ANALYSIS Vinay Prasad Mandal a Raihan Ahmad a Sufia Rehman a Md Masroor a, Haroon Sajjad a  a Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India  haroon.geog@gmail.com (Corresponding author) ARTICLE HISTORY: Received: 08-Feb-2019 Accepted: 16-Apr-2019 Online Available: 03- May-2019 Keywords: Crop sequence, cultivation land utilization index, yield, NPK, Katihar ABSTRACT The present study attempts to identify the suitable crop sequences in an agriculturally productive district of Bihar, India. Rice-rice- vegetables, rice-maize-rice, rice-maize-maize, rice-maize-jute, rice- pulse-rice, rice-pulse-jute, rice-pulse-potato, and maize-maize-rice were found to be the suitable crop sequences in the area under study. Findings revealed that most of the blocks were found suitable for suggested crop sequences, while only two blocks (namely Balrampur and Barsoi) showed low potential in the rice-maize-rice sequence. Use of farm machinery, application of efficient farm inputs, and irrigation is required to maximize the yield and achieve long-term agricultural sustainability in this district. Contribution/ Originality This study intends to identify the suitable cropping sequence for achieving a long-term sustainability in agriculture, in the Katihar district of Bihar, India. This study tried to establish the relationship of yield with cultivated land utilization index (CLUI), soil bulk density, and NPK, for suggesting suitable crop sequence for sustainable agricultural development in the district. DOI: 10.18488/journal.1005/2019.9.1/1005.1.62.81 ISSN (P): 2304-1455/ISSN (E):2224-4433 How to cite: Vinay Prasad Mandal, Raihan Ahmad, Sufia Rehman, Md Masroor and Haroon Sajjad (2019). Exploring optimal cereal crop sequence using cultivated land utilization index and yield in Katihar district, India: a sub division level analysis. Asian Journal of Agriculture and Rural Development, 9(1), 62-81. © 2019 Asian Economic and Social Society. All rights reserved. Asian Journal of Agriculture and Rural Development Volume 9, Issue 1 (2019): 62-81 http://www.aessweb.com/journals/5005 mailto:haroon.geog@gmail.com http://www.aessweb.com/journals/5005 http://crossmark.crossref.org/dialog/?doi=10.18488/journal.1005/2019.9.1/1005.1.62.81 Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 63 1. INTRODUCTION Agriculture has always been an important source of livelihood to the rural masses in India. Increasing population pressure on resources has been a barrier to achieving agricultural sustainability, especially in rural areas. Identification of suitable crop sequence and efficacious measures to increase agricultural yield, is necessary for sustainable agricultural development. Agriculture has gone through many stages over time. It does not only provide livelihood, but also plays a key role in the manufacturing and industrial sectors (Sajjad et al., 2014). Agriculture has made rapid strides over the years, and many countries like Russia federation, India, China, Brazil and Indonesia, have started participating in international agricultural markets. Export of agricultural commodities in these nations have increased from 9.4% to 20% between 2000 and 2015. A similar pattern was also observed in imports (FAO, 2018). However, at the same time agriculture has also been affected by urbanization, industrialization and climate change. The reducing level of GHGs, has also increased conflict for land and water resources (FAO, 2017). Nearly 33% farms in dry lands have been highly degraded affecting overall livelihood, health of the ecosystem, food security and impoverishment of farming communities, globally (FAO, 2014). Hence, achieving agricultural sustainability with less resource intensification is a great challenge, particularly for developing countries like India. Fulfilling the demand of 10 billion people for fibre, fuel and food by 2050 is a difficult task. For achieving long-term sustainability in agricultural production, it must be grown at an average rate of 1.75% by 2050 (Global Agricultural Productivity Index, 2018). It is also anticipated that food demands would reach 60% by 2050. Therefore, effectual farming practices are essential to make production sustainable (Liebisch et al., 2014). Characterization of farming system refers to the assessment of nature and traits of farming system, in terms of inputs like irrigation, fertilizers, high yielding variety of seeds, insecticides, pesticides, land use and quality of soils, etc. It is also essential to assess the cropping pattern, crop combination, crop intensity, crop diversification, utilization of cultivated land and yield, when suggesting long term sustainability of agricultural systems. Characterization of a farming system is significant, especially in a diversified farming system. Adoption of new techniques play an important role in achieving such characterization (Timler et al., 2014). Indian agriculture has experienced tremendous changes in the previous century. Increase in food production exceeded the population growth since in the 1960s (Sajjad et al., 2014). Earlier, the emphasis were laid on ensuring self-sufficiency in production, which was somehow achieved. This massive production resulted to various socio-environmental implications, ranging from social lamentation to degrading land and natural resources (Abrol and Sangar, 2006). The size of most of the landholdings in India are small, resulting to meagre income of the farmers. Of the total landholdings, nearly 86% of the farmers belong to the small and marginal farmers’ category (Iqbal, 2018). Characterization of farming system requires assessment of land use/land cover modification. Land cover transformation affects fallow cycles, cropping pattern, crop rotation, etc. The main features of land cover modification are increasing land use production and intensification of land use. This land intensification has direct impact on cropping intensity which simultaneously helps in understanding the relationship between natural resources, people and sustainable land resource management (Zhang and Li, 2016). Agricultural sustainability at farm level can be helpful in creating a holistic perspective of ecological, social and economic development (Sajjad and Nasreen, 2016). Cropping pattern, cropping system and cropping intensity are significant parameters of farming system characterization, determined by the climate, soil, nourishment and other farm level components (Das, 2006). Land holdings and regional characteristics, determine the cropping pattern as monoculture, mixed cropping, double cropping and sequential cropping. Farmers with less land holdings are vulnerable to climate change, fragmentation of land, and lesser earnings. Diversified mixed cropping can significantly minimize the vulnerability of farmers, by improving Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 64 the crop yield and derived benefits (Amejo et al., 2019). Cropping intensity is mainly defined as the ratio of output and input in an agricultural system (Ruiz-Martinez et al., 2015). Assessment of cropping intensity will also help in attaining agricultural productivity, while maintaining the sustainability of the environment (Fan et al., 2014). It is generally believed that agriculture may lead to environmental problems, which is not usually the case. Prudent agricultural activities can significantly provide solutions to various environmental problems, but monitoring has several implications. However, agricultural monitoring is more difficult than other economic sectors (Dumanski and Pieri, 2000). Various scholars have examined agricultural intensity in terms of single components like micronutrients (Kleijn et al., 2008; Overmars et al., 2014) and pesticides (Ruiz-Martinez et al., 2015). Several consensuses among scientific studies were made to characterize the farming system (Hess and Hoskinson, 1996; Van et al., 2010; Alemu, 2016). Bembridge (1987) analysed the various constraints of a farming system namely, unscrupulous inputs, technological barriers and small landholdings. Knowledge of crops with adequate technological support were identified as the two important determinants of a crop farming system. Remote sensing data is playing an important role in identifying the cropping types, cropping intensity and plant phenology at various scales (Fan et al., 2014). Geospatial techniques have emanated as significant tools to prepare spatial inventory of cropping pattern, land use characteristics, examining the biophysical characters and land resources (Choudhury et al., 2006). Various indices obtained from high resolution satellite data as leaf area index (LAI), leaf water content (LWC), canopy cover (CC), plant chlorophyll content and normalized difference vegetation index (NDVI), etc. can help in identifying inherent cropping characteristics (Liebisch et al., 2014). Chatterjee et al. (2015) characterized the farming system and farm typology, using multivariate statistical, cluster and principal component analyses. They emphasized that identification of farm typology may significantly reduce the problems of farmers and can be indelible to provide better livelihood opportunities. Assessment of soil and land use characteristics are indispensable for increasing the productivity. NDVI and assessment of ground cover derived from satellite data, provides empirical information regarding the land and soil quality (Sheffield and Morse-McNabb, 2015). Land suitability is carried out when assessing the sustainability of agriculture. It is focused on the assessment of the potentiality of land, for specific utilization (Jamil et al., 2018a). Zhang and Li (2016) examined the agrarian land use intensity of varied characteristics using principal component and energy analyses in Beijing, China. Jamil et al. (2018b) examined the cropland suitability through GIS based multi-criteria decision-making, using site-specific parameters in the Bijnor district of Uttar Pradesh, India. Singh et al. (2013) characterized the farming system in southern arid lands of Rajasthan, using field derived samples. They identified that lack of farming techniques and planting methods hindered the farming efficiency. Sajjad et al. (2014) examined the spatial and temporal variability of agricultural sustainability in the Vaishali district, using sustainable livelihood security index (SLSI) at block level. Bio-intensive farming requires appropriate cropping pattern, techniques and agricultural inputs in order to maintain the output sustainability. Sajjad and Prasad (2014) evaluated the crop diversification in the Jalandhar district of Punjab, India using the Gibs and Martin index. They identified that, declining crop diversification may affect farmers’ income, nutrients, natural resources and ground water, etc. Rajbhandari (2011) evaluated the efficiency and relationship among land utilization index (LUI), rotational intensity and yield efficiency for analysing the bio- intensive farming system. Katihar is a district of Bihar in India, known for its agricultural productivity and yield. Agriculture is the mainstay of the economy, and provides livelihood to the rural masses. Analysis of present cropping pattern, is essential in suggesting alternate crop-based farming systems. Thus, this study intends to characterize crop farming, based on the land utilization index. This study also tried to examine the relationship between cultivated land utilization index (CLUI), soil bulk density, NPK and yield, in suggesting a suitable crop sequence for sustainable agricultural development. Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 65 1.1. Study area Katihar is a district in Bihar, India covers an area of 3057 km2 lies between 25°13' to 25°53' north latitudes and 87°12' to 87°04' east longitudes (Figure 1). The district comprises 16 blocks (administrative division of the district). It relishes a warm temperate climate with three distinct seasons winter, summer and rainy seasons. Average temperature and rainfall of the district is 24 °C and 1281 mm. Monsoon showers take place during June to September which leads to increase in water level up to 50 times higher causing devastating floods. Numerous tributaries of Kosi and Mahananda Rivers flow across the study area. Major soils in the district are Haplic Fluvisols, Stagnic Cambisols, Fluvic Cambisols, Haplic Cambisols, Endogleyic Cambisols and Haplic Arenosols (Reza et al., 2017). Nearly 2.41 lakhs people reside in the district. Agricultural population comprises nearly 11279 and marginal workers around 13049 (Census, 2011). Agriculture is the major source of livelihood to the rural masses as this district lies in Mid Gangetic plane region of agro-climate zone. Of the total area of district (291 ha) nearly 146 ha are cultivable and 39 hac are non-agricultural lands while 10 ha came under miscellaneous tree crops and groves, 22 ha under barren and uncultivable and 1.7 ha area came under forest (Agriculture Contingency Plan for District, 2012). Katihar is one of the agriculturally advanced districts of Bihar in India. The farmers grow crop during all the three seasons (Rabi, Kharif and Zaid). Figure 1: Location map of the study area 2. METHODOLOGY For identifying the land use under agriculture, land use/land cover map was prepared from Sentinel 2A data through supervised classification. Cropping pattern was identified using Sentinel 2A data and verified with GPS locations. Maps of cropping pattern in kharif (rainy season crop), rabi (winter season crop) and Zaid (short season between kharif and rabi seasons) seasons were used to examine the crop sequence in the study area. Cultivated land utilization index (CLUI) is essential for assessing the utilization of efficiency and designates where the land is being utilized. It is generally represented in percentage or fraction. If the value of CLUI is 1 it shows that the land is fallow if it is more than 1 then it represents the Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 66 relay and intercropping (Rana and Rana, 2011). CLUI assists in identifying the cropping sequence and to increase the land productivity for agriculture (Panigrahy et al., 2005). Block wise CLUI was calculated to identify the land utilization in kharif, rabi and Zaid seasons as: 𝐶𝐿𝑈𝐼 = 𝑎1𝑏2 𝐴 × 365 × 100 Where, 𝑎1 refers to area occupied by ith crop, 1,2 is the total number of crops, A is the total cultivated land area available for 365 days. Relationship of yield with soil bulk density, nitrogen, phosphorus and potassium (NPK) and irrigation was determined to distinguish the suitable cropping sequence in the study area. The detailed methodology is presented in Figure 2. Figure 2: Methodological framework of the study 3. RESULTS AND DISCUSSION Of the total geographical area (183160 hectares), nearly 60% is under agriculture including fallow land (Table 1 and Figure 3). Not all the area under agriculture is utilized in all the seasons because of low rainfall and non-availability of irrigation. Nearly 133939 ha is utilized for agriculture during Kharif season, 119910 ha during Rabi season and 128574 ha in Zaid season. Largest area under agriculture is utilized during Kharif season mainly due to monsoon rainfall. Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 67 Table 1: Block wise area under various classes of land use/land cover in Katihar district (2017) Block Area in Ha Agricultural & fallow land Built-up Land Plant/Forest Scrub and Grass Wetlands Rivers Total Azamnagar 16460 3170 3600 2630 1230 1420 28500 Amdabad 10420 3100 1440 1040 490 3700 20200 Barari 20940 3560 2280 1430 750 3890 32900 Balrampur 9500 2700 2090 1550 720 1120 17700 Barsoi 19200 2350 2860 1790 950 1000 28200 Dandkhora 4600 1210 1280 1000 450 520 9100 Falka 11440 580 2450 1490 810 150 16900 Hasangunj 3890 930 850 680 290 400 7100 Kadwa 20960 3350 3990 2560 1340 1650 33900 Katihar 5890 1710 1300 1080 460 610 11000 Korha 21980 1160 3210 1930 1060 580 29900 Kursela 6520 2360 800 530 260 2310 12800 Manihari 12020 4340 2050 1330 690 4310 24700 Mansahi 5040 640 900 620 300 420 7900 Pranpur 7690 1950 1930 1370 670 1740 15300 Sameli 6610 370 1280 810 430 150 9600 Total 183160 33480 32310 21840 10900 23970 305700 Source: Authors’ own calculations based on Sentinel 2A data (January, 2017) Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 68 Figure 3: Land use/land cover of Katihar district (2017) Major crops in the kharif season are rice, maize, pulses and vegetables (Figure 4A). Wheat, rice, maize, mustard, pulses, banana and vegetables are the dominant crops in rabi season in the study area while rice, maize, jute and pulses are important crops in the Zaid season (Figure 4B & C). Crop land utilization index (CLUI) is a measure to understand the availability of agricultural land for whole year. However, it also determines the land occupancy for crop during a particular season. Therefore, CLUI was calculated for both seasonal and annual cultivation practices (Table 2). For Kharif season high CLUI was found in Sameli, Pranpur, Mansahi, Korha, Katihar, Kadwa, Hasangunj, Dandkhora, Azamnagar and Balrampur blocks of the district. Falka, Amdabad, Manihari, Barari and Kursela blocks have recorded moderate CLUI while low CLUI was found in Barsoi. In Rabi season high CLUI was observed in Amdabad, Manihari and Pranpur blocks of Katihar district. Moderate CLUI was found in Barari, Kadwa, Mansahi, Kursela, Korha, Dandkhora and Hasangunj blocks while Falka, Sameli, Azamnagar, Balrampur, Katihar and Barsoi blocks have experienced low CLUI in Rabi season. High CLUI in Zaid season was found in Pranpur, Balrampur, Azamnagar, Dandkhora, Hasangunj, Kadwa, Amdabad, Katihar and Barsoi blocks of the districts. Moderate CLUI was observed in Mansahi, Manihari, Falka, Sameli and Korha blocks while low CLUI was found in Barari and Kursela blocks. High annual CLUI was found in Pranpur, Amdabad, Dandkhora, Kadwa. Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 69 Table 2: Block wise cultivated land utilization index during different seasons in Katihar district Blocks Kharif Rabi Zaid Annual Net shown CLUI Net shown CLUI Net shown CLUI CLUI Crop Intensity Azamnagar 14297.50 0.26 8448.00 0.14 15432.00 0.30 0.73 245.08 Amdabad 5873.00 0.17 8584.00 0.22 8506.00 0.26 0.78 264.01 Barari 15476.50 0.22 15958.00 0.20 7141.00 0.11 0.62 210.31 Balrampur 9474.90 0.30 4934.00 0.14 9302.00 0.31 0.74 249.85 Barsoi 2250.80 0.04 8977.00 0.13 14499.00 0.24 0.66 222.27 Dandkhora 4434.60 0.29 2887.00 0.17 4348.00 0.30 0.76 257.28 Falka 5181.60 0.14 6666.00 0.16 7226.00 0.20 0.66 221.43 Hasangunj 3863.50 0.30 2531.00 0.17 3244.00 0.27 0.74 248.46 Kadwa 17575.40 0.25 15235.00 0.20 17448.00 0.27 0.76 255.93 Katihar 5830.00 0.30 3052.00 0.14 4515.00 0.25 0.68 228.47 Korha 19707.50 0.27 15133.00 0.18 12039.00 0.18 0.66 223.62 Kursela 4448.20 0.21 4654.00 0.19 1736.00 0.09 0.59 198.01 Manihari 8656.40 0.22 9463.00 0.21 7851.00 0.21 0.72 244.04 Mansahi 4575.10 0.27 3719.00 0.20 3534.00 0.22 0.72 243.91 Pranpur 7112.40 0.28 6058.00 0.21 7666.00 0.32 0.82 278.47 Sameli 5181.60 0.24 3611.00 0.15 4087.00 0.20 0.64 216.46 Total 133939.00 0.22 119910.00 0.18 128574.00 0.23 0.70 235.66 Source: Authors’ own calculations based on Sentinel 2A data (January, 2017) Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 70 Balrampur and Hasangunj blocks. Moderate annual CLUI was found in Azamnagar, Manihari, Mansahi and Katihar blocks whereas Barsoi, Falka, Korha, Sameli, Barari and Kursela blocks have experienced low annual CLUI. From the above results it is very clear that CLUI in Katihar district is not homogeneous due to rainfall availability and irrigation potential. The farmers are bound to carry out their agricultural practices influenced by rainfall, flood, irrigation potential and land quality. Various crops are grown during kharif season in the district specially rice, maize, pulses and vegetables. Number of crops grown are more during rabi season than kharif due to variation in topography and flood which includes wheat, rice, maize, mustard, pulses and vegetables. Rice, maize, Jute, pulses and vegetables are dominant crops during Zaid season. Crop sequence in a year was identified to suggest a suitable crop farming system in Katihar district. Maps of cropping pattern during different seasons were prepared using remote sensing data. Based on those thematic maps, crop sequence was identified and area occupied by those crop sequences was determined. Crop yield data of all blocks were used to identify the suitable crop sequences (Table 3). The combined average yield for three seasons of 150 q/ha was used as a lower margin for selection of crop sequences. In this way eight sequences were suggested for sustainable farming system. These sequences are rice-rice-vegetables, rice-maize-rice, rice-maize- maize, rice-maize-jute, rice-pulse-rice, rice-pulse-jute, rice-pulse-potato and maize-maize-rice. Yield and CLUI for these sequences were also calculated for every block in the district (Table 4 and Table 5). Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 71 Figure 4: Cropping pattern in Katihar district: A) Kharif (during 2016), B) Rabi (during 2017) and C) Zaid (during 2017) Table 3: Yield of crops during Rabi, Kharif and Zaid seasons in the study area Blocks Kharif Yields (Q/ha) Rabi crop Yields (Q/ha) Zaid Crop Yields (Q/ha) Rice Maize Pulses Vegetables Wheat Rice Maize Mustard Pulses Vegetable Potato Rice Maize Jute Pulses Vegetables Azamnagar 32.67 92.86 7.72 150.52 28.89 47.65 133.45 12.35 8.47 188.1543 262.1055 24.7128 62.860 30.99286 8.09381 123.5235 Amdabad 25.57 0.00 8.92 144.68 25.32 40.54 137.57 14.27 9.67 180.8528 284.5717 26.2106 0.000 36.40361 9.29620 117.6823 Barari 22.64 84.73 6.05 135.33 21.33 34.41 0.00 9.68 6.64 169.1651 217.7081 23.5360 74.730 24.28692 6.34507 108.332 Balrampur 32.85 0.00 7.44 137.79 26.34 47.83 140.81 11.91 8.19 172.2408 247.1281 22.4662 0.000 29.74976 7.81757 110.7926 Barsoi 29.10 0.00 8.12 149.03 25.58 44.07 0.00 12.98 8.86 186.2821 256.8634 23.2151 0.000 32.77487 8.48982 122.0257 Dandkhora 23.20 92.97 4.90 108.27 19.68 34.97 106.78 7.84 5.49 135.332 209.4705 19.4172 72.967 19.11196 5.19507 81.26563 Falka 24.20 92.97 5.78 147.10 19.25 35.97 108.27 9.25 6.37 183.8751 250.0701 24.1244 62.967 23.07079 6.07482 120.1 Hasangunj 22.13 87.67 5.64 110.62 18.83 33.90 109.01 9.02 6.22 138.274 234.7717 19.1230 77.672 22.41599 5.92930 83.61923 Kadwa 21.21 84.73 6.15 112.97 18.41 32.98 107.97 9.83 6.73 141.216 247.1281 22.3592 74.730 24.71625 6.44047 85.97283 Katihar 20.30 82.20 6.95 135.33 17.99 32.07 106.78 11.12 7.54 169.1651 234.1833 17.6520 72.200 28.33721 7.24513 108.332 Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 72 Source: Authors’ own calculations based on field survey (2016-2017) Table 4: Block-wise yield of sequential crops in Katihar district Blocks Rice-rice- vegetable Rice-maize- rice Rice-maize- maize Rice-maize- jute Rice-pulse- rice Rice-pulse- jute Rice-pulse- potato Maize-maize- rice Azamnagar 176.91 173.83 211.98 180.11 315.49 321.77 298.87 238.02 Amdabad 169.46 179.34 153.13 189.54 336.35 346.54 319.43 153.78 Barari 156.51 178.99 230.19 179.74 265.89 266.64 248.70 239.08 Balrampur 164.11 53.32 30.85 60.60 300.45 307.73 285.80 22.47 Barsoi 174.34 52.31 29.10 61.87 309.18 318.74 294.45 23.22 Dandkhora 126.88 162.39 215.94 162.09 255.09 254.78 240.86 221.16 Falka 168.43 156.60 195.44 155.54 298.40 297.34 280.35 215.36 Hasangunj 127.87 153.26 211.81 156.55 279.03 282.32 265.83 215.80 Kadwa 137.35 187.34 239.71 189.70 298.50 300.86 282.58 243.05 Katihar 151.28 160.73 215.28 171.42 277.13 287.82 266.73 217.63 Korha 174.55 187.92 237.23 187.63 375.12 374.83 357.24 243.98 Kursela 155.57 166.54 209.38 166.90 314.96 315.32 297.45 216.86 Manihari 159.02 174.04 215.88 178.32 341.94 346.22 326.68 222.27 Mansahi 159.15 162.35 202.31 166.81 282.06 286.52 267.52 208.56 Pranpur 128.01 168.81 206.59 171.84 277.87 280.89 264.04 215.15 Sameli 160.74 175.79 210.86 176.36 337.20 337.77 319.87 220.46 Source: Authors’ own calculations based on Sentinel 2A data (2016-2017) Korha 19.38 79.43 5.95 150.04 17.57 31.15 106.42 9.52 6.54 187.5526 323.6201 24.1244 73.434 23.82829 6.24315 123.042 Kursela 21.65 76.67 6.03 132.39 17.14 33.41 106.36 9.65 6.62 165.4875 264.7801 23.8302 66.669 24.1963 6.32493 105.39 Manihari 21.68 73.90 6.51 138.27 19.60 33.44 106.30 10.41 7.10 172.8426 294.2001 22.0650 63.903 26.34592 6.80262 111.274 Mansahi 20.93 71.14 6.35 140.04 20.44 32.70 106.24 10.16 6.94 175.0491 235.9485 21.1824 61.138 25.64016 6.64579 113.0392 Pranpur 22.03 68.37 5.90 112.38 20.90 33.80 106.18 9.44 6.49 140.4805 235.2424 20.5940 58.372 23.62025 6.76660 85.38443 Sameli 24.05 68.73 6.04 140.04 21.48 35.82 114.08 9.66 6.63 175.0491 289.4929 23.6537 58.725 24.22444 6.33118 113.0392 Total 383.59 1056.37 104.44 2144.81 338.75 584.71 1596.22 167.11 114.50 2681.02 4087.28 358.2662 880.3655 419.7156 110.0416 1712.815 Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 73 Table 5: Block-wise cultivated land utilization index of identified crop sequence in Katihar district Crop Sequence Rice-rice- vegetable Rice-maize- rice Rice-maize- maize Rice-maize- jute Rice-pulse- rice Rice-pulse- jute Rice-pulse- potato Maize-maize- rice Azamnagar 0.90 0.90 0.90 0.90 0.82 0.89 0.92 0.74 Amdabad 0.90 0.90 0.90 0.90 0.82 0.89 0.92 0.74 Barari 0.89 0.89 0.81 0.81 0.82 0.89 0.92 0.74 Balrampur 0.92 0.92 0.82 0.82 0.92 0.92 0.82 0.82 Barsoi 0.88 0.88 0.74 0.74 0.88 0.88 0.74 0.82 Dandkhora 0.74 0.74 0.82 0.82 0.74 0.74 0.82 0.81 Falka 0.74 0.74 0.81 0.81 0.74 0.74 0.81 0.81 Hasangunj 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.82 Kadwa 0.74 0.74 0.82 0.82 0.74 0.74 0.82 0.74 Katihar 0.81 0.81 0.82 0.82 0.81 0.81 0.82 0.82 Korha 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.81 Kursela 0.74 0.74 0.89 0.89 0.74 0.74 0.89 0.74 Manihari 0.90 0.90 0.92 0.92 0.90 0.90 0.92 0.82 Mansahi 0.82 0.82 0.89 0.89 0.82 0.82 0.89 0.82 Pranpur 0.82 0.82 0.63 0.63 0.82 0.82 0.63 0.74 Sameli 0.82 0.82 0.67 0.67 0.82 0.82 0.89 0.74 Average 0.82 0.82 0.81 0.81 0.80 0.82 0.83 0.78 Source: Authors’ own calculations based on Sentinel 2A data (2016-2017) Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 74 Relationship of yield and CLUI were analyzed through the graphical representation. The analysis helped to understand the production and land utilization (Table 6). Relationship of crop sequence yield with NPK, bulk density and irrigation was also analyzed to suggest suitable crop sequence. Table 6: Correlation between crop sequence yield and supporting land resources Crop Sequence Coefficient of correlation (R2) Average Production (Q/ha) NPK Bulk Density Irrigation Rice-rice-vegetable 0.167 0.0159 0.0692 155.64 Rice-maize-rice 0.238 0.00003 0.0734 155.85 Rice-maize-maize 0.297 0.00007 0.092 188.48 Rice-maize-jute 0.0199 0.0003 0.0535 259.69 Rice-pulse-rice 0.2311 0.0469 0.2716 304.04 Rice-pulse-jute 0.0113 0.0431 0.2062 307.88 Rice-pulse-potato 0.0231 0.0521 0.2624 288.53 Maize-maize-rice 0.255 0.00002 0.0943 194.80 Source: Authors’ own calculations based on field survey (2016-2017) Rice- rice-vegetable sequence will be suitable in Korha and Falka blocks due to low CLUI and high yield (Figure 5). Rice-maize-rice was found suitable in Kadwa and Korha blocks (Figure 6). However, Balrampur and Barsoi have very low potential for this crop sequence. Rice-maize- maize, rice-maize-jute and maize-maize-rice were not suitable crop sequences except Korha block which has some potential in these sequences (Figure 7, 8 & 9). Rice-maize-jute will be more beneficial in Pranpur and Sameli blocks (Figure 8). Korha block has more advantage for rice- pulses-rice, rice-pulses-jute and rice-pulses-potato sequences (Figure 10, 11 & 12). In terms of productivity and CLUI maize-maize-rice sequence will be beneficial for Azamnagar, Barari, Kadwa, Kursela, Pranpur and Sameli blocks (Figure 9). Figure 5: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-rice- vegetable crop sequence Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 75 Figure 6: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-maize- rice crop sequence Figure 7: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-maize- maize crop sequence Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 76 Figure 8: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-maize- jute crop sequence Figure 9: Relationship of yield with CLUI, NPK, bulk density and irrigation for maize- maize-rice crop sequence Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 77 Figure 10: Relationship of yield with CLUI, NPK, bulk density and irrigation for maize- pulse-rice crop sequence Figure 11: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-pulse- jute crop sequence Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 78 Figure 12: Relationship of yield with CLUI, NPK, bulk density and irrigation for rice-pulse- potato crop sequence Korha has high potential for rice-rice-vegetable and rice-maize-rice. Rice-rice-vegetable is highly potential in Falka block. Kadwa block has high potential in rice-maize-rice sequence while Balrampur and Barsoi blocks have low potential in rice-maize-rice sequence. In rice-maize-maize crop sequence. Korha block has registered moderate potential in rice-maize-maize, rice-maize-jute, rice-pulse-rice, rice-pulse-rice, rice-pulse-jute and rice-pulse-potato sequences. Pranpur and Sameli blocks have high potential in rice-maize-maize. Azamnagar, Amdabad, Barari, Balrampur, Barsoi, Dandkhora, Falka, Hasangunj, Kadwa, Katihar, Kursela, Manihari, Mansahi and Pranpur blocks have registered potential in all sequence (Table 7). The scatter plots of NPK, bulk density and irrigation shows the relationship between yield and land resource potential. The higher R2 value shows the increasing yield with increase in land resource potential. However, lower R2 value represents increasing yield with decreasing land resource potential. Therefore, in this study, the lower R2 value is most desirable as it represents the high yield production in marginally available land resources like NPK, bulk density and irrigation. Crop sequence with maximum average yield found in the rice - pulses - jute followed by rice - pulses - rice, rice - pulse - potato and rice- maize - jute. However, rice -maize- jute (R2 =0.02) is the most suitable sequence with low land resource potential followed by rice-pulse-jute (R2 =0.09), rice-pulse-potato (R2 =0.11) and rice-pulse-rice (R2 =0.18). Based on the analysis of the study, the ranking of crop sequences for maximum production in Katihar district are rice-pulse- jute followed by rice -maize- jute, rice-pulse-rice and rice- pulse-potato. Jute is most suitable crop in flood inundated areas due to its resistance in flood. Rice is the prime food crop of the region and suitable for low laying areas. These crop sequence-based farming system will be more effective in the district due to their potential of maximum return with limited land resources. The production through these crop sequence may be increased from present condition with the use of farm machinery and fertilizers. 4. CONCLUSION The study sought to determine a suitable crop sequence for sustainable agricultural development in flood-prone districts of Bihar in India. Sentinel 2A satellite data (2016-2017) was used to identify major land uses and cropping patterns for different agricultural seasons. A cultivated land Asian Journal of Agriculture and Rural Development, 9(1)2019: 62-81 79 utilization index was calculated to identify the period that land was under various crop sequences. A relationship was established between yield of crop sequences, NPK, bulk density, and irrigation. Rice-rice-vegetables, rice-maize-rice, rice-maize-maize, rice-maize-jute, rice-pulse-rice, rice- pulse-jute, rice-pulse-potato, and maize-maize-rice were found to be the suitable crop sequences for this area under study. This study revealed rice-pulse-rice as the most suitable crop sequence, followed by rice-pulse-potato, and rice-maize-jute in terms of average maximum yield. With low resource potential, rice-maize-jute was found to be the most suitable sequence, followed by rice- pulse-jute, rice-pulse-potato, and rice-pulse-rice. Balrampur and Barsoi blocks registered low potential in the rice-maize-rice sequence. In these blocks, effective farming techniques will play in important role in increasing the agricultural yield. In rice-maize-maize, rice-maize-jute, rice-pulse- rice, rice-pulse-rice, rice-pulse-jute, and rice-pulse-potato sequences, the Korha block registered moderate potential. However, the Azamnagar, Amdabad, Barari, Balrampur, Barsoi, Dandkhora, Falka, Hasangunj, Kadwa, Katihar, Kursela, Manihari, Mansahi and Pranpur blocks registered potential in all sequence. In these blocks, farm machinery is required to maximize the yield and achieve sustainability in agricultural productivity of the area under study. Efficiency in farm inputs, fertilizers, scrupulous irrigation, and maintenance of fallow cycles will help increase the overall productivity of the suitable crop sequences in the area under study. Funding: This study received no specific financial support. Competing Interests: The authors declared that they have no conflict of interests. Contributors/Acknowledgement: The authors wish to thank the anonymous reviewers and editors, for their constructive comments and suggestions for improving the overall quality of the manuscript. 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