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Journal of Environmental Management xxx (2014) 1e11

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Fire regimes and potential bioenergy loss from agricultural lands in the Indo-Gangetic Plains Krishna Vadrevu*, Kristofer Lasko Department of Geographical Sciences, University of Maryland College Park, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 September 2013 Received in revised form 18 December 2013 Accepted 19 December 2013 Available online xxx

Agricultural fires in the Indo-Gangetic Plains (IGP) are a major cause of air pollution. In this study, we evaluate fire regimes and quantify the potential of agricultural residues in generating bioenergy that otherwise are subject to burning by local farmers in the region. For characterizing the fire regimes, we used MODIS satellite datasets in conjunction with IRS-AWiFS classified data. We collected crop statistical data for area, production, and yield for 31 different crops and mapped the bioenergy potential of agricultural residues. We also tested the MODIS net primary production (NPP) dataset potential for crop yield estimation and thereby bioenergy calculations. Results from land use-fire analysis suggested that 88.13% of fires occurred in agricultural areas. Relatively more fires and burnt areas were recorded during the winter rice residue burning season than the summer wheat residue burning season. Monte Carlo analysis suggested that nearly 16.5 Tg of crop residues are burned at 60% probability. MODIS NPP data could explain 62% of variation in field-level crop yield estimates. Our analysis revealed that in the IGP nearly 73.28 Tg of crop residue biomass is available for recycling. The energy equivalent from these residues is estimated to be 1110.77 PJ. From the residues, the biogas potential production is estimated to be 1165.1098 million m3, the electric power potential at 20% efficiency is estimated at 61698.9 kWh, and the total bioethanol production potential at 21.0 billion liters. Results also highlight geographic locations of bioenergy resources in the IGP useful for energy planning. Controlling agricultural residue burning and promoting the bioenergy sector is an attractive "winewin" strategy in the IGP. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Agricultural fires Crop residues MODIS Bioenergy Sustainability

1. Introduction Agriculture is the dominant sector of India’s economy accounting for 16% of gross domestic potential and nearly 65% of the population depends directly on this sector (Gupta, 1998; Lerche, 2011; Kalirajan and Singh, 2013). India has fifteen different agroclimatic zones with a variety of both tropical and temperate crops grown in highly diverse regions. Of the different geographic regions in India, the Indo Gangetic Plains (IGP) is one of the largest and richest fertile areas encompassing a large part of northern and eastern India, parts of Pakistan, southern Nepal, and most of Bangladesh (Erenstein et al., 2007; Chauhan et al., 2012). The region is named after the Indus and Ganges, the twin river systems that drain the area. The IGP support nearly 1 billion people. In India, the IGP occupy nearly 20% of the total geographical area. The states of Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal constitute the major part of the region (Kumar et al., 1999). In the IGP, rice and

* Corresponding author. Tel.: þ1 3302340387. E-mail addresses: [email protected], [email protected] (K. Vadrevu).

wheat crops account for over 10 million ha. These two crops together contribute more than 70% of total cereal production in India from an area of around 27 Mha under Wheat and about 40 Mha under Rice (Vadrevu et al., 2011). Although, the rice-wheat cropping system in the IGP is considered highly productive and it serves as a food basket for millions of people in that region, there are other concerns about this system such as air pollution (Sidhu et al., 1998; Gupta et al., 2001; Saud et al., 2011). After the harvest, a huge amount of agricultural residues are burned in this region that otherwise could be used for bioenergy generation. The open burning of agricultural residues results in emissions of trace gases like CO2, CO, CH4, N2O, NOx, NMHCs and aerosols (Prasad et al., 2000; Kant et al., 2000; Gupta et al., 2001; Badarinath et al., 2006, 2007, 2009; Sahai et al., 2010; Sarkar, 2007) which pose a health hazard to local inhabitants (Estrellan and Lino, 2010). Diverting some of the residue into bioenergy production would allow the energy from the residue to be harvested instead of burned on the field, likely helping to mitigate greenhouse gas emissions during the agricultural residue burning season (Sudha and Ravindranath, 1999; Saxena et al., 2009; Vadrevu et al., 2006, 2011, 2012).

0301-4797/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.12.026

Please cite this article in press as: Vadrevu, K., Lasko, K., Fire regimes and potential bioenergy loss from agricultural lands in the Indo-Gangetic Plains, Journal of Environmental Management (2014), http://dx.doi.org/10.1016/j.jenvman.2013.12.026

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K. Vadrevu, K. Lasko / Journal of Environmental Management xxx (2014) 1e11

Fig. 1. Study area location map.

In this study, we characterize the agricultural fire regimes covering the MODIS satellite era (2003 till recent) in the IGP. Fire regimes are determined by frequency, type, extent, and seasonality; the concept is useful for a generalized description on the nature of fires (Agee, 1998; Vadrevu et al., 2006, 2011). Both the active fires as well as burnt area statistics have been analyzed. Very few studies attempted to characterize bioenergy potential of crop residues at a district level for a large region such as the IGP. Additionally, there were no spatially explicit estimates on the total available crop residue production, energy-equivalent of those crop-residues and bioenergy production potentials for biogas, electricity, and bioethanol in the IGP. Further, the use of satellite data for estimating bioenergy potential is gaining significance. For example, Elmore et al. (2008) used MODIS satellite-derived net primary production (NPP) in conjunction with Landsat data to map rice residues and potential biofuel production from China. Similarly, Campbell et al. (2008) based on historical land use data, satellite-derived land cover data, and global ecosystem modeling showed the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand. Recently, Smith et al. (2012) used satellite-derived net primary productivity (NPP) data measured for every 1 km2 of the 7.2 million km2 of vegetated land in the conterminous USA to estimate primary bioenergy potential. In contrast to these studies, literature review suggests that in the IGP, satellite-based methodologies for estimating bioenergy potential were not yet explored. In this study, we contribute to the understanding of total available crop residues in the IGP, and bioenergy production potential by addressing the following questions: 1). What are the typical fire regimes in the IGP which is dominated by an agricultural landscape?; 2). How much of the agricultural residues are burned in the IGP?; 3). What is the potential of agricultural residues in generating bioenergy that otherwise would be subject to burning by local farmers?; 4). Which crop residues have the most bioenergy potential in the IGP and which sub-regions (districts) of the IGP have the most potential? (5) What is the total contribution of bioenergy from the IGP compared to India? (6) How does the bioenergy potential differ for electricity, bio coal,

bioethanol and biogas in the IGP and what are their spatial patterns? In addition to these questions, we also address the potential of satellite-derived NPP which is useful for bioenergy characterization from agricultural crop lands in the IGP. 2. Study area The study area map is shown in Fig. (1). The area covers 0.59 million sq.km with over 190 districts located mostly in the major states of Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal. The area can be divided into four major sub-regions following agro-ecological, socio-economic and political factors and their interactions (Narang and Virmani, 2001; Erenstein et al., 2007) e Transgangetic plains covering Punjab and Haryana in the north-western plains, Upper Gangetic Plains covering western and central Uttar Pradesh, Middle-Gangetic plains covering eastern Uttar Pradesh and Bihar, and Lower Gangetic plains with West Bengal (Fig. 1). For the study area, we spatially quantified the agricultural fire regimes and the bioenergy potential of agricultural residues. 3. Datasets 3.1. Fire hotspots (2003e2012) We used the MODIS (MODerate resolution Imaging Spectroradiometer) Active Fire Product (2003e2012) processed through MODAPS (MODIS Adaptive Processing System) using the enhanced contextual fire detection algorithm (Giglio, 2013) into the Collection 5 Active fire product. The MODIS sensor is on board NASA’s Earth Observing System (EOS) satellites, Terra (EOS AM-1) and Aqua (EOS PM-1). The sun-synchronous, polar-orbiting satellites pass over the equator at approximately 10:30am/pm (Terra) and 1:30pm/am (Aqua) with a revisit time of 1e2 days. The fire data are at a 1 km spatial resolution. However, flaming fires one tenth of that size can be detected under ideal observing conditions such as near nadir, little or no smoke, and a relatively homogenous land surface.



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The product is relatively more refined than the earlier product as it captures smaller agricultural fires and uses MODIS daily surface reflectance data for characterizing fires and associated burnt areas (Giglio et al., 2013). 3.3. Land cover data

Fig. 2. IRS-AWiFS (56 m) land cover map for the IGP.

Further, the smallest size of a flaming fire that can be detected is 50 m2 under the most pristine conditions (Giglio, 2013). 3.2. Burnt area in the IGP (2003e2012) We used the 500 m Collection 5.1 MODIS direct broadcast (DB) burnt area product (MCD64A1) that is based on the MODIS direct broadcast burnt area mapping algorithm (Giglio et al., 2009).

For characterizing the fire events (2003e2012) across the landscape, we used IRS-AWiFS classified land cover data. The land cover product obtained from Indian Space Research Organization (ISRO), National Remote Sensing Center (NRSC) is derived by classification of a multi-temporal AWiFS data at 56 m spatial resolution using decision tree classifiers (Fig. 2). Generation of land cover maps is an ongoing process for ISRO and already eight cycles have been completed (2004-05-2011-12). The land cover data is available from the NRSC Bhuvan website for visualization and through the open-geospatial consortium (OGC) service. We chose this product as it was developed by local Indian scientists. 3.4. Burnt crop residues and bioenergy estimation District-wise crop area (ha), production (tonnes), and yield (tonnes/ha) for 31 different crops from 2008 to 09 and 2010e2011 have been obtained from the India cropstat database and Department of Agriculture and Cooperation, Ministry of agriculture, Government of India (www.agricoop.nic.in). The 31 crops included pigeon pea, barley, black gram, finger millet, gram, green

Fig. 3. aed. 3a). Spatial variation of fire characteristics from MODIS hotspots. 3a). MODIS hotspots during the peak November season (2009) in the IGP; 3b). Fire seasonality and trends (2003e2012); 3c). Annual fire trends (2003e2012); 3d). Monthly fire trends (2003e2012).


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gram, groundnut, horse gram, jute, linseed, maize, niger seed, other Kharif crops, other Rabi pulses, pearl millet, peas, potato, rapeseed, red lentils, rice, safflower, small millets, sorghum, soybeans, sugarcane, sunflower, sun hemp, sweet potato, tapioca, tobacco and wheat. The quantity of residue that is produced and can potentially be removed is directly related to the production yields of crops in the rotation. As farmers rarely measure the residue amounts on the field, we used the crop yield related conversion factors to account for residues (FAO, 1995). Specifically, for rice, wheat, and barley, we used a grain to straw ratio of 1:1.5, for sorghum, maize and pearl millet we used grain to Stover ratios of 1:2; 1:1.5; 1:2 respectively, and for other crops we used economic produce to residue ratios for pigeon pea (1:2.5), other pulses (1:1), groundnut (pod) (1:1.5), other oil seeds (1:2), sugarcane (1:0.1) and potato (1:0.5). The other pulses include chickpea, green gram, black gram, field pea, horse gram, etc. Other oil seeds include rape seed, mustard, sesame, sunflower, safflower, niger seed, linseed, castor seed, etc. Using these ratios, the amount of crop residue production for individual crops has been computed by dividing its yield by the corresponding ratios. The results have been summed for the entire IGP to arrive at total residue production. Of the total residues produced, we assumed a range of 10e35% as burned (Vadrevu et al., 2012). As these were best guess estimates and the values can be low or high, we followed a statistically robust procedure of Monte Carlo (MC) Analysis to arrive at a range of burnt biomass values (Tg) for the IGP. Latin hypercube sampling was used for the MC simulations with prior triangular distributions for input burnt crop residue

a

c

amounts. We used 10,000 random simulations for MC analysis (Pebesma and Heuvelink, 1999). We also performed a sensitivity analysis to infer individual crop contribution to the total biomass burned in the IGP. The analysis has been performed by splitting the simulation data from input distributions into ten groups in terms of their cumulative probability: 0%e10%, 10%e20%, 20%e 30%, and 90%e100%. Then simulation data are filtered for each of these groups to find the corresponding output values that occurred when the input variable being analyzed lies within each percentile band listed above. The conditional mean is then calculated for the filtered data (Vose, 2008). Repeating this analysis across each probability range for each selected input crop produced the spider plot. Of the different crops, rice and wheat dominated in the IGP. For quantifying the energy equivalent of various crop residues, respective available residues were multiplied with the corresponding equivalent energy figures obtained from the literature (Jenkins et al., 1998; Sarkar, 2007; Saxena et al., 2009). The energy equivalent values for different crops used were as follows: rice straw (15.0 MJ/kg), wheat straw (14.5 MJ/kg), barley straw (19.2 Mj/kg), sorghum Stover (18.0 MJ/kg), maize Stover (18.2 MJ/ kg), pearl millet Stover (18.0 MJ/kg), pigeon pea residue (16.2 MJ/ kg), other pulses (16.2 MJ/kg), groundnut shell residue (18.7 MJ/ kg), other oil seed residue (16.5 MJ/kg), sugarcane leaves (16.0 MJ/ kg), potato residue (10.0 MJ/kg), etc. Further, the equivalent energy (Mega joule) of crop residues is based on the dry ash free basis. We assumed that two-thirds of the residue is consumed as animal feed and used for other purposes while the remaining one-

b

d

Fig. 4. aed. MODIS burnt areas (ha) for IGP. 4a). MODIS burnt areas (ha) (2009) for November; 4b). Fire seasonality and trends (2003e2012); 4c). Annual burnt area trends (2003e 2012); 4d). Monthly trends (2003e2012).



third is available for recycling as bioenergy (Tandon, 1995; Sarkar, 2007). The biogas potential of crop residues is computed by multiplying the available amount of crop residues expressed in Kgs by the rate of biogas production (0.159 m/Kg of crop residues, (Sarkar, 2007). The electricity power potential of residues when converted to briquette to make bio-coal under direct combustion is calculated by multiplying the corresponding equivalent energy of crop residues multiplied by 20% and the value is converted to billion kWh (kilo Watt hour) (APCTT, 2004; Sarkar, 2007). Thus, for direct combustion to produce electricity, the energy conversion efficiency has been assumed as 20% of crop residue energy. Similarly, the electric power potential of bio-coal

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when co-fired with coal is calculated by multiplying the crop residue amount (in Kg) by the corresponding equivalent energy of crop residues multiplied by 40% and the value is converted to kWh. For quantifying the ethanol production potential from wheat, maize, and rice, the residue amounts were first converted to dry biomass followed by ethanol (in liters per amount of dry biomass) for individual crops. The dry matter values were obtained through taking out moisture content from residues which were 10.9% for wheat, 13.8% for maize and 11.4% for rice. Using the dry matter values, the ethanol yields were obtained using the conversion factors of 0.40, 0.46 and 0.48 L per Kg of dry biomass respectively (Kim and Dale, 2004).

Fig. 5. a.b. 5a). Total crop residues burnt estimate (Tg) obtained through Monte Carlo simulations suggesting crop residue burnt estimates 10.0e21 Tg 5b). Sensitivity analysis of individual crops contribution to the mean amount (Tg) of residues burnt. Of the different crops, Rice (purple) followed by wheat (black) dominate in the IGP. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)


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3.5. MODIS net primary production (NPP) and crop yield for bioenergy Since crop yield estimates are the basis for crop residue and bioenergy calculations, we evaluated the potential of the MODIS NPP dataset for yield estimates. We first converted the NPP to crop yield and then compared the values with the India cropstat-derived yield estimates (www.agricoop.nic.in) for the IGP. We used the MODIS NPP product (MOD17) for this exercise. The main data inputs to the MOD17 algorithm include Fraction of Photosynthetic Active Radiation (FPAR) and Leaf Area Index (LAI) from MODIS. In addition, temperature, incoming solar radiation, vapor pressure deficit, land cover classification, biome specific lookup table for

energy efficiency for different vegetation types and biome-specific parameters for respiration are used to arrive at the MODIS NPP product (Running et al., 2004; Zhao and Running, 2010). MODISderived NPP has been converted into crop yield using the following equation (Qin et al., 2012):

EYi ¼ NPPi HIi =Di C ðRSi þ 1Þ where EY is the economic yield, ‘i’ is the specific food crop (wheat, rice, soybeans, groundnut, pulses, etc.), NPP, is the net primary production (gC/m2/month), HI is the harvest index, which measures the proportion of total aboveground biological yield allocated to the economic yield of the crop, D is the dry proportion of the EY;

Fig. 6. a,b. 6a). Average NPP (gC/m^2/month) derived from MODIS NPP product. 6b). Relationship between MODIS NPP and crop yield. MODIS NPP could explain 62% of variation in field derived crop yield data.



c is the carbon content in the dry matter and RS is the root shoot ratio. The parameters for the above equation have been obtained from Prince et al. (2001); Hicke and Lobell (2004); Hicke et al. (2004). For example, the harvest index for different crops were as follows: wheat (0.39), rice (0.40), corn (0.53), peanuts (0.40), potatoes (0.50), etc. and the corresponding the root shoot ratio for these crops were 0.20, 0.46, 0.18, 0.07, 0.07. 4. Results 4.1. Agricultural land use, active fires and burnt areas IRS-AWiFS classified land use/cover map for the IGP is shown in (Fig. 2). The spatial patterns in land cover clearly suggest that agriculture is the dominant land use. The total area of the IGP is 59 million ha, of which the cropped area for the 2008e2009 crop season (www.agricoop.nic.in) is 51 million ha; thus crops account for nearly 87% of the IGP area. The majority of the cropped area consists of wheat and rice. Spatial patterns of MODIS-derived active fires for the month of November, 2008 are shown in (Fig. 3a) and the yearly fire counts from 2003 to 2012 are shown in (Fig. 3b,c). Of the different years, 2012 recorded the highest number of fire counts (25,896) followed by 2005 (23,914), and 2008 (23,666) with a mean fire count of 21,361 per year (Fig. 3c). Relatively more fire counts were recorded during the winter rice residue burning season than the summer wheat residue burning season (Fig. 3d). Analysis from the IRS-AWiFS land cover data suggested that 88.13% of fires occurred in agricultural areas. For this analysis, we included only agricultural categories such as kharif (rainy/monsoon season), rabi (summer season), zaid (the crops grown in the short duration between rabi and kharif crop season, mainly from March to June), double/triple crop, current fallow categories and eliminated the land cover categories of build-up areas, plantations/orchards, forest categories, scrub, littoral swamp, water bodies, etc. from the land cover map (Fig. 2). Sample burnt areas for the IGP for the month of November, 2009 are shown in (Fig.4a) the yearly trends in (Fig. 4b,c) and the seasonal trends in (Fig. 4d) Analysis from the MODIS burnt area product (2003e2012) suggested an average of 809714 ha as burnt every year in the IGP. Of the several years, 2009 recorded the highest amount of burnt area (1382609 ha) followed by 2011 (1025606 ha), 2010 (949043 ha), etc. (Fig. 4b, c). Further, temporal trends suggested that most of the burning occurs during the winter season (November and October) and then in summer (May, April, and March) (Fig. 4b, b). Burnt areas averaged across months for different years suggest that the winter season had almost 30% more burning than the summer. Further, compared to active fires that were the highest during October, burnt areas were the highest during November suggesting a fire progression. Badarinath et al. (2006) using the IRS-P6 AWiFS satellite data for the Punjab state of the Indo-Ganges region estimated that nearly 5504.0 sq.km (or 550400 ha) is burnt under the wheat crop residue burning and 12,685 sq.km (or 1268500 ha) under the rice crop residue burning for year 2005. Compared to these estimates, MODIS-derived burnt areas (average of 809714 ha/yr) are an underestimate, as Punjab state is only a part of the IGP. However, both of the studies confirm that burning is relatively higher during winter than summer. Results from the MC simulation suggested that 10.0e21 Tg of crop residues are burnt in the IGP (Fig. 5a). Sensitivity analysis also suggested that rice and wheat contribute to most of the residues burned in the region compared to the other crops (Fig. 5b). MODIS-derived NPP for the IGP varied from 36.8 to 111.1 gC/m2/ month (Fig. 6a). Results from converting the MODIS NPP to yield estimates and comparison with ground-based crop yield data

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Table 1 Available crop residues (Tg) and their potential in sub-regions of the Indo-Ganges region. Category

Lower GP Middle GP Trans GP Upper GP Total

Available crop residue (tg) Energy equivalent of crop residue (PJ) Biocoal potential (20% efficiency kWh) Biocoal potential (40% efficiency kWh) Maize ethanol potential (Million Liters) Rice ethanol potential (Million Liters) Wheat ethanol potential (Million Liters) Biogas potential (Million m3)

13.04

14.91

24.96

20.37

73.28

205.80

225.28

377.42

302.28

1110.77

11430

12515

20963

16791

61699

22860

25030

41926

33582

123398

65.28

385.60

130.94

176.87

758.69

3025.99

2339.26

2682.97

1418.05

9466.27

137.14

2147.04

4998.77

3493.27

10776.21

207.34

237.04

396.89

323.84

1165.11

suggested a correlation of 62.6% (Pearson correlation; two tailed test of significance was used; p ¼ 0.000) (Fig. 6b). 4.2. Available crop residue biomass for recycling In total, the IGP contains a large amount of crop residue biomass that is available for recycling with a total of 73.28 Tg (Table 1, Fig. 7a). Of the different sub-regions in the IGP, Trans-IGP region had the highest available residues followed by Upper-IGP, middleIGP and lower-IGP. The top three states that had the highest available residues are Uttar Pradesh, West Bengal, and Punjab. Spatial patterns of the amount of agricultural residue available for individual districts are shown in. (Fig. 7a). Of the total 190 districts,

Fig. 7. a,b. 7a). Total available crop residues (Tg) for re-cycling in the IGP; 7b). Crop residue potential from the top-five crops in the IGP.


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the top five districts having the highest available residues include Murshidabad (West Bengal), Medinipur (West Bengal), Firozpur (Punjab), Nadia (West Bengal), and Sangrur (Punjab). The district average of available crop residues is 0.387 Tg. Further, of the twenty-five top districts, both Punjab and West Bengal states had the highest number of districts with the available residues for recycling. Of the 31 different crops, the top five crops contributing to the agricultural residues include wheat, rice, sugarcane, potato, and jute. Percent-wise contribution of available residues from different crops is shown in (Fig. 7b). Results clearly suggest that rice and wheat contribute to nearly 73.0% of total available crop residues for recycling in the region. Further, when compared to the total cropped area of India of 195 Mha, the IGP constitutes 52.42 Mha of which rice constitutes 19.12 Mha and wheat 17.99 Mha (www.agricoop.nic.in). We also compared the total residue estimate with the earlier studies. For example, Sarkar (2007) estimated 182.0 Tg of available crop residues for all of India. Thus, the IGP accounts for approximately 40% of the total crop residue biomass in India. 4.3. Energy equivalent of agricultural residues In total, the energy equivalent of available crop residue biomass for recycling in the IGP is 1110.77 PJ (Fig. 8a). Of the different subregions in the IGP, Trans-IGP region had the highest energy

equivalent of crop residue followed by Upper-IGP, Middle-IGP and the lower-IGP (Table 1). The top three states that had the highest energy equivalent of crop residue are Uttar Pradesh, West Bengal and Punjab. Spatial patterns of the amount of energy equivalent of crop residues (PJ) are provided in Fig. 8a. Of the total 190 districts, the top five districts having the highest energy equivalent were Murshidabad (West Bengal), Nadia (West Bengal), Medinipur (West Bengal), Firozpur (Punjab), and Sangrur (Punjab). The district average energy equivalent from crop residues is 5.846 PJ. Further, of the top twenty-five districts, West Bengal state had the most districts, followed by Punjab and Uttar Pradesh. Of the 31 different crops, the top five crops contributing to the energy equivalent are wheat, rice, jute, sugarcane, and pearl-millet. Rice and wheat together accounted for nearly 71% of the total energy equivalent. 4.4. Biogas potential of agricultural residues Our results on the biogas potential production from crop residues in the IGP suggest 1165.11 million m3. Of the different subregions of the IGP, Trans-IGP had the most biogas potential (Table 1). The top three states that had the highest biogas potential are Uttar Pradesh, West Bengal and Punjab. Spatial patterns of biogas production potential from agricultural residues are shown in (Fig. 8a, b). Of the total 190 districts, the following are the top five districts having highest biogas potential (Murshidabad (West

Fig. 8. aed; 8a). Total energy equivalent of crop residues (PJ); 8b). Total biogas potential from crop residues (million cubic liters); 8c). Electric power potential from crop residues with 20% efficiency; 8d). Electric potential from crop residues with 40% efficiency.



Bengal), Medinipur (West Bengal), Firozpur (Punjab), Nadia (West Bengal), and Sangrur (Punjab)). Further, of the top twenty-five districts of biogas potential West Bengal had most of the districts, followed by Punjab and Uttar Pradesh. The district average of biogas potential is 6.132 million m3. Of the 31 different crops the top five are wheat, rice, jute, sugarcane, and potato. Wheat and rice together account for nearly 73% of the total biogas potential and the top five crops together account for 88.06% of the total. 4.5. Electric power potential In total, the electric power potential at 20% efficiency in the IGP is 61698.9 kWh and at 40% efficiency the total potential in the IGP is 123397.8 kWh (Table 1). Of the different sub-regions of the IGP, Trans-IGP region had the highest potential (Table 1). The top three states that had the highest electric power potential are Uttar Pradesh, West Bengal and Punjab. Spatial patterns of the electric power potential in different districts of IGP are shown in Fig. (8c,d). Of the total 190 districts, the following are the top five districts having the highest potential (Murshidabad (West Bengal), Nadia (West Bengal), Medinipur (West Bengal), Firozpur (Punjab), and Sangrur (Punjab)). The district average of electric power potential at 40% efficiency is 652.90 kWh. Further of the top twenty-five districts West Bengal had the most followed by Punjab and Uttar Pradesh. Of the 31 different crops the top five are wheat, rice, jute, sugarcane, and pearl millet. Further, wheat and rice together account for nearly

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71% of the total electric power potential from bio coal. At both efficiencies, wheat accounts for 39.48% of the total, while the top five crops together account for 87.66% if the total. 4.6. Bioethanol potential of agricultural residues Overall, the total bioethanol potential production from crop residues of maize, rice and wheat residues in the IGP is 21.0 billion liters (Table 1). Of the different sub-regions of the IGP, Trans-IGP had the most bioethanol potential followed by Upper-IGP, Middle-IGP, and the Lower-IGP. The top three states of bioethanol potential are Uttar Pradesh, Punjab, and Haryana. The spatial patterns of bioethanol potential are available in (Fig. 9aec). Of the total 190 districts, the following are the top five districts having the highest bioethanol potential from crop residues (Firozpur (Punjab), Sirsa (Haryana), Sangrur (Punjab), Bathinda (Punjab), and Ludhiana (Punjab). The district average of potential bioethanol is 110.5 million liters. Further, of the top twenty-five districts Punjab has the most, followed by Haryana and Uttar Pradesh. Of the three different crops, wheat accounts for 51.3%, rice-45.0% and maize accounts for 3.61% (Fig. 9aec) of the total bioethanol potential. 5. Discussion Results from our land use-fire analysis clearly suggest that agricultural crop residues in the IGP are burned that could

Fig. 9. aed). Ethanol potential in million liters. 9a. Wheat; 9b. Rice; 9c. Maize; 9d. Combined ethanol potential (million liters).


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otherwise have a huge potential as a bioenergy resource. Bioenergy from agricultural residues has several benefits. For example, it can foster economic development and contribute to the creation of jobs and investment in that sector (Sims, 2003). The benefits for rural areas can vary depending on the scale of bioenergy operations, i.e. whether biomass is processed in centralized or decentralized plants. The implementation of bioenergy from crop residue has advantages over dedicated bioenergy crops. The primary advantage is that the crop yield would still be used as food instead of fuel. The supply of bioenergy does not directly compete with food. Our detailed analysis on the bioenergy potential of crop residues at a district-level provides valuable information on both quantitative aspects as well as spatial information on the best locations where bioenergy plants can be installed. Bioenergy plants can offer opportunities to dispose of crop residues that are otherwise burned in the region. Through such bioenergy plants, farmers can participate in the added value of crop residues useful for biogas, electricity, and bioethanol production in their local areas. Further, to recognize the full potential of agricultural residues, proper storage/processing of residues in addition to appropriate mix of agricultural residues might be explored so that energy is generated almost equally for all the months. In contrast to agricultural residue burning that is practiced in the region and a huge pollution concern (Vadrevu et al., 2011), effective use of existing agricultural wastes for bioenergy generation could provide additional revenue for poor rural communities. However, starting a bioenergy plant can be an expensive endeavor. It may also require building infrastructure in and around farms to collect and transport the crop residues to the bioenergy plant. The solution to this problem is the need for close collaboration between public and private enterprises. Also, local governments may have to provide initial incentives to help launch such an industry. Encouraging bioenergy as an alternative to fossil fuels may also require enacting a regulatory framework that allows for the development of agro waste-based bioenergy. Crop residue burning is already banned in several regions of the IGP and the next step should be providing mechanisms and facilitating effective recycling of agricultural residues through bioenergy plants. Such an endeavor will not only benefit farmers for raising their economic standard but also will help mitigate air pollution in the IGP. Acknowledgments The authors would like to thank Dr. Louis Giglio (UMd) for the MODIS active fire dataset and burnt areas products used in the study. We gratefully acknowledge the encouragement and support from Prof. Chris Justice, Chair, Dept. of Geographical Sciences, University of Maryland, College Park, USA. This research was supported by NASA grant NNX10AU77G. References APCTT, 2004. Briquette Plant. Asian and Pacific Center for Transfer of Technology, pp. 1e2. Sample Database, Technology offer, Reference ID: APC-7007-TO. www. apctt.org. Agee, J.K., 1998. The landscape ecology of western forest fire regimes. Northwest Sci. 72 (17), 24e34. Badarinath, K.V.S., Chand, T.K., Prasad, V.K., 2006. Agriculture crop residue burning in the Indo-Gangetic Plainsea study using IRS-P6 AWiFS satellite data. Curr. Sci. 91 (8), 1085. Badarinath, K.V.S., Kharol, S.K., Latha, K.M., Chand, T.R., Prasad, V.K., Jyothsna, A.N., Samatha, K., 2007. Multiyear ground-based and satellite observations of aerosol properties over a tropical urban area in India. Atmos. Sci. Lett. 8 (1), 7e13. Badarinath, K.V.S., Kharol, S.K., Sharma, A.R., Krishna Prasad, V., 2009. Analysis of aerosol and carbon monoxide characteristics over Arabian Sea during crop residue burning period in the Indo-Gangetic Plains using multi-satellite remote sensing datasets. J. Atmos. Solar-Terrest. Phys. 71 (12), 1267e1276. Campbell, J.E., Lobell, D.B., Genova, R.C., Field, C.B., 2008. The global potential of bioenergy on abandoned agriculture lands. Environ. Sci. Technol. 42 (15), 5791e 5794.

Chauhan, B.S., Mahajan, G., Sardana, V., Timsina, J., Jat, M.L., 2012. Productivity and sustainability of the rice-wheat cropping system in the Indo-Gangetic Plains of the Indian subcontinent: problems, opportunities, and strategies. Adv. Agron. 117, 315e369. Elmore, A.J., Shi, X., Gorence, N.J., Li, X., Jin, H., Wang, F., Zhang, X., 2008. Spatial distribution of agricultural residue from rice for potential biofuel production in China. Biomass Bioenergy 32 (1), 22e27. Erenstein, O., Hellin, J., Chandna, P., 2007. Livelihoods, Poverty and Targeting in the Indo-Gangetic Plains: a Spatial Mapping Approach, first ed. (New Delhi, India). Estrellan, C.R., Iino, F., 2010. Toxic emissions from open burning. Chemosphere 80 (3), 193e207. FAO, 1995. Grain:straw/stover or Economic Produce: Residue Ratio, pp. 1e4. FAO document. No. 15&24. Gupta, A., 1998. Postcolonial Developments: Agriculture in the Making of Modern India, first ed. Duke University Press, Durham. Gupta, P.K., Prasad, K.V., Sharma, C., Sarkar, A.K., Kant, Y., Badarinath, K.V.S., Mitra, A.P., 2001. CH4 emissions from biomass burning of shifting cultivation areas of tropical deciduous forestseexperimental results from ground-based measurements. Chemosphere-Global Change Sci. 3 (2), 133e143. Giglio, L., Loboda, T., Roy, D.P., Quayle, B., Justice, C.O., 2009. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 113 (2), 408e420. Giglio, L., 2013. MODIS Collection 5 Active Fire Product User’s Guide V.2.5. http:// modis-fire.umd.edu/Documents/MODIS_Fire_Users_Guide_2.5.pdf. Giglio, L., Randerson, J.T., Werf, G.R., 2013. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosci. 118 (1), 317e328. Hicke, J.A., Lobell, D.B., 2004. Spatiotemporal patterns of cropland area and net primary production in the central United States estimated from USDA agricultural information. Geophys. Res. Lett. 31, L20502. Hicke, J.A., Lobell, D.B., Asner, G.P., 2004. Cropland area and net primary production computer from 30 years of USDA agricultural harvest data. Earth Interact. 8 (10). Jenkins, B.M., Baxter, L.L., Miles Jr., T.R., Miles, T.R., 1998. Combustion properties of biomass. Fuel Process. Technol. 54, 17e46. Kalirajan, K., Singh, K., 2013. Understanding sectoral economic growth in India: the potential for services. Inst. Econ. 5 (1), 91e112. Kant, Y., Ghosh, A.B., Sharma, M.C., Gupta, P.K., Prasad, V.K., Badarinath, K.V.S., Mitra, A.P., 2000. Studies on aerosol optical depth in biomass burning areas using satellite and ground-based observations. Infrared Phys. Technol. 41 (1), 21e28. Kim, S., Dale, B.E., 2004. Global potential bioethanol production from wasted crops and crop residues. Biomass Bioenergy 26, 361e375. Kumar, P., Joshi, P.K., Johansen, C., Asokan, A., 1999. Rice-Wheat Consortium Paper Series. In: Sustainability of Rice-wheat Based Cropping Systems in India: Socioeconomic and Policy Issues, vol. 5. RWC, New Delhi, India, p. 61e77. Lerche, J., 2011. Agrarian crisis and agrarian questions in India. J. Agrar. Change 11 (1), 104e118. Narang, R.S., Virmani, S.M., 2001. Riceewheat Cropping Systems of the IndoGangetic Plain of India. In: RiceeWheat Consortium Paper Series 11. RWC, New Delhi. Pebesma, E.J., Heuvelink, G.B., 1999. Latin hypercube sampling of Gaussian random fields. Technometrics 41 (4), 303e312. Prasad, V.K., Gupta, P.K., Sharma, C., Sarkar, A.K., Kant, Y., Badarinath, K.V.S., Mitra, A.P., 2000. NOx emissions from biomass burning of shifting cultivation areas from tropical deciduous forests of Indiaeestimates from ground-based measurements. Atmos. Environ. 34 (20), 3271e3280. Prince, S.D., Haskett, J., Steininger, M., Strand, H., Wright, R., 2001. Net primary production of U.S. Midwest croplands from agricultural harvest yield data. Ecol. Appl. 11 (4), 1194e1205. Qin, Z., Zhuang, Q., Chen, M., 2012. Impacts of land use change due to biofuel crops on carbon balance, bioenergy production, and agricultural yield, in the conterminous United States. GCB Bioenergy 4 (3), 277e288. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54 (6), 547e560. Smith, W.K., Cleveland, C.C., Reed, S.C., Miller, N.L., Running, S.W., 2012. Bioenergy potential of the United States constrained by satellite observations of existing productivity. Environ. Sci. Technol. 46 (6), 3536e3544. Sahai, S., Sharma, C., Singh, S.K., Gupta, P.K., 2010. Ambient CO levels during winter and summer agricultural burning seasons of Northern India. Asian J. Water Environ. Pollut. 7 (4), 99e104. Sarkar, A., 2007. Potential of crop residue in India as a source of energy. Int. J. Global Energy Iss. 28, 181e194. Saud, T., Singh, D.P., Mandal, T.K., Gadi, R., Pathak, H., Saxena, M., Sharma, S.K., Mukherjee, A., Bhatnagar, R.P., 2011. Spatial distribution of biomass consumption as energy in rural areas of the IGP. Biomass Bioenergy 35 (2), 932e941. Saxena, R.C., Adhikari, D.K., Goyal, H.B., 2009. Biomass-based energy fuel through biochemical routes: a review. Renew. Sustain. Energy Rev. 13 (1), 167e178. Sidhu, B.S., Rupela, O.P., Beri, V., Joshi, P.K., 1998. Sustainability implications of burning rice- and wheat-straw in Punjab. Econ. Polit. Wkly. 33 (39), 163e168. Sims, R., 2003. Climate change solutions from biomass, bioenergy and biomaterials. Agric. Eng. Int. CIGR J. Sci. Res. Dev. 8, 1e29. Sudha, P., Ravindranath, N.H., 1999. Land availability and biomass production potential in India. Biomass Bioenergy 16 (3), 207e221. Tandon, H.L.S. (Ed.), 1995. Fertilizer, Organic Manures, Recyclable Waste and Bio-fertilizers. Fertilizer development and consultation organization, New Delhi, pp. 36e51.


K. Vadrevu, K. Lasko / Journal of Environmental Management xxx (2014) 1e11 Vadrevu, K.P., Eaturu, A., Badarinath, K.V.S., 2006. Spatial distribution of forest fires and controlling factors in Andhra Pradesh, India using spot satellite datasets. Environ. Monit. Assess. 123 (1e3), 75e96. Vadrevu, K.P., Ellicott, E., Badarinath, K.V.S., Vermote, E., 2011. MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, North India. Environ. Pollut. 159 (6), 1560e 1569.

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Vadrevu, K.P., Ellicott, E., Giglio, L., Badarinath, K.V.S., Vermote, E., Justice, C., Lau, W.K., 2012. Vegetation fires in the Himalayan regioneAerosol load, black carbon emissions and smoke plume heights. Atmos. Environ. 47, 241e251. Vose, D., 2008. Risk Analysis: A Quantitative Guide. Wiley. com. Zhao, M., Running, S.W., 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329 (5994), 940e943.
