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Yield, water use efficiency and economic analysis of energy sorghum in South Texas
Biomass and Bioenergy 81 (2015) 339e344
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Biomass and Bioenergy journal homepage: http://www.elsevier.com/locate/biombioe
Research paper
Yield, water use efficiency and economic analysis of energy sorghum in South Texas J. Enciso a, *, J. Jifon b, L. Ribera c, S.D. Zapata d, G.K. Ganjegunte e a
BAEN Dept., Texas A&M AgriLife Research, Weslaco, TX, USA Horticulture Department., Texas A&M AgriLife Research, Weslaco, TX, USA c Agricultural Economics Dept., Texas A&M AgriLife Extension, College Station, TX, USA d Agricultural Economics Dept., Texas A&M AgriLife Extension, Weslaco, TX, USA e Soil and Crop Sciences, Texas A&M AgriLife Research, El Paso, TX, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2015 Received in revised form 14 July 2015 Accepted 16 July 2015 Available online 30 July 2015
Yields, water use efficiency and economic returns (net farm revenues) of biomass sorghum [Sorghum bicolor (L.) Moench] were investigated over two years (2012 and 2014) under limited water resource conditions. Energy sorghum was grown under four water supply regimes: rain-fed (or dry-land, level 1), 50% (level 2), 75% (level 3) and 100% (level 4) of crop evapotranspiration rates (% ETc). Biomass yields ranged from 5.8 to 16.6 Mg ha1 (dry weight) after 126 days of growth. Average water use efficiencies ranged from 3.95 kg m3 to 23.4 kg m3. Net return was approximately 410 $ ha1 with water depths above 400 ha-mm. These results suggest that it is possible to obtain more than 60 Mg ha1 of sorghum biomass (wet basis) with at least 425 mm of water. While biomass yield under irrigation was greater than rain-fed conditions, there were no significant differences among irrigation treatments. Biomass chemical composition did not differ significantly among water treatments suggesting that biofuel quality would not be affected by water deficits. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Feedstock Bioenergy Sorghum bicolor L. Moench Irrigation Biomass Water use efficiency
1. Introduction In the U.S. most of the ethanol used as fuel is derived from corn [1,2]. Even if the entire crop of corn produced in U.S. were to be used for biofuel production, it would not be enough to meet the RFS2 target. Moreover, given that corn is an important food and feed crop, it is highly unlikely that 100% of the corn produced in U.S. will be used for bioenergy production. Therefore, there is an urgent need for evaluating the potential of alternative bioenergy crops to meet the RFS2 goals. Considerable research has been directed towards identifying alternate bioenergy crops. Among the candidate crops, sorghum is very promising because of its well-known drought tolerance potential. Other favorable characteristics that make sorghum a potential bioenergy crop include: high yield potential, biomass composition, high water use
* Corresponding author. Biological and Agricultural Engineering Department, Texas A&M AgriLife Research, 2415 E. Hwy 83, Weslaco, TX 78596-8399, USA. E-mail addresses: [email protected] (J. Enciso), [email protected] (J. Jifon), [email protected] (L. Ribera), [email protected] (S.D. Zapata), [email protected] (G.K. Ganjegunte). http://dx.doi.org/10.1016/j.biombioe.2015.07.021 0961-9534/© 2015 Elsevier Ltd. All rights reserved.
efficiency, established production system, and potential for genetic improvement using both traditional and genomic approaches [3]. Renewed interest in bioenergy in the recent years has led to the development of new cultivars that have higher biomass yields, enhanced sucrose and structural carbohydrates (cellulose and hemicelluloses contents). In addition to biomass yield and composition, production inputs such as water, fertilizers, and feedstock conversion efficiencies are also critical in determining the profitability and economic sustainability of the biofuel enterprise [4e6]. Only a few studies have addressed this key question of input requirements for feedstock production especially in the context of regional water resources (quantity and quality) and fertilizer costs [4]. Excessive use of scarce, expensive inputs will likely reduce the chances of economic success by inflating the overall production cost per tonne of biomass, as well as the cost per liter of biofuel [7]. The knowledge of nitrogen and water efficiency will allow farmers to adapt their production practices to climate variability and to recognize the risk of growing bioenergy crops [4]. Every year several thousand hectares of farmland remain idle due to low crop return margins, high water delivery costs, and limited water supplies. Thus, future agricultural crops including bioenergy crops will have to be
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produced under water constraints [8]. Therefore it is important to understand the biomass yield potential of these new biomass crops under limited water availability. The goal of this study was to characterize (i) yield potentials, (ii) water use efficiency and (iii) farm (net) revenue of biomass sorghum under limited water availability. 2. Material and methods 2.1. Field experiment This study was conducted during the spring-summer growing seasons (FebruaryeJune) of 2012 and 2014 in commercial-type fields located at the Texas A&M AgriLife Research Center in Weslaco, South Texas (97 560 25.8500 W, 26100 1.7600 N; elevation 22 m above sea level). The soil type at the study site is a sandy clay loam (fine-loamy, mixed, hyperthermic Typic Calciustolls). Study area has a humid subtropical climate and an average annual rainfall of 558 mm. Energy sorghum (Sorghum bicolor L. Moench) hybrid ES5200 with the Skyscraper® high biomass trait (Blade Seeds, Ceres, Inc., Thousands Oaks, CA, USA) was direct-seeded with an inter-row spacing of 0.762 m. The seed areal density was 292,000 ha1 with a seeding depth of 38e45 mm in both years. A subsurface drip irrigation system was installed in 2012 and 2014 in the center of each bed using drip tape (Model Typhoon Series, Netafim USA, Fresno, CA, USA) with 15 mm thickness. Drip emitters were spaced 0.60 m along the lateral with a nominal discharge of 1.5 L h1 per emitter, resulting in a water application rate of 3.3 mm h1. The emitters were constructed of plastic impregnated with Trifluralin to inhibit root intrusion. The high flow-rate of the drip tapes was used to avoid emitter plugging. The drip irrigation system was used to assure uniform germinations and to have better control in measuring water inputs during irrigation. It is expected to translate the yields to surface irrigation systems by adapting them to different efficiencies and irrigation uniformities. Irrigation water was obtained from the Rio Grande, and was filtered using disk filters prior to field use. Planting dates and irrigation amounts were typical for the region (Table 1). A randomized complete block design with four water application levels was utilized for this study: dry-land; two water limiting conditions levels, level 2 and level 3; and a full irrigation treatment, with three replications. The different water application levels were achieved by irrigating to replace crop water use calculated using the sorghum crop coefficient suggested by FAO 56 and using the PennmaneMonteith ET equation [9]. Water levels 2 and 3 were planned to target approximately 50% and 75% of ET, but this was difficult to achieve because rainfall sometimes occurred while the treatments were irrigated or just after irrigation. Crop coefficients were applied to ETo to adjust for the particular crop, stage of
growth, and local conditions in determining water use. The standard Kc ini, Kc mid, and Kc end values of 0.15, 1.15 and 1.1 were used for irrigation [9]. The length of the four growth stages were 20d for initial, 35 d for development, 40 d for mid and 30 d for the end stage. An internet based program developed by Texas A&M AgriLife Research which is accessed at [10] was used in the calculations. Irrigation assumed 100% system efficiency. An automatic weather station (model ET106, Campbell Scientific, Logan, UT) at the site was used to monitor rainfall (TE525 tipping bucket rain gauge), maximum and minimum temperature, relative humidity (CS500 temperature and relative humidity sensor), total solar radiation (LI200X pyranometer), and average wind speed (034A wind set) which were recorded hourly using a CR10X data logger. Water application was measured using totalizing flow meters for each irrigation level in each block. Separate supply lines with cutoff valves were set up to control water supply. All plots received nitrogen at a rate of 100 kg ha1 (from urea ammonium nitrate; 32% mass fraction of N) applied in two equal split applications through the drip system. The same total fertilizer amount was side-dressed on the dryland plots. Each plot was 36.5 m long and 3.0 m wide, with four raised beds spaced 0.76 m apart (4 rows per plot). Two rows were used as border rows between plots. Field operations each season included disking and chiseling the soil, bed formation, planting, cultivation, and spraying herbicides for weed control. Soil samples were collected about one week before planting from 0 to 30 cm depths from each plot in 2012 and 2014 and analyzed for chemical properties at the Texas A&M AgriLife Extension Service Soil, Water and Forage Testing Laboratory. Field plots were harvested at the end of each season using a forage harvester (model: Jaguar 940; Claas Herzebrock, Germany). A separate weigh wagon pulled alongside the harvester was used to collect and measure fresh mass of harvested plots. Just prior to whole-plot harvests, a sub-sample of five or six plants was randomly selected from the center two rows and oven-dried at 60 C to constant weight to determine tissue moisture content. Subsamples from each plot were also collected and used to determine cell wall composition (cellulose, hemicellulose, and lignin) using the method proposed by Sluiter et al. [11] at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas. All data were subjected to analysis of variance (ANOVA) using the Proc Mixed procedures in SAS [12]. The irrigation treatment was treated as fixed effect. Replications and year were considered random effects. The relationship between fresh and dry biomass yield and total water applied was analyzed with the SAS NLIN procedure [12], using linear, quadratic and exponential models. The quadratic model was preferred over other models based on the highest correlation coefficient to simulate the response between fresh and dry biomass and total water applied in 2012 and 2014. 2.2. Economic analysis
Table 1 Agronomic and irrigation data for 2012 and 2014 growing seasons. Activity/Input
Planting date Harvesting date Length of growing season (d) Growing season precipitation (mm) Dry-land irrigation treatment elevel 1 (mm) Limited irrigation-level 2 treatment (mm) Limited irrigation-level 3 treatment (mm) Full irrigation (100% ET treatment) elevel 4 (mm) Reference ET (mm) Sorghum ET (mm)
Year 2012
2014
4/16 8/20 126 102 45 265 336 429 537 582
4/15 8/25 132 153 37 283 380 443 560 554
The model used to analyze the empirical data of this study is based on the crop input response function and profit maximization model proposed by Tembo [13] to estimate the optimal level of nitrogen fertilizer for wheat. Compared to the stochastic plateau used as the crop input response function by Tembo [13], we considered that the relationship between irrigation water and energy sorghum yield is well-defined by a quadratic response function. Additionally, the proposed model extends Tembo [13] economic analysis by taking into account not only the agronomic component, but both the feedstock and biofuel production processes. The energy sorghum yield response to a water input is assumed to be given by a quadratic function
J. Enciso et al. / Biomass and Bioenergy 81 (2015) 339e344
yi ¼ b1 wi þ b2 w2i þ εi ;
(2.1)
where yi is the average dry yield of the ith treatment level, wi is total water applied including both rainfall (wrain ) and irrigation (wirr i i ), b's are yield response parameters, and εi ~ N(0,s2) is a random error term. Consider a risk neutral decision maker whose objective is to maximize the expected profit of producing ethanol from energy sorghum; then objective function can be expressed as
¼ EðRi Þ EðCi Þ; E pi wrain ; wirr i i
(2.2)
where Eð$Þ is the expected value operator, and pi, Ri and Ci are the profit, revenue and cost function, respectively. For illustration purposes and without loss of generality, it was assumed that ethanol was produced using a hydrolysis conversion process. Namely, the total ethanol produced out of yi is given by mi ¼ ayi, where a is the hydrolysis conversion rate. Then, the revenue function and cost function, including both feedstock and biofuel production cost, are given by
Ri ¼ pmi ;
(2.3)
and var Ci ¼ rwirr yi þ cfix þ cm mi ; i þc
(2.4)
where p is the ethanol price, r is the irrigation price, cvar is the variable harvesting and hauling cost, cfix includes all other costs independent of yield, and cm is the unit cost of producing ethanol. Finally, the expected profit function in (2.2) is equal to
2 irr rain irr rain irr ¼ pa b w þ b w E pi wrain ; w þ w þ w 1 2 i i i i i i n var b1 wrain rwirr þ wirr i þ c i i 2 þ cfix þ cm a b1 wrain þ wirr þ b2 wrain i i i 2 þ b2 wrain : þ wirr þ wirr i i i (2.5) irr*
Therefore, given a rainfall level received, w is the profit maximizing level of irrigation water that needs to be supplied. 3. Results and discussion 3.1. Rainfall and irrigation patterns Rainfall patterns and amounts were different during each year of the study, resulting in different irrigation needs for each season (Fig. 1, Table 1). Rainfall in 2012 was lower than that of 2014. About 102 mm was received during the growing season, and generally followed a bimodal pattern observed during the second week of May and last week of June. Rainfall received during the 2014 growing season was 153 mm, which was evenly distributed during the growing season. The rainfall received during the growing season in both years was well below the long term average of 272 mm. The total irrigation applied in 2012 was 429 mm for the full irrigation treatment, and 45 mm for the dry-land irrigation. In 2014, the irrigation applied for the full irrigation treatment was 443 mm and 37 mm for the dry-and treatment. Additionally, more water was applied in 2014, because on two occasions, rainfall occurred
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just after irrigation water was applied. The dry-land irrigation treatments were irrigated in both years to assure a uniform germination of the seed. In some years dry-land yields are not possible without irrigation. 3.2. Sorghum yield and quality response to water applications 3.2.1. Sorghum yield Higher sorghum fresh yields were observed for the higher water treatments (levels 2, 3, and 4) than the dry-land treatment in both years of the study (Table 2). The relation between total water applied and fresh sorghum wet yield is presented in Fig. 2. There were no statistically significant differences on fresh yield between the water limiting treatments (2 and 3), and the full irrigation treatment (4) for both years of the experiment (Table 2). In 2012, the dry-land irrigation treatment received 102 mm of rainfall and 45 mm of irrigation, which produced a fresh yield of 24 Mg ha1. During the same year, the yield increased from 24 to 46.1 Mg ha1 when the total water applied increased to 309 mm. Although, numerically higher yields were observed in 2012 with water depths above 309 mm, the differences on yield were not statistically significant. Similarly, in 2014 with water depths above 436 mm, fresh weight numerically increased slightly with more water applied, but the yields were not statistically different. In 2014 the fresh biomass yield decreased from 58.9 Mg ha1 under the level 3 (596 mm) treatment to 69.6 Mg ha1 under full irrigation (638 mm). Slightly lower yields in the full irrigation treatment verses the water limiting treatments could be due to increased insect damage observed in the full water treatment. The sorghum experiment was affected by sugarcane aphids one month before harvest in 2014. The insect populations were not counted, but it was visually observed that the full irrigation treatment was more affected than the other treatments, probably reducing biomass accumulation. In 2012, the dry biomass yields increased from 5.8 Mg ha1 under water depth of 147 mm to 11.7 Mg ha1 under 309 mm treatment. Sorghum dry biomass yields increased slightly when greater amounts of water were applied. Similarly in 2014, the dry yields increased from 8.7 under dryland treatment to 14.7 Mg ha1 under level 2 treatments (439 mm). Dry biomass yields did not differ significantly among levels 2, 3 and 4, although water level 3 produced maximum dry biomass yield of 16.6 Mg ha1. As explained above higher yield in level 3 than level 2 was due to greater insect damage observed in level 2 water treatment. There were some differences on the amount of water applied and the yield response during 2012 and 2014, probably because the year 2014 received more rainfall than 2012 (51 mm). Biomass yields obtained in our study were comparative with that reported in literature. Tamang et al. [14] reported that the total dry matter yield of four sorghum cultivars (sweet and photoperiod sensitive) grown near Lubbock, Texas averaged 13 Mg ha1 over a two year study period. Similar to our study, the total amount of water applied in their study was 349 mm in year 1 and 405 mm in year 2. However, the potential for energy sorghum could be much greater because Rooney et al. [3] reported a total dry matter yield of 30 Mg ha1 (dry weight basis) from a single harvest near College Station, Texas. Habyarimana et al. [15] evaluated performance of 75 sorghum cultivars under different water regimes under Mediterranean conditions and reported that dry biomass yields were greater under irrigated conditions compared to that under rain fed conditions. Similar to our study they reported that dry biomass yields for the same cultivar were significantly greater under full irrigation than dry-land conditions. 3.2.2. Biomass composition Sorghum biomass compositions under four water levels are
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J. Enciso et al. / Biomass and Bioenergy 81 (2015) 339e344
Fig. 1. Cumulative precipitation at the study site for 2012, and 2014.
Table 2 Average irrigation applied, fresh yield, dry yields and irrigation use efficiencies for the energy sorghum experiment in the Lower Rio Grande Valley, TX. Treatment 2012 Dry-land Level 2 Level 3 Level 4 2014 Dry-land Level 2 Level 3 Level 4
Irrigation þ rainfall (mm)
Fresh yield (Mg ha1)
Dry mass yield (Mg ha1)
Water use efficiency kg m3; (fresh basis)
147 309 373 436
24.0 46.1 58.6 60.2
b a a a
5.8 11.7 14.0 14.6
b a a a
3.95 3.79 3.75 3.35
a b b b
190 436 596 638
36.0 58.0 69.4 61.3
b a a a
8.7 14.7 16.6 14.9
b a a a
4.58 3.37 2.79 2.34
a b b b
presented in Table 3. There were no statistically significant differences in ash, lignin, cellulose and hemicellulose contents of biomass produced under different water levels during either of the study years. The biomass contained an average of 339 g kg1 cellulose, 375 g kg1 hemicellulose, 162 g kg1 lignin, and 20 g kg1 ash in 2012 and had similar composition in 2014 with 309 g kg1 cellulose, 246 g kg1 hemicellulose, 162 g kg1 lignin, and 22 g kg1 ash. The composition of sorghum biomass in our study is similar to that reported in other studies. Guimaraces et al. [16] reported that the cellulose, hemicellulose and lignin contents of sweet sorghum ranged from 21 to 49 g kg1, 18e35 g kg1 and 2e12 g kg1, respectively. They also reported that the theoretical yield of ethanol of sweet sorghum ranged from 221 to 412 L Mg1. Our study results
Fig. 2. Average fresh wet yield (Mg ha1) response to four water levels (mm) in drip irrigated sorghum during 2012 and 2014.
are also in agreement with the range for cellulose, hemicellulose and lignin content in sorghum reported in other studies [17]. According to these studies, sorghum typically contained 27e48 g kg1 cellulose, 19e24 g kg1 hemicellulose and 9e32 g kg1 lignin. 3.2.3. Water use efficiency (WUE) and energy production The dry-land treatment resulted in higher average WUE than the water limiting and full irrigation treatments in both years. In 2012, WUE decreased from 3.95 kg m3 for the dry-land treatment to 3.79 kg m3 for the water limiting treatment 2 which received an average irrigation depth of 207 mm. During the same year, there were no statistically significant differences in WUE for the water limiting treatments 2, 3, and 4. The WUE in water limiting treatments varied from 3.79 kg m3 to 3.35 kg m3 with average irrigation depth ranging from 207 to 334 mm. Similar results were observed in 2014, WUE was higher for the dry-land irrigation treatment (4.58 kg m3) and WUE decreased for the water limiting and full irrigation water treatments. The WUE for the water limiting treatments and full irrigation were not statistically different and the WUE varied from 3.37 kg m3 to 2.34 kg m3 with averaged applied irrigation depths ranging from 283 to 485 mm. Similar WUE have been observed by other authors. WUEs ranging from 3.0 kg m3 to 4.7 kg m3 have been observed in the High Plains of Texas [3]. Their biomass yields (dry basis) ranged from 11 to 23 Mg ha1 during three years of the experiment and our yields ranged from 5.8 to 14.6 Mg ha1. The lowest yields in this experiment were observed for the dry-land irrigation treatments resulting in a yield of 5.8 Mg ha1 in 2012 and 8.7 Mg ha1 in 2014. Lowest yields and WUE were observed in the Lower Rio Grande compared to the Texas High Plains, because more rainfall was received in the High Plains. In another study conducted in Italy, WUEs of 2.73 kg m3 were reported which were slightly lower than the ones observed in this study [18].
J. Enciso et al. / Biomass and Bioenergy 81 (2015) 339e344 Table 3 Biomass chemical composition (leaves and stalks) of the energy sorghum grown in 2012 and 2014 in Weslaco, TX. Treatment 2012 Dry-land Level 2 Level 3 Level 4 2014 Dry-land Level 2 Level 3 Level 4
Ash g kg1
Total lignin g kg1
Cellulose g kg1
Hemicellulose g kg1
23 15 16 25
158 163 167 161
312 335 357 353
350 359 404 385
24 24 21 20
156 159 167 166
300 307 310 319
239 238 253 254
Table 4 Technical and economic parameters used. Parameter
Value
b1 b2
0.05665 0.00005 272 0.1621 10 1003.72 302.83 0.3566 0.5891
Rainfall (mm) Irrigation price ($ mm1) Variable cost ($ Mg1) Fixed cost ($ ha1) Ethanol conversion rate (L Mg1) Ethanol production cost ($ L1) Ethanol price ($ L1)
3.3. Economic analysis results Yield response parameters, as well as the technical and economic parameters of the feedstock and biofuel production processes are presented in Table 4. With the aim of making the findings of this study of practical use over time, the rainfall level was set to be equal to the long term rainfall average (i.e., 272 mm). The irrigation cost and fixed production cost were estimated to be equal to 0.16 $ mm1 and 1003.7 $ ha1, respectively. The fixed production cost includes 776.4 $ ha1 for all the expenses incurred on seeds, labor, machinery use and depreciation, interest and land rental; and 227.3 $ ha1 for harvesting and hauling as reported in Monge et al. [2]. The variable harvesting and hauling cost, hydrolysis conversion rate and ethanol production cost were based on the values reported in Monge et al. [2]. The ethanol production cost includes the corresponding dividends, interest and operating and fixed cost of each gallon of ethanol produced. Lastly, the ethanol price corresponds to the 2014 nominal ethanol wholesale price
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estimated by the U.S. Energy Information Administration [19]. Empirical results suggest that at the observed yields, current ethanol price and considered feedstock and biofuel production technologies it is not economically feasible to produce ethanol from energy sorghum. The profit response to the irrigation water input is presented in Fig. 3. Even though no profits were observed at any irrigation level, the model suggests that maximum profits (e.i., 10.9 $ ha1) are obtained when 310 mm e in addition to the 272 mm received in the form of precipitation-of irrigation water are supplied. The breakeven ethanol price (price at which total cost and total revenue are equal) was estimated to be equal to 0.59 $ L1, or just 0.35% higher than the 2014 nominal ethanol wholesale price. It is important to mention that no tax or biofuel production credits were considered in this analysis, and that the inclusion of those credits is likely to increase the chances of economic success. Similar non-positive profit levels were obtained by Monge et al., [2]. Empirical results also suggest that further efforts are needed to develop high yielding energy sorghum lines better adapted to local production conditions. 4. Conclusions The biomass yield and composition of the energy sorghum obtained in our study compared well with that reported in the literature. While dry-land conditions produced least amount of biomass, level 3 water treatments (75% ET) resulted in the greatest amount of biomass. Application of water at 100% ET resulted in increased insect damages and reduced the biomass yield. The composition of biomass did not differ significantly among water treatments and compared well with those reported in the literature. As expected dry-land treatment resulted in higher average IUE than the irrigation treatments in both the study years. However, optimal yields were obtained with 75% ET water level. Economic analysis indicated that production of energy sorghum under dryland conditions is not recommended as it results in negative profits. Future efforts need to be focused on making more efficient feedstock and biofuel production processes, and on developing high yielding energy sorghum lines better adapted to dry-land conditions. Our study also indicated that it is possible to obtain more than 60 Mg ha1 of sorghum biomass (wet basis) and 14 Mg ha1 (dry basis) with at least 425 mm of water. Applications of water depths above 425 mm may result in net returns above 410 $ ha1. Results of our study can be used with crop models to simulate crop performance under different weather and soil conditions. Acknowledgments This material is based upon work supported by the Texas A&M AgriLife Research Bioenergy Initiative Program and the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2013-67009-21353. References
Fig. 3. Profit response function to irrigation water.
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Research paper
Yield, water use efficiency and economic analysis of energy sorghum in South Texas J. Enciso a, *, J. Jifon b, L. Ribera c, S.D. Zapata d, G.K. Ganjegunte e a
BAEN Dept., Texas A&M AgriLife Research, Weslaco, TX, USA Horticulture Department., Texas A&M AgriLife Research, Weslaco, TX, USA c Agricultural Economics Dept., Texas A&M AgriLife Extension, College Station, TX, USA d Agricultural Economics Dept., Texas A&M AgriLife Extension, Weslaco, TX, USA e Soil and Crop Sciences, Texas A&M AgriLife Research, El Paso, TX, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2015 Received in revised form 14 July 2015 Accepted 16 July 2015 Available online 30 July 2015
Yields, water use efficiency and economic returns (net farm revenues) of biomass sorghum [Sorghum bicolor (L.) Moench] were investigated over two years (2012 and 2014) under limited water resource conditions. Energy sorghum was grown under four water supply regimes: rain-fed (or dry-land, level 1), 50% (level 2), 75% (level 3) and 100% (level 4) of crop evapotranspiration rates (% ETc). Biomass yields ranged from 5.8 to 16.6 Mg ha1 (dry weight) after 126 days of growth. Average water use efficiencies ranged from 3.95 kg m3 to 23.4 kg m3. Net return was approximately 410 $ ha1 with water depths above 400 ha-mm. These results suggest that it is possible to obtain more than 60 Mg ha1 of sorghum biomass (wet basis) with at least 425 mm of water. While biomass yield under irrigation was greater than rain-fed conditions, there were no significant differences among irrigation treatments. Biomass chemical composition did not differ significantly among water treatments suggesting that biofuel quality would not be affected by water deficits. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Feedstock Bioenergy Sorghum bicolor L. Moench Irrigation Biomass Water use efficiency
1. Introduction In the U.S. most of the ethanol used as fuel is derived from corn [1,2]. Even if the entire crop of corn produced in U.S. were to be used for biofuel production, it would not be enough to meet the RFS2 target. Moreover, given that corn is an important food and feed crop, it is highly unlikely that 100% of the corn produced in U.S. will be used for bioenergy production. Therefore, there is an urgent need for evaluating the potential of alternative bioenergy crops to meet the RFS2 goals. Considerable research has been directed towards identifying alternate bioenergy crops. Among the candidate crops, sorghum is very promising because of its well-known drought tolerance potential. Other favorable characteristics that make sorghum a potential bioenergy crop include: high yield potential, biomass composition, high water use
* Corresponding author. Biological and Agricultural Engineering Department, Texas A&M AgriLife Research, 2415 E. Hwy 83, Weslaco, TX 78596-8399, USA. E-mail addresses: [email protected] (J. Enciso), [email protected] (J. Jifon), [email protected] (L. Ribera), [email protected] (S.D. Zapata), [email protected] (G.K. Ganjegunte). http://dx.doi.org/10.1016/j.biombioe.2015.07.021 0961-9534/© 2015 Elsevier Ltd. All rights reserved.
efficiency, established production system, and potential for genetic improvement using both traditional and genomic approaches [3]. Renewed interest in bioenergy in the recent years has led to the development of new cultivars that have higher biomass yields, enhanced sucrose and structural carbohydrates (cellulose and hemicelluloses contents). In addition to biomass yield and composition, production inputs such as water, fertilizers, and feedstock conversion efficiencies are also critical in determining the profitability and economic sustainability of the biofuel enterprise [4e6]. Only a few studies have addressed this key question of input requirements for feedstock production especially in the context of regional water resources (quantity and quality) and fertilizer costs [4]. Excessive use of scarce, expensive inputs will likely reduce the chances of economic success by inflating the overall production cost per tonne of biomass, as well as the cost per liter of biofuel [7]. The knowledge of nitrogen and water efficiency will allow farmers to adapt their production practices to climate variability and to recognize the risk of growing bioenergy crops [4]. Every year several thousand hectares of farmland remain idle due to low crop return margins, high water delivery costs, and limited water supplies. Thus, future agricultural crops including bioenergy crops will have to be
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produced under water constraints [8]. Therefore it is important to understand the biomass yield potential of these new biomass crops under limited water availability. The goal of this study was to characterize (i) yield potentials, (ii) water use efficiency and (iii) farm (net) revenue of biomass sorghum under limited water availability. 2. Material and methods 2.1. Field experiment This study was conducted during the spring-summer growing seasons (FebruaryeJune) of 2012 and 2014 in commercial-type fields located at the Texas A&M AgriLife Research Center in Weslaco, South Texas (97 560 25.8500 W, 26100 1.7600 N; elevation 22 m above sea level). The soil type at the study site is a sandy clay loam (fine-loamy, mixed, hyperthermic Typic Calciustolls). Study area has a humid subtropical climate and an average annual rainfall of 558 mm. Energy sorghum (Sorghum bicolor L. Moench) hybrid ES5200 with the Skyscraper® high biomass trait (Blade Seeds, Ceres, Inc., Thousands Oaks, CA, USA) was direct-seeded with an inter-row spacing of 0.762 m. The seed areal density was 292,000 ha1 with a seeding depth of 38e45 mm in both years. A subsurface drip irrigation system was installed in 2012 and 2014 in the center of each bed using drip tape (Model Typhoon Series, Netafim USA, Fresno, CA, USA) with 15 mm thickness. Drip emitters were spaced 0.60 m along the lateral with a nominal discharge of 1.5 L h1 per emitter, resulting in a water application rate of 3.3 mm h1. The emitters were constructed of plastic impregnated with Trifluralin to inhibit root intrusion. The high flow-rate of the drip tapes was used to avoid emitter plugging. The drip irrigation system was used to assure uniform germinations and to have better control in measuring water inputs during irrigation. It is expected to translate the yields to surface irrigation systems by adapting them to different efficiencies and irrigation uniformities. Irrigation water was obtained from the Rio Grande, and was filtered using disk filters prior to field use. Planting dates and irrigation amounts were typical for the region (Table 1). A randomized complete block design with four water application levels was utilized for this study: dry-land; two water limiting conditions levels, level 2 and level 3; and a full irrigation treatment, with three replications. The different water application levels were achieved by irrigating to replace crop water use calculated using the sorghum crop coefficient suggested by FAO 56 and using the PennmaneMonteith ET equation [9]. Water levels 2 and 3 were planned to target approximately 50% and 75% of ET, but this was difficult to achieve because rainfall sometimes occurred while the treatments were irrigated or just after irrigation. Crop coefficients were applied to ETo to adjust for the particular crop, stage of
growth, and local conditions in determining water use. The standard Kc ini, Kc mid, and Kc end values of 0.15, 1.15 and 1.1 were used for irrigation [9]. The length of the four growth stages were 20d for initial, 35 d for development, 40 d for mid and 30 d for the end stage. An internet based program developed by Texas A&M AgriLife Research which is accessed at [10] was used in the calculations. Irrigation assumed 100% system efficiency. An automatic weather station (model ET106, Campbell Scientific, Logan, UT) at the site was used to monitor rainfall (TE525 tipping bucket rain gauge), maximum and minimum temperature, relative humidity (CS500 temperature and relative humidity sensor), total solar radiation (LI200X pyranometer), and average wind speed (034A wind set) which were recorded hourly using a CR10X data logger. Water application was measured using totalizing flow meters for each irrigation level in each block. Separate supply lines with cutoff valves were set up to control water supply. All plots received nitrogen at a rate of 100 kg ha1 (from urea ammonium nitrate; 32% mass fraction of N) applied in two equal split applications through the drip system. The same total fertilizer amount was side-dressed on the dryland plots. Each plot was 36.5 m long and 3.0 m wide, with four raised beds spaced 0.76 m apart (4 rows per plot). Two rows were used as border rows between plots. Field operations each season included disking and chiseling the soil, bed formation, planting, cultivation, and spraying herbicides for weed control. Soil samples were collected about one week before planting from 0 to 30 cm depths from each plot in 2012 and 2014 and analyzed for chemical properties at the Texas A&M AgriLife Extension Service Soil, Water and Forage Testing Laboratory. Field plots were harvested at the end of each season using a forage harvester (model: Jaguar 940; Claas Herzebrock, Germany). A separate weigh wagon pulled alongside the harvester was used to collect and measure fresh mass of harvested plots. Just prior to whole-plot harvests, a sub-sample of five or six plants was randomly selected from the center two rows and oven-dried at 60 C to constant weight to determine tissue moisture content. Subsamples from each plot were also collected and used to determine cell wall composition (cellulose, hemicellulose, and lignin) using the method proposed by Sluiter et al. [11] at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas. All data were subjected to analysis of variance (ANOVA) using the Proc Mixed procedures in SAS [12]. The irrigation treatment was treated as fixed effect. Replications and year were considered random effects. The relationship between fresh and dry biomass yield and total water applied was analyzed with the SAS NLIN procedure [12], using linear, quadratic and exponential models. The quadratic model was preferred over other models based on the highest correlation coefficient to simulate the response between fresh and dry biomass and total water applied in 2012 and 2014. 2.2. Economic analysis
Table 1 Agronomic and irrigation data for 2012 and 2014 growing seasons. Activity/Input
Planting date Harvesting date Length of growing season (d) Growing season precipitation (mm) Dry-land irrigation treatment elevel 1 (mm) Limited irrigation-level 2 treatment (mm) Limited irrigation-level 3 treatment (mm) Full irrigation (100% ET treatment) elevel 4 (mm) Reference ET (mm) Sorghum ET (mm)
Year 2012
2014
4/16 8/20 126 102 45 265 336 429 537 582
4/15 8/25 132 153 37 283 380 443 560 554
The model used to analyze the empirical data of this study is based on the crop input response function and profit maximization model proposed by Tembo [13] to estimate the optimal level of nitrogen fertilizer for wheat. Compared to the stochastic plateau used as the crop input response function by Tembo [13], we considered that the relationship between irrigation water and energy sorghum yield is well-defined by a quadratic response function. Additionally, the proposed model extends Tembo [13] economic analysis by taking into account not only the agronomic component, but both the feedstock and biofuel production processes. The energy sorghum yield response to a water input is assumed to be given by a quadratic function
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yi ¼ b1 wi þ b2 w2i þ εi ;
(2.1)
where yi is the average dry yield of the ith treatment level, wi is total water applied including both rainfall (wrain ) and irrigation (wirr i i ), b's are yield response parameters, and εi ~ N(0,s2) is a random error term. Consider a risk neutral decision maker whose objective is to maximize the expected profit of producing ethanol from energy sorghum; then objective function can be expressed as
¼ EðRi Þ EðCi Þ; E pi wrain ; wirr i i
(2.2)
where Eð$Þ is the expected value operator, and pi, Ri and Ci are the profit, revenue and cost function, respectively. For illustration purposes and without loss of generality, it was assumed that ethanol was produced using a hydrolysis conversion process. Namely, the total ethanol produced out of yi is given by mi ¼ ayi, where a is the hydrolysis conversion rate. Then, the revenue function and cost function, including both feedstock and biofuel production cost, are given by
Ri ¼ pmi ;
(2.3)
and var Ci ¼ rwirr yi þ cfix þ cm mi ; i þc
(2.4)
where p is the ethanol price, r is the irrigation price, cvar is the variable harvesting and hauling cost, cfix includes all other costs independent of yield, and cm is the unit cost of producing ethanol. Finally, the expected profit function in (2.2) is equal to
2 irr rain irr rain irr ¼ pa b w þ b w E pi wrain ; w þ w þ w 1 2 i i i i i i n var b1 wrain rwirr þ wirr i þ c i i 2 þ cfix þ cm a b1 wrain þ wirr þ b2 wrain i i i 2 þ b2 wrain : þ wirr þ wirr i i i (2.5) irr*
Therefore, given a rainfall level received, w is the profit maximizing level of irrigation water that needs to be supplied. 3. Results and discussion 3.1. Rainfall and irrigation patterns Rainfall patterns and amounts were different during each year of the study, resulting in different irrigation needs for each season (Fig. 1, Table 1). Rainfall in 2012 was lower than that of 2014. About 102 mm was received during the growing season, and generally followed a bimodal pattern observed during the second week of May and last week of June. Rainfall received during the 2014 growing season was 153 mm, which was evenly distributed during the growing season. The rainfall received during the growing season in both years was well below the long term average of 272 mm. The total irrigation applied in 2012 was 429 mm for the full irrigation treatment, and 45 mm for the dry-land irrigation. In 2014, the irrigation applied for the full irrigation treatment was 443 mm and 37 mm for the dry-and treatment. Additionally, more water was applied in 2014, because on two occasions, rainfall occurred
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just after irrigation water was applied. The dry-land irrigation treatments were irrigated in both years to assure a uniform germination of the seed. In some years dry-land yields are not possible without irrigation. 3.2. Sorghum yield and quality response to water applications 3.2.1. Sorghum yield Higher sorghum fresh yields were observed for the higher water treatments (levels 2, 3, and 4) than the dry-land treatment in both years of the study (Table 2). The relation between total water applied and fresh sorghum wet yield is presented in Fig. 2. There were no statistically significant differences on fresh yield between the water limiting treatments (2 and 3), and the full irrigation treatment (4) for both years of the experiment (Table 2). In 2012, the dry-land irrigation treatment received 102 mm of rainfall and 45 mm of irrigation, which produced a fresh yield of 24 Mg ha1. During the same year, the yield increased from 24 to 46.1 Mg ha1 when the total water applied increased to 309 mm. Although, numerically higher yields were observed in 2012 with water depths above 309 mm, the differences on yield were not statistically significant. Similarly, in 2014 with water depths above 436 mm, fresh weight numerically increased slightly with more water applied, but the yields were not statistically different. In 2014 the fresh biomass yield decreased from 58.9 Mg ha1 under the level 3 (596 mm) treatment to 69.6 Mg ha1 under full irrigation (638 mm). Slightly lower yields in the full irrigation treatment verses the water limiting treatments could be due to increased insect damage observed in the full water treatment. The sorghum experiment was affected by sugarcane aphids one month before harvest in 2014. The insect populations were not counted, but it was visually observed that the full irrigation treatment was more affected than the other treatments, probably reducing biomass accumulation. In 2012, the dry biomass yields increased from 5.8 Mg ha1 under water depth of 147 mm to 11.7 Mg ha1 under 309 mm treatment. Sorghum dry biomass yields increased slightly when greater amounts of water were applied. Similarly in 2014, the dry yields increased from 8.7 under dryland treatment to 14.7 Mg ha1 under level 2 treatments (439 mm). Dry biomass yields did not differ significantly among levels 2, 3 and 4, although water level 3 produced maximum dry biomass yield of 16.6 Mg ha1. As explained above higher yield in level 3 than level 2 was due to greater insect damage observed in level 2 water treatment. There were some differences on the amount of water applied and the yield response during 2012 and 2014, probably because the year 2014 received more rainfall than 2012 (51 mm). Biomass yields obtained in our study were comparative with that reported in literature. Tamang et al. [14] reported that the total dry matter yield of four sorghum cultivars (sweet and photoperiod sensitive) grown near Lubbock, Texas averaged 13 Mg ha1 over a two year study period. Similar to our study, the total amount of water applied in their study was 349 mm in year 1 and 405 mm in year 2. However, the potential for energy sorghum could be much greater because Rooney et al. [3] reported a total dry matter yield of 30 Mg ha1 (dry weight basis) from a single harvest near College Station, Texas. Habyarimana et al. [15] evaluated performance of 75 sorghum cultivars under different water regimes under Mediterranean conditions and reported that dry biomass yields were greater under irrigated conditions compared to that under rain fed conditions. Similar to our study they reported that dry biomass yields for the same cultivar were significantly greater under full irrigation than dry-land conditions. 3.2.2. Biomass composition Sorghum biomass compositions under four water levels are
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Fig. 1. Cumulative precipitation at the study site for 2012, and 2014.
Table 2 Average irrigation applied, fresh yield, dry yields and irrigation use efficiencies for the energy sorghum experiment in the Lower Rio Grande Valley, TX. Treatment 2012 Dry-land Level 2 Level 3 Level 4 2014 Dry-land Level 2 Level 3 Level 4
Irrigation þ rainfall (mm)
Fresh yield (Mg ha1)
Dry mass yield (Mg ha1)
Water use efficiency kg m3; (fresh basis)
147 309 373 436
24.0 46.1 58.6 60.2
b a a a
5.8 11.7 14.0 14.6
b a a a
3.95 3.79 3.75 3.35
a b b b
190 436 596 638
36.0 58.0 69.4 61.3
b a a a
8.7 14.7 16.6 14.9
b a a a
4.58 3.37 2.79 2.34
a b b b
presented in Table 3. There were no statistically significant differences in ash, lignin, cellulose and hemicellulose contents of biomass produced under different water levels during either of the study years. The biomass contained an average of 339 g kg1 cellulose, 375 g kg1 hemicellulose, 162 g kg1 lignin, and 20 g kg1 ash in 2012 and had similar composition in 2014 with 309 g kg1 cellulose, 246 g kg1 hemicellulose, 162 g kg1 lignin, and 22 g kg1 ash. The composition of sorghum biomass in our study is similar to that reported in other studies. Guimaraces et al. [16] reported that the cellulose, hemicellulose and lignin contents of sweet sorghum ranged from 21 to 49 g kg1, 18e35 g kg1 and 2e12 g kg1, respectively. They also reported that the theoretical yield of ethanol of sweet sorghum ranged from 221 to 412 L Mg1. Our study results
Fig. 2. Average fresh wet yield (Mg ha1) response to four water levels (mm) in drip irrigated sorghum during 2012 and 2014.
are also in agreement with the range for cellulose, hemicellulose and lignin content in sorghum reported in other studies [17]. According to these studies, sorghum typically contained 27e48 g kg1 cellulose, 19e24 g kg1 hemicellulose and 9e32 g kg1 lignin. 3.2.3. Water use efficiency (WUE) and energy production The dry-land treatment resulted in higher average WUE than the water limiting and full irrigation treatments in both years. In 2012, WUE decreased from 3.95 kg m3 for the dry-land treatment to 3.79 kg m3 for the water limiting treatment 2 which received an average irrigation depth of 207 mm. During the same year, there were no statistically significant differences in WUE for the water limiting treatments 2, 3, and 4. The WUE in water limiting treatments varied from 3.79 kg m3 to 3.35 kg m3 with average irrigation depth ranging from 207 to 334 mm. Similar results were observed in 2014, WUE was higher for the dry-land irrigation treatment (4.58 kg m3) and WUE decreased for the water limiting and full irrigation water treatments. The WUE for the water limiting treatments and full irrigation were not statistically different and the WUE varied from 3.37 kg m3 to 2.34 kg m3 with averaged applied irrigation depths ranging from 283 to 485 mm. Similar WUE have been observed by other authors. WUEs ranging from 3.0 kg m3 to 4.7 kg m3 have been observed in the High Plains of Texas [3]. Their biomass yields (dry basis) ranged from 11 to 23 Mg ha1 during three years of the experiment and our yields ranged from 5.8 to 14.6 Mg ha1. The lowest yields in this experiment were observed for the dry-land irrigation treatments resulting in a yield of 5.8 Mg ha1 in 2012 and 8.7 Mg ha1 in 2014. Lowest yields and WUE were observed in the Lower Rio Grande compared to the Texas High Plains, because more rainfall was received in the High Plains. In another study conducted in Italy, WUEs of 2.73 kg m3 were reported which were slightly lower than the ones observed in this study [18].
J. Enciso et al. / Biomass and Bioenergy 81 (2015) 339e344 Table 3 Biomass chemical composition (leaves and stalks) of the energy sorghum grown in 2012 and 2014 in Weslaco, TX. Treatment 2012 Dry-land Level 2 Level 3 Level 4 2014 Dry-land Level 2 Level 3 Level 4
Ash g kg1
Total lignin g kg1
Cellulose g kg1
Hemicellulose g kg1
23 15 16 25
158 163 167 161
312 335 357 353
350 359 404 385
24 24 21 20
156 159 167 166
300 307 310 319
239 238 253 254
Table 4 Technical and economic parameters used. Parameter
Value
b1 b2
0.05665 0.00005 272 0.1621 10 1003.72 302.83 0.3566 0.5891
Rainfall (mm) Irrigation price ($ mm1) Variable cost ($ Mg1) Fixed cost ($ ha1) Ethanol conversion rate (L Mg1) Ethanol production cost ($ L1) Ethanol price ($ L1)
3.3. Economic analysis results Yield response parameters, as well as the technical and economic parameters of the feedstock and biofuel production processes are presented in Table 4. With the aim of making the findings of this study of practical use over time, the rainfall level was set to be equal to the long term rainfall average (i.e., 272 mm). The irrigation cost and fixed production cost were estimated to be equal to 0.16 $ mm1 and 1003.7 $ ha1, respectively. The fixed production cost includes 776.4 $ ha1 for all the expenses incurred on seeds, labor, machinery use and depreciation, interest and land rental; and 227.3 $ ha1 for harvesting and hauling as reported in Monge et al. [2]. The variable harvesting and hauling cost, hydrolysis conversion rate and ethanol production cost were based on the values reported in Monge et al. [2]. The ethanol production cost includes the corresponding dividends, interest and operating and fixed cost of each gallon of ethanol produced. Lastly, the ethanol price corresponds to the 2014 nominal ethanol wholesale price
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estimated by the U.S. Energy Information Administration [19]. Empirical results suggest that at the observed yields, current ethanol price and considered feedstock and biofuel production technologies it is not economically feasible to produce ethanol from energy sorghum. The profit response to the irrigation water input is presented in Fig. 3. Even though no profits were observed at any irrigation level, the model suggests that maximum profits (e.i., 10.9 $ ha1) are obtained when 310 mm e in addition to the 272 mm received in the form of precipitation-of irrigation water are supplied. The breakeven ethanol price (price at which total cost and total revenue are equal) was estimated to be equal to 0.59 $ L1, or just 0.35% higher than the 2014 nominal ethanol wholesale price. It is important to mention that no tax or biofuel production credits were considered in this analysis, and that the inclusion of those credits is likely to increase the chances of economic success. Similar non-positive profit levels were obtained by Monge et al., [2]. Empirical results also suggest that further efforts are needed to develop high yielding energy sorghum lines better adapted to local production conditions. 4. Conclusions The biomass yield and composition of the energy sorghum obtained in our study compared well with that reported in the literature. While dry-land conditions produced least amount of biomass, level 3 water treatments (75% ET) resulted in the greatest amount of biomass. Application of water at 100% ET resulted in increased insect damages and reduced the biomass yield. The composition of biomass did not differ significantly among water treatments and compared well with those reported in the literature. As expected dry-land treatment resulted in higher average IUE than the irrigation treatments in both the study years. However, optimal yields were obtained with 75% ET water level. Economic analysis indicated that production of energy sorghum under dryland conditions is not recommended as it results in negative profits. Future efforts need to be focused on making more efficient feedstock and biofuel production processes, and on developing high yielding energy sorghum lines better adapted to dry-land conditions. Our study also indicated that it is possible to obtain more than 60 Mg ha1 of sorghum biomass (wet basis) and 14 Mg ha1 (dry basis) with at least 425 mm of water. Applications of water depths above 425 mm may result in net returns above 410 $ ha1. Results of our study can be used with crop models to simulate crop performance under different weather and soil conditions. Acknowledgments This material is based upon work supported by the Texas A&M AgriLife Research Bioenergy Initiative Program and the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2013-67009-21353. References
Fig. 3. Profit response function to irrigation water.
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