Simulation of Energy Sorghum under Limited Irrigation Levels Using the EPIC Model

Chávez, José César Lenin Navarro; Enciso, Juan; Meki, Manyowa N.; Jeong, Jaehak; Singh, Vijay P.

doi:10.13031/trans.12470

Cited by 6 publications

(5 citation statements)

References 45 publications

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“…The dry biomass productivities obtained at the Weslaco site are comparable to those reported by Chavez and Enciso [16], in which the energy sorghum was grown under similar conditions. They obtained productivities that ranged from 26.57 to 28.05 Mg ha −1 , which is similar to the ones observed by Rocateli and Raper [19], who reported dry biomass yields of 26.0 to 31.6 Mg ha −1 in a study conducted in the southeastern US, and the dry biomass reported by Palumbo and Vonella [20], who reported 20.9 to 26.4 Mg ha −1 in a Mediterranean environment.…”

Section: Discussionsupporting

confidence: 85%

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Energy Sorghum Production under Arid and Semi-Arid Environments of Texas

Enciso¹,

Chávez

Ganjegunte³

et al. 2019

Water

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Water availability and supply are critical factors in the production of bioenergy. Dry biomass productivity and water use efficiency (WUE) of two biomass sorghum cultivars (Sorghum bicolor (L.) Moench) were studied in two different climatic locations during 2014 and 2015. The objective of this field study was to evaluate the dry biomass productivity and water use efficiency of two energy sorghum cultivars grown in two different climatic environments: one at Pecos located in the Chihuahuan Desert and a second one located at Weslaco in the Lower Rio Grande bordering Mexico and with a semiarid environment. There were significant differences between locations in dry biomass and WUE. Dry biomass productivity ranged from 22.4 to 31.9 Mg ha−1 in Weslaco, while in Pecos it ranged from 7.4 to 17.6 Mg ha−1. Even though it was possible to produce energy sorghum biomass in an arid environment with saline-sodic soils and saline irrigation, the energy sorghum dry biomass yield was reduced more than 50% in the arid environment compared to production in a semiarid environment with good soil and water quality, and it required approximately twice as much water. Harsh production conditions combined with low energy prices resulted in negative net returns for all treatments. However, a moderate increase in ethanol price could make the semiarid cropland of Texas an economically feasible feedstock production location.

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Section: Discussionsupporting

confidence: 85%

“…The sorghum cultivars Blade ES 5140 and Blade ES 5200 reached maximum productivities of 25.2 and 28.7 Mg h −1 , respectively. These final dry biomasses performed as expected and were comparable with yields obtained in similar conditions in previous studies [16].…”

Section: Dry Biomasssupporting

confidence: 89%

Energy Sorghum Production under Arid and Semi-Arid Environments of Texas

Enciso¹,

Chávez

Ganjegunte³

et al. 2019

Water

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“…Furthermore, EPIC is capable of predicting the effects of field management parameters, and their combined impact on crop yields for areas (Ko et al, 2009;Chavez et al, 2018;Yang et al, 2019;Feng et al, 2021). For example, simulating the variance of soil water and its effects on crop production.…”

Section: Irrigation Prejectionmentioning

confidence: 99%

Impact of irrigation on vulnerability of winter wheat under extreme climate change scenario: a case study of North China Plain

Gao,

Wang,

Yue

2024

Front. Sustain. Food Syst.

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An inadequate understanding of the impacts of adaptation countermeasures tends to exaggerate the adverse effects of climate change on agricultural systems. Motivated by proposing reasonable climate change adaptation countermeasures, the present study applied the EPIC model to quantify the impacts of climate change and irrigation changes with future socioeconomic development on agricultural production. Winter wheat yield losses using dynamic irrigation parameters in the North China Plain (NCP) from 2010 to 2099 under a scenario coupling climate change and future socioeconomic development (RCP8.5-SSP3), and those under an extreme climate change scenario (RCP8.5), were simulated. Results show that EPIC model demonstrates superior performance in simulating winter wheat yields in NCP (RMSE = 12.79 kg/ha), with the distribution of simulated and observed yields is relatively consistent. The winter wheat yield loss in the NCP was high in the south and low in the north. The yield loss rate of winter wheat was 0.21 under the RCP8.5-SSP3 scenario, compared with 0.35 under the RCP8.5 scenario, indicating a superior climatic adaptation of irrigation. However, under the RCP8.5-SSP3 scenario, the yield loss rate increased from 0.17 in the near term to 0.26 in the long term, implying the benefits of irrigation will be diminished with long-term climate change. It is noteworthy that yield improvement was facilitated by irrigation in part of the NCP (accounting for 14.6% area), suggesting that irrigation may lead to an increase in winter wheat yields in some regions even under extreme climate change conditions. This study highlights the significance of quantitatively revealing the benefits and limitations of adaptive countermeasures which could assist in enhancing climate change adaptation while preserving a sustainable agricultural system.

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“…Although conducting agronomic trials helps to identify suitable crop management practices under real‐world conditions, it requires considerable investment of time and other resources. Crop models work as tools to integrate information from agronomic experiments and facilitate prediction of varying spatial (e.g., land), temporal (e.g., weather), and management (e.g., irrigation, fertilizer, tillage) factors on crop growth and yield (Attia et al., 2016a; Chavez et al., 2018; Jones et al., 2003). Researchers have been increasingly using simulation models in applications such as prediction of yield, determination of optimal management practices, precision agriculture, and in many other aspects of agronomic and plant breeding activities around the globe (Adhikari et al., 2016; Attia et al., 2016a; Chen et al., 2017; Marin et al., 2012; Sannagoudar et al., 2019 Sonkar et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the DSSAT‐CANEGRO model for simulating the growth of energy cane (Saccharum spp.), a biofuel feedstock crop

Pokhrel

Rajan

Jifon³

et al. 2021

Crop Science

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The Decision Support System for Agrotechnology Transfer for sugarcane (DSSAT-CANEGRO) was evaluated for simulating the growth of energy cane (Saccharum spp. hybrid), a perennial biofuel feedstock crop. Plant growth data collected from a field experiment conducted in the semi-arid Texas Rolling Plains was used for model calibration and validation. All model performance indicators showed good agreement between simulated and measured biomass accumulation. After calibration, model was used for simulating plant cane and ratoon biomass yield using long-term (i.e., 33-year) historic weather data under rainfed and two irrigation levels. The irrigated treatments were a) 300 mm seasonal supplementary irrigation and b) auto-irrigation to replenish soil water in the profile to field capacity. The long-term average biomass yield of plant cane (6.4 ± 0.47 Mg ha -1 ) and ratoon (14.4 ± 0.92 Mg ha -1 ) were the lowest when simulated under rainfed conditions. Supplemental irrigation with 300 mm irrigation water significantly increased yield of both crops (14.6 ± 0.53 Mg ha -1 for plant cane and 29.5 ± 1.33 Mg ha -1 for ratoon). The auto-irrigation option substantially increased the amount of water applied and resulted in an average biomass of 16.7 ± 0.24 Mg ha -1 for plant cane and 44.4 ± 0.36 Mg ha -1 for ratoon.Biomass water use efficiency was the highest for ratoon due to its longer growing season. This modeling study showed reasonable performance of DSSAT-CANEGRO for simulating energy cane growth. Irrigation experiments using historical data showed energy cane as a competitive biomass crop in the Texas Rolling Plains region. INTRODUCTIONCrop simulation models are widely used in many aspects of crop improvement due to their ability to integrate the response of genotype, environment, management practices, and their Abbreviations: CANEGRO, sugarcane crop simulation model included in the DSSAT software; CRM, coefficient of residual mass; DSSAT, Decision Support System for Agrotechnology Transfer; ET, evapotranspiration; LAI, leaf area index; MAE, mean absolute error; RMSE, root mean square error; WUE, water use efficiency; WUE B , biomass water use efficiency.

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Simulation of Energy Sorghum under Limited Irrigation Levels Using the EPIC Model

Cited by 6 publications

References 45 publications

Energy Sorghum Production under Arid and Semi-Arid Environments of Texas

Energy Sorghum Production under Arid and Semi-Arid Environments of Texas

Impact of irrigation on vulnerability of winter wheat under extreme climate change scenario: a case study of North China Plain

Evaluation of the DSSAT‐CANEGRO model for simulating the growth of energy cane (Saccharum spp.), a biofuel feedstock crop

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