Modeling the biomass of energy crops: Descriptions, strengths and prospective

Jiang, Rui; Wang, Tongtong; Shao, Jin; Guo, Sheng; Zhu, Wei; Yu, Yajun; Chen, Shaolin; Hatano, Ryusuke

doi:10.1016/s2095-3119(16)61592-7

Cited by 25 publications

(11 citation statements)

References 77 publications

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“…In this context, crop modeling is an effective way to simulate the mechanisms and semi-empirical procedures associated to crop growth [13][14][15]. Many process-based crop models have been developed [16], such as AquaCrop [17], STICS [18], DSSAT [19], APSIM [20], SUCROS87 [21], WOFOST [22], and CERES [23]. Nevertheless, a common challenge for using these models is the necessity of knowing several input parameters describing the agro-environmental conditions, which are not always available.…”

Section: Introductionmentioning

confidence: 99%

A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas

et al. 2020

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In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (εconv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (εimax) and conversion (εconv_max) by a single parameter εmax, (3) the modeling of εmax as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The model calibration allowed the parameterization of εmax in four periods according to the wheat phenological stages. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. These correspond to a relative RMSE of about 19% for DM and 20% for GY. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values.

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Section: Introductionmentioning

confidence: 99%

A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas

et al. 2020

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“…Crop modeling is an effective way to simulate the mechanisms and semi‐empirical procedures during crop growth (Sinclair and Seligman, 1996). Several process‐based crop models have been developed (Jiang et al, 2017), such as AquaCrop (Raes et al, 2009b), DSSAT (Jones et al, 2003), WOFOST (Diepen et al, 1989), STICS (Brisson et al, 2003) and APSIM (McCown et al, 1996). An extensive calibration, assessment, and parameterization of these models have been conducted on wheat production in China (He et al, 2013; Xiangxiang et al, 2013; Zhang et al, 2013; Ji et al, 2014; Huang et al, 2015).…”

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confidence: 99%

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

et al. 2019

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Winter wheat (Triticum aestivum L.) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass (MS,d) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for MS,d simulation in both calibration (RMSE = 0.054–0.183 kg m−2) and validation (RMSE = 0.146 kg m−2) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m−2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future. Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.

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“…The responses of LAI develoment in SRC poplar and AFCs can be potentially used for the screening of genotypes under stress conditions or for the development of adaptation management strategies to reduce climate change impacts. Some earlier studies have mentioned the importance of LAI measurements in different ecosystems for process-based models such as APEX and ALMANAC, which were used for the prediction of climate change impacts on SRC poplar and crop productivity [72][73][74]. Thus, our findings may contribute to improving the models as well as to help farmers in better selection of crops for future climates together with fulfilling the growing requirements for food and green-energy production [35].…”

Section: Discussionmentioning

confidence: 72%

The Evaluation of Radiation Use Efficiency and Leaf Area Index Development for the Estimation of Biomass Accumulation in Short Rotation Poplar and Annual Field Crops

Tripathi,

Pohanková,

Fischer

et al. 2018

Forests

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Abstract:We evaluated the long-term pattern of leaf area index (LAI) dynamics and radiation use efficiency (RUE) in short rotation poplar in uncoppice (single stem) and coppice (multi-stem) plantations, and compared them to annual field crops (AFCs) as an alternative for bioenergy production while being more sensitive to weather fluctuation and climate change. The aim of this study was to evaluate the potential of LAI and RUE as indicators for bioenergy production and indicators of response to changing environmental conditions. For this study, we selected poplar clone J-105 (Populus nigra L. × P. maximowiczii A. Henry) and AFCs such as barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), maize (Zea mays L.), and oilseed rape (Brassica napus L.), and compared their aboveground dry mass (AGDM) production in relation to their LAI development and RUE. The results of the study showed the long-term maximum LAI (LAI max ) to be 9.5 in coppice poplar when compared to AFCs, where LAI max did not exceed the value 6. The RUE varied between 1.02 and 1.48 g MJ −1 in short rotation poplar and between 0.72 and 2.06 g MJ −1 in AFCs. We found both LAI and RUE contributed to AGDM production in short rotation poplar and RUE only contributed in AFCs. The study confirms that RUE may be considered an AGDM predictor of short rotation poplar and AFCs. This may be utilized for empirical estimates of yields and also contribute to improve the models of short rotation poplar and AFCs for the precise prediction of biomass accumulation in different environmental conditions.

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Modeling the biomass of energy crops: Descriptions, strengths and prospective

Cited by 25 publications

References 77 publications

A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas

A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

The Evaluation of Radiation Use Efficiency and Leaf Area Index Development for the Estimation of Biomass Accumulation in Short Rotation Poplar and Annual Field Crops

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