Adapting the CROPGRO Legume Model to Simulate Growth of Faba Bean

Boote, Kenneth J.; Mínguez, M. Inés; Sau, Federico

doi:10.2134/agronj2002.7430

Cited by 81 publications

(44 citation statements)

References 25 publications

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“…Furthermore, specific coefficients or parameters were adjusted using literature values and measured data. A similar approach was used to develop models for safflower [26], rapeseed [52], and faba bean [50]. This can be briefly described as:…”

Section: Modeling Approachmentioning

confidence: 99%

“…In general, to understand the influence of uncontrollable environmental effects (temperature, soil characteristics, etc. ), the usage of growth data that is not affected by water and nitrogen stress is the best way to estimate the model parameters for potential biomass accumulation of a new crop [50]. Using the measured rainfall datasets, the simulation of soil water balance showed no evidence for water stress affecting photosynthesis during the growth cycles of different sowing dates in 2016 (not shown).…”

Section: Model Adaptationmentioning

confidence: 99%

“…Hence, the function was calibrated to a T b of −3 • C (next day's photosynthetic rate equal to zero) and an asymptotic value of 15 • C without producing limits on the next day's photosynthesis (Table 6). By comparison, the values for faba bean, another cold-tolerant species, were set to −2 and 14 • C, respectively [50]. Allowing the model to simulate a higher canopy photosynthesis, the specific leaf weight (SLWREF) at which LFMAX was defined was finally reduced to 0.0034 g cm −2 (Table 11) based on the optimization against the biomass data.…”

Section: Specific Leaf Area and Photosynthesismentioning

confidence: 99%

“…The parameters affecting the partitioning among reproductive parts (pod weight and grain weight) were mainly calibrated by comparing the simulated relative ratios of pod harvest index, grain harvest index, and grain to pod ratio with the observed (Figures 7 and 8). Since these ratios are not biased by randomly-taken small biomass samples, this can be considered a reliable method of calibration [50]. Due to the absence of available literature on cardinal temperatures regarding pod addition, these temperatures were initially optimized using comparisons of observed and simulated pod indexes for both cultivars and all sowing dates.…”

Section: Reproductive Partitioningmentioning

confidence: 99%

“…Among others, the species file also defines additional coefficients and relationships for tissue composition, senescence, photosynthesis, respiration, and N-fixation. Besides the definition of cultivar specific growth and development parameters, the cultivar file is used to define a critical daylength and the slope of the daylength sensitivity to slow down or accelerate crop development depending on daylength [50]. By taking advantage of the above-mentioned generic source code of the CROPGRO model, many legume and non-legume species were successfully adapted [26,[50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

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Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model

et al. 2019

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In recent years, the intra-annual yield variability of traditional food crops grown in Europe increased due to extreme weather events driven by climate change. The Andean crop quinoa (Chenopodium quinoa Willd.), being well adapted to drought, salinity, and frost, is considered to be a promising new crop for Europe to cope with unfavorable environmental conditions. However, cultivation guidelines and cropping experiences are missing on a long-term scale. The adaptation of a mechanistic crop growth model will support the long-term evaluation of quinoa if grown under the diverse environmental conditions of Europe. The objective of this study was to adapt the process-based cropping system model (CSM) CROPGRO, which is included in the Decision Support System for Agrotechnology Transfer (DSSAT). Therefore, species and genetic coefficients were calibrated using literature values and growth analysis data, including crop life cycle, leaf area index (LAI), specific leaf area (SLA), dry matter partitioning and nitrogen concentrations in different plant tissues, aboveground biomass, and yield components, of a sowing date experiment (covering two cultivars and four sowing dates) conducted in southwestern Germany in 2016. Model evaluation was performed on the crop life cycle, final aboveground biomass, and final grain yield for different sowing dates using an independent data set collected at the same site in 2017. The resulting base temperatures regarding photosynthetic, vegetative, and reproductive processes ranged between 1 and 10 °C, while the corresponding optimum temperatures were between 15 and 36 °C. On average, the crop life cycle was predicted with a root mean square error (RMSE) of 4.7 and 3.0 days in 2016 and 2017, respectively. In 2016, the mean predicted aboveground biomass during the growth cycle showed a d-index of 0.98 (RMSE = 858 kg ha−1). Furthermore, the LAI, SLA, and leaf nitrogen concentrations were simulated with a high accuracy, showing a mean RMSE of 0.29 (d-index = 0.94), 25 cm2 g−1 (d-index = 0.88), and 0.51% (d-index = 0.95). Evaluations on the grain yield and aboveground biomass across four sowing dates in 2017 suggested a good robustness of the new quinoa model. The mean predicted aboveground biomass and grain yield at harvest maturity were 6479 kg ha−1 (RMSE = 898.9 kg ha−1) and 3843 kg ha−1 (RMSE = 450.3 kg ha−1), respectively. Thus, the CSM-CROPGRO model can be used to evaluate the long-term suitability, as well as different management strategies of quinoa under European conditions. However, further development on the simulation of small seed sizes and under water or nitrogen-limited environments are needed.

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Section: Modeling Approachmentioning

confidence: 99%

Section: Model Adaptationmentioning

confidence: 99%

Section: Specific Leaf Area and Photosynthesismentioning

confidence: 99%

Section: Reproductive Partitioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model

et al. 2019

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Adapting the CROPGRO model to simulate chia growth and yield

et al. 2020

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Chia (Salvia hispanica L.) seeds are becoming increasingly popular as a superfood in Europe. However, broad experience in growing chia in temperate climates is missing. Crop simulation models can be helpful tools for management and decision-making in crop production systems in different regions. The objective of this study was to adapt the CROPGRO model for simulating growth and yield of chia. Data sets from a field experiment conducted over 2 yr in southwestern Germany (48 • 74′ N, 08 • 92′ E, 475 m above sea level) were used for model adaptation. The initial starting point was the CROPGRO-soybean [Glycine max (L.) Merr.] model as a template for parameterizing temperature functions and setting tissue composition. Considerable iterations were made in optimizing growth, development, and photosynthesis parameters. After model calibration, the simulation of leaf area index (LAI) was reasonable for both years, slightly over-predicting LAI with an average d-statistic of 0.95 and root mean square error (RMSE) of 0.53. Simulations of final leaf number were close to the observed data with dstatistic of 0.98 and RMSE of 1.36. Simulations were acceptable for total biomass (d-statistic of 0.93), leaf (d-statistic of 0.94), stem (d-statistic of 0.94), pod mass (d-statistic of 0.89), and seed yield (d-statistic of 0.88). Pod harvest index (HI) showed good model performance (d-statistic of 0.96 and RMSE of 0.08). Overall, the model adaptation resulted in a preliminarily adapted model with realistically simulated crop growth variables. Researchers can use the developed chia model to extend knowledge on the eco-physiology of chia and to improve its production and adaption to other regions. Abbreviations: CSM, cropping system model; CUL, cultivar; DSSAT, Decision Support System for Agrotechnology Transfer; ECO, ecotype; HI, harvest index; LAI, leaf area index; N1, nitrogen fertilizer treatment 0 kg ha −1 ; N2, nitrogen fertilizer treatment 20 kg ha −1 ; N3, nitrogen fertilizer treatment 40 kg ha −1 ; PD, photothermal days; PHI, pod harvest index; RMSE, root mean square error; SLA, specific leaf area; SPE, species; Tb, base temperature; TD, thermal days; Tmax, maximum temperature; Tmin, minimum temperature; Topt, optimum temperature. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Biophysical and management factors causing yield gap in soybean in the subtropics of Brazil

et al. 2021

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Little is known about the relationships between soybean [Glycine max (L.) Merr.] maturity grouping and the yield-limiting factors in the subtropics. This information can be used to estimate soybean potential yield (Yp) and the water-limited yield (Yw) and to optimize current soybean management practices to improve yield and resource use efficiency. The objectives were (a) to estimate the Yp, Yw, and yield gaps (YGs) of soybean in subtropical Brazil and (b) to identify the biophysical and management factors which potentially explain the YG. The CSM CROPGRO model that was calibrated with data collected between 2011 and 2019 was used to estimate the influence of sowing date and maturity group (MG) on yield potentials and water efficiencies. Yield varied from 6.1 to 7.2 Mg ha −1 and from 2.5 to 5.1 Mg ha −1 between buffer zones (BZs) for Yp and Yw, respectively. The YG caused by water deficit (YGw) ranged from 26% (1.8 Mg ha −1 ) to 62% (4.1 Mg ha −1 ) of the Yp and the YG caused by management (e.g., sowing date, MG, final density) ranged from 9% (0.6 Mg ha −1 ) to 39% (2.7 Mg ha −1 ) of the Yp. The main management factor of the YG was sowing date. The potential yield was higher in the early MGs, showing greater water use efficiency in MG ≤ 5.5 (9.6 kg ha −1 mm −1 ) than in high MGs (MG ≥ 5.6). Findings from this study can be used by agronomists in subtropical regions to optimize soybean yields.

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Adapting the CROPGRO Legume Model to Simulate Growth of Faba Bean

Cited by 81 publications

References 25 publications

Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model

Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model

Adapting the CROPGRO model to simulate chia growth and yield

Biophysical and management factors causing yield gap in soybean in the subtropics of Brazil

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