SSM-iCrop2: A simple model for diverse crop species over large areas

Soltani, Afshin; Alimagham, S M; Nehbandani, Alireza; Torabi, Benjamin; Zeinali, E; Dadrasi, Amir; Eskandar, Zand; Ghassemi, Saeid; Pourshirazi, Shabnam; Alasti, Omid; Hosseini, Reza; Zahed, M; Arabameri, R; Mohammadzadeh, Z.; Rahban, S.; Kamari, H.; Fayazi, H.; Mohammadi, S.; Keramat, S.; Vadez, Vincent; Ittersum, M.K. van; Sinclair, Thomas R.

doi:10.1016/j.agsy.2020.102855

Cited by 31 publications

(12 citation statements)

References 35 publications

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“…The model used in this study was SSM_iCrop2 1 (Soltani and Sinclair, 2012;Soltani et al, 2020a). The model includes daily phenology progress, leaf area development and senescence, dry matter production, yield formation, and soil water balance.…”

Section: Crop Modelmentioning

confidence: 99%

“…The model also accounts for the effect of freezing temperatures on plant leaf area that might take place in early spring sowings or winter sowings. The model has been tested extensively for a wide range of plant species and proved to be robust (Soltani et al, 2020a).…”

Section: Crop Modelmentioning

confidence: 99%

“…SSM_iCrop2 model has considerable advantages compared with other models. Although SSM_iCrop2 requires a maximum of 37 parameters, the actual relevant number of parameters is about half of the total number (i.e., between 15 and 20 depending on plant species) because many parameters are interconnected and some parameters are not important for some species (Soltani et al, 2020a). Whereas, the APSIM and DSSAT models needed 292 and 211 parameters to estimate the potential yield and phenological stages, respectively.…”

Section: Sensitivity Analysis Of Model Parametersmentioning

confidence: 99%

“…Some used models for simulation of soybean yield and phenological stages include EPIC (Williams and Watson, 1985), SoySim (Setiyono et al, 2010), CROPGRO-Soybean (Boote et al, 1998) and APSIM (Keating et al, 2003). There is another a group of plant models, called SSM_iCrop2 (Soltani et al, 2020a). SSM_iCrop2 model simulated a large number of plant species including orchard species and perennial forages (Soltani et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…There is another a group of plant models, called SSM_iCrop2 (Soltani et al, 2020a). SSM_iCrop2 model simulated a large number of plant species including orchard species and perennial forages (Soltani et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

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Estimating crop model parameters for simulating soybean production in Iran conditions

Nehbandani

Soltani

Nourbakhsh³

et al. 2020

OCL

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Crop modelling has the potential to contribute to food security. In this study, to provide a simple model for estimating the soybean potential yield and phenological stages in Iran, a simulation model (SSM_iCrop2) was parameterized and tested. This model estimates the soybean phenological stages and potential yield based on the weather data (minimum and maximum temperature, solar radiation and rainfall) using the phenological models such as leaf area development, mass production and partitioning and soil water balance. Regarding the model parametrization, the two maturities groups of 3 and 5 with the temperature unit of 2000 and 2400 growth degrees day (GDD) were chosen. The model evaluation results indicated that the soybean yield ranged between 1.9 and 4.8 with the average of 3.5 t.ha−1, while the range of simulated yield changes between 1.8 and 4.7 with the average of 3.7 t.ha−1. Comparing the observed yield to the simulated yield, values of r, CV and RMSE were obtained 0.84, 13%, 0.5 t.ha−1 which indicates the high accuracy of the model. All of these results indicated that the estimated model parameters are high accuracy for use in the simulation of soybean yield at the country level.

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Section: Crop Modelmentioning

confidence: 99%

Section: Crop Modelmentioning

confidence: 99%

Section: Sensitivity Analysis Of Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating crop model parameters for simulating soybean production in Iran conditions

Nehbandani

Soltani

Nourbakhsh³

et al. 2020

OCL

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Microbial effects on plant phenology and fitness

O'Brien

Ginnan

Rebolleda‐Gómez

et al. 2021

American J of Botany

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Plant development and the timing of developmental events (phenology) are tightly coupled with plant fitness. A variety of internal and external factors determine the timing and fitness consequences of these life-history transitions. Microbes interact with plants throughout their life history and impact host phenology. This review summarizes current mechanistic and theoretical knowledge surrounding microbedriven changes in plant phenology. Overall, there are examples of microbes impacting every phenological transition. While most studies have focused on flowering time, microbial effects remain important for host survival and fitness across all phenological phases. Microbe-mediated changes in nutrient acquisition and phytohormone signaling can release plants from stressful conditions and alter plant stress responses inducing shifts in developmental events. The frequency and direction of phenological effects appear to be partly determined by the lifestyle and the underlying nature of a plant-microbe interaction (i.e., mutualistic or pathogenic), in addition to the taxonomic group of the microbe (fungi vs. bacteria). Finally, we highlight biases, gaps in knowledge, and future directions. This biotic source of plasticity for plant adaptation will serve an important role in sustaining plant biodiversity and managing agriculture under the pressures of climate change.

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