Evaluation of the ORYZA2000 Rice Growth Model under Nitrogen-Limited Conditions in an Irrigated Mediterranean Environment

Artacho, Pamela; Meza, Francisco; Alcalde, José Antonio

doi:10.4067/s0718-58392011000100003

Cited by 18 publications

(11 citation statements)

References 25 publications

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“…For dry matter and grain yield, the RMSE and NRMSE of the SimulArroz model varied from 0.6 to 1.0 Mg ha -1 and from 6.3 to 8.3%, respectively (Figure 2 E). Tang et al (2009) and Artacho et al (2011) achieved RMSE values ranging from 0.6 to 1.6 Mg ha -1 and a NRMSE of 19%, slightly higher than those obtained by the SimulArroz model for grain yield in the present study.…”

Section: Resultscontrasting

confidence: 76%

An update of new flood-irrigated rice cultivars in the SimulArroz model

Ribas

Streck

Duarte

et al. 2020

Pesq. agropec. bras.

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The objective of this work was to model, in the SimulArroz model, the three flood-irrigated rice (Oryza sativa) cultivars currently most grown in the state of Rio Grande do Sul, Brazil. The experiments to calibrate and validate the model were conducted in the municipalities of Cachoeirinha, Santa Maria, Uruguaiana, Santa Vitória do Palmar, and Cachoeira do Sul during four crop seasons. The number of leaves, phenology, aboveground dry matter biomass, and yield of each cultivar were evaluated. The results showed a slight overestimate of the R1, R4, and R9 stages; however, overall, the SimulArroz model had a good performance in simulating rice phenology for the three studied genotypes. Furthermore, the model had a reasonable accuracy in simulating aboveground dry matter and yield. The root-mean-square error (RMSE) for aboveground dry matter (leaves, stems, panicles, and grains) ranged from 0.5 to 3.0 Mg ha-1. For yield, the RMSE ranged from 0.8 to 1.3 Mg ha-1. The calibration of the SimulArroz model is efficient in simulating the growth, development, and grain yield of the most important flood-irrigated rice cultivars in Southern Brazil and can be used to estimate harvest forecast and yield potential, as well for yield gap studies.

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Section: Resultscontrasting

confidence: 76%

An update of new flood-irrigated rice cultivars in the SimulArroz model

Ribas

Streck

Duarte

et al. 2020

Pesq. agropec. bras.

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“…During the 1980's, the yield was predicted based on the past years' yield scenario and the current weather conditions [84]. The yield prediction can be significantly improved by including the knowledge of field level soil moisture and plant health status during the growing season [85]- [86]. It has been identified that LAI, fresh biomass, plant water content, and height indicates the plant health status.…”

Section: E Crop Yieldmentioning

confidence: 99%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Sivasankar

Kumar

Srivastava

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field. Keywords-biophysical parameters retrieval; crop inventory; synthetic aperture radar (SAR); synthetic aperture radar interferometry (InSAR); SAR polarimetry (PolSAR); polarimetric SAR interferometry (PolInSAR).

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“…Mostly, researchers use one set of carefully calibrated parameters either with data from several years or several specific treatments, and validate it with data for other years or other treatments. Mostly, researchers use one set of parameters calibrated either with data from several years (Shuai et al, 2009;Li et al, 2011) or several specific treatments (Jing et al, 2007;Zhang et al, 2007;Amiri and Rezaei, 2010;Artacho et al, 2011) and validate it with data for other years or treatments. For example, Li et al (2011) calibrated ORYZA with data under continuous flooding irrigation and validated it with data under alternated wetting and drying irrigation regimes.…”

Section: Introductionmentioning

confidence: 99%

Variability of Parameters of ORYZA (v3) for Rice under Different Water and Nitrogen Treatments and the Cross Treatments Validation

Xu¹

2018

IJAB

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To reveal the variability in ORYZA (v3) model parameters among different water and nitrogen treatments, and illustrate the model performance and uncertainty in rice biomass simulation with different treatment specific calibrated parameters, ORYZA (v3) model were calibrated and validated treatment specifically based on data from four different water and nitrogen treatments. Generally, the treatment specific calibrated ORYZA (v3) model is accurate in modelling rice biomass accumulation in the exact specific treatment, and it performed a little better under potential condition than water or nitrogen limited condition. Variability of treatment specific calibrated parameters for ORYZA (v3) model was low, with the maximum CV of 6.43% for biomass partitioning factor to leaf. The cross treatment validation indicated the errors in simulated total above ground biomass (WAGT) and panicles biomass (WSO) for a specific treatment were enlarged in a certain degree, when it was simulated by parameters calibrated from others. For WAGT simulation, the FF (flooding irrigation + farmers' fertilization practice) treatment calibrated parameters performed slightly better than others. For WSO simulation, all the four group calibrated parameters underestimated it in the early panicle growth phrase under water or nitrogen limited conditions, and FF and NFS (non-flooding controlled irrigation + site specific nitrogen management) treatment parameters performed slightly better than FS (flooding irrigation + site specific nitrogen management) treatment and NFF (non-flooding controlled irrigation + farmers' fertilization practice treatment). There would be a certain degree of uncertainty when rice biomass production simulated by ORYZA (v3) model with different treatment specific calibrated parameters. The range and standard deviation of simulated WAGT or WSO is low at beginning and increased along with crop development, with the values in FF and FS treatment are generally lower than that in NFF and NFS.

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Evaluation of the ORYZA2000 Rice Growth Model under Nitrogen-Limited Conditions in an Irrigated Mediterranean Environment

Cited by 18 publications

References 25 publications

An update of new flood-irrigated rice cultivars in the SimulArroz model

An update of new flood-irrigated rice cultivars in the SimulArroz model

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Variability of Parameters of ORYZA (v3) for Rice under Different Water and Nitrogen Treatments and the Cross Treatments Validation

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