A new approach for determining rice critical nitrogen concentration

Confalonieri, Roberto; Debellini, Claudia; Pirondini, M.; Possenti, P.; Bergamini, Leonardo Lima; Barlassina, G.; Bartoli, Arianna; Agostoni, Emilio; Appiani, Marta; Babazadeh, L.; Bedin, Edevan; Bignotti, A.; Bouca, M.; Bulgari, Roberta; Cantore, A.; Degradi, D.; Facchinetti, Davide; Fiacchino, Dena C.; Frialdi, M.; Galuppini, L.; Gorrini, C.; Gritti, Andrea; Gritti, P.; Lonati, S.; Martinazzi, D.; Messa, C.; Minardi, Alessandro; Nascimbene, L.; Oldani, D.; Pasqualini, E.; Perazzolo, F.; Pirovano, Livia; Pozzi, L.; Rocchetti, Gabriele; Rossi, Sergio; Rota, Leana; Rubaga, N.; Russo, Giovanni; Sala, J.M.; Seregni, S.; Sessa, Fabio; Silvestri, Siara; Simoncelli, P.; Soresi, D.; Stemberger, Christian; Tagliabue, Paola; Tettamanti, K.; Vinci, M.; Vittadini, Giorgio; Zanimacchia, M.; Zenato, O.; Zetta, A.; Bregaglio, Simone; Chiodini, Marcello Ermido; Perego, Alessia; Acutis, Marco

doi:10.1017/s0021859611000177

Cited by 18 publications

(17 citation statements)

References 29 publications

(51 reference statements)

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“…Besides the scientific results, this study-performed with students of a cropping systems MS course and following previous experiences (e.g., Confalonieri et al, 2011a;Graves et al, 2002)-further demonstrated the key role of models in education (Van Dam et al, 1997).…”

Section: Discussionsupporting

confidence: 69%

“…The latter, following previous experiences (e.g., Confalonieri et al, 2011a), was the use of models as tools for supporting education. The former was analyzing and modeling the effect of floodwater level, N fertilization, and sowing density on the rate of rice plant elongation.…”

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confidence: 97%

“…The former was analyzing and modeling the effect of floodwater level, N fertilization, and sowing density on the rate of rice plant elongation. The latter, following previous experiences (e.g., Confalonieri et al, 2011a), was the use of models as tools for supporting education. In this study, students were divided in five work packages: project coordination, greenhouse experiments, analysis and modeling of processes, statistical analyses (for both experimental results and model performances), and dissemination.…”

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confidence: 97%

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Impact of Agromanagement Practices on Rice Elongation: Analysis and Modelling

et al. 2014

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Plant height has a deep influence on the productivity of many crops, as it involves susceptibility to lodging, crop-weed competition, and the achievement of favorable harvest index. Nevertheless, modellers have practically ignored related ecophysiological processes, especially those modulated by management practices. The aim of this study was to analyze and model the processes involved with the effects of management on rice (Oryza sativa L.) elongation. Data were collected in two greenhouse experiments (2010-2011) where three factors (floodwater level, N fertilization, sowing density) were arranged in a split-plot design with three replicates. The model proposed demonstrated its suitability in reproducing both the dynamics involved with tissue elongation in the different phenological phases and the effects of submergence and N luxury consumption on elongation rates. Relative root mean square error (RRMSE) ranged between 4.23 and 12.41% for different treatments and years. The inclusion of algorithms for the impact of agronomic practices on plant height in cropping system models would increase their suitability for scenario analyses and for in silico ideotyping studies, owing to the great interest shown by geneticists in related traits. Moreover, this study-performed with students of a Cropping Systems MS course-demonstrated once more the power of modeling within educational activities. In this case, models were not the subject of the teaching but tools for analyzing processes and formalizing new knowledge

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Section: Discussionsupporting

confidence: 69%

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confidence: 97%

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confidence: 97%

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Impact of Agromanagement Practices on Rice Elongation: Analysis and Modelling

et al. 2014

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“…So, it is sensible to confirm sufficient N accumulation prior to the onset of reproductive phase for grain filling and development. Furthermore, the algorithms obtained on the basis of this concept can be coupled with other generic crop growth and management models applicable to rice, such as RiceGrow and MAZINGA (Tang et al, 2009;Confalonieri et al, 2011). The existing models could get assistance from the newly established relationships on a large dataset to intensify the domain of the genotype x environment x management landscape, in which their yield prediction methods are effective.…”

Section: Application Of Nc Dilution Curve For In-season Yield Estimationmentioning

confidence: 99%

In-season estimation of rice grain yield using critical nitrogen dilution curve

Ata-Ul-Karim

Liu

et al. 2016

Field Crops Research

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“…(1998) and Confalonieri et al . (2011). The Nmax, Nc and Nmin curves of plant and organs of rice was developed in the Yangtze River Reaches as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

et al. 2018

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Plant nitrogen (N) links with many physiological progresses of crop growth and yield formation. Accurate simulation is key to predict crop growth and yield correctly. The aim of the current study was to improve the estimation of N uptake and translocation processes in the whole rice plant as well as within plant organs in the RiceGrow model by using plant and organ maximum, critical and minimum N dilution curves. The maximum and critical N (Nc) demand (obtained from the maximum and critical curves) of shoot and root and Nc demand of organs (leaf, stem and panicle) are calculated by N concentration and biomass. Nitrogen distribution among organs is computed differently pre- and post-anthesis. Pre-anthesis distribution is determined by maximum N demand with no priority among organs. In post-anthesis distribution, panicle demands are met first and then the remaining N is allocated to other organs without priority. The amount of plant N uptake depends on plant N demand and N supplied by the soil. Calibration and validation of the established model were performed on field experiments conducted in China and the Philippines with varied N rates and N split applications; results showed that this improved model can simulate the processes of N uptake and translocation well.

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A new approach for determining rice critical nitrogen concentration

Cited by 18 publications

References 29 publications

Impact of Agromanagement Practices on Rice Elongation: Analysis and Modelling

Impact of Agromanagement Practices on Rice Elongation: Analysis and Modelling

In-season estimation of rice grain yield using critical nitrogen dilution curve

Improving the estimation and partitioning of plant nitrogen in the RiceGrow model

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