Impact of climate change on climatic zoning of common bean in the South of Brazil.

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Impacto de cenários futuros de clima no zoneamento agroclimático do trigo na região Sul do Brasil.

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Σχεδιασμός κοχλιοφόρου αντλίας κονιαμάτων με μεταβλητή διατομή

Crop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype × management × environment (G × M × E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G × M × E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34'S, 152°20'), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G × M × E combinations. Capturing this advance with reduced complexity compared to the N concentration approach provides a firm basis to progress the role of modelling in exploring the genetic underpinning of complex traits and in plant breeding and crop improvement generally

Effects of nitrogen supply on canopy development of maize and sunflower

Nitrogen (N) limitation reduces canopy carbon assimilation by directly reducing leaf photosynthesis, and by developmentally reducing the rate of new leaf area development and accelerating leaf senescence. Effective use of N for biomass production under N limitation may be considered to be a result of a trade-off between the use of N to maintain high levels of specific leaf nitrogen (SLN the amount of N per unit leaf area) for high photosynthetic rate versus using N to maintain leaf area development (leaf area index - LAI). The objective here is to compare the effects of N supply on the dynamics of LAI and SLN for two crops, maize (Zea mays L.) and sunflower (Helianthus annuus L.) that contrast in the structure and development of their canopy. Three irrigated experiments imposed different levels of N and plant density. While LAI in both maize and sunflower was reduced under N limitation, leaf area development was more responsive to N supply in sunflower than maize. Observations near anthesis showed that sunflower tended to maintain SLN and adjust leaf area under reduced N supply, whereas maize tended to maintain leaf area and adjust SLN first, and, when this was not sufficient, SLN was also reduced. The two species responded differently to variation in N supply, and the implication of these different strategies for crop adaptation and management is discussed

Impacto de cenários futuros de clima no zoneamento agroclimático do trigo na região Sul do Brasil.

Made available in DSpace on 2018-02-07T23:40:19Z (GMT). No. of bitstreams: 1 ID442922017v25n2p303Agrometeoros.pdf: 1420951 bytes, checksum: c1b507368139870094bd722ffd4ef6e9 (MD5) Previous issue date: 2018-02-07bitstream/item/172343/1/ID44292-2017v25n2p303Agrometeoros.pd

Impact of climate change on climatic zoning of common bean in the South of Brazil.

Made available in DSpace on 2018-02-07T23:41:08Z (GMT). No. of bitstreams: 1 ID442932017v25n2p313Agrometeoros.pdf: 968008 bytes, checksum: 63e30154bc5c055b60315bd82beae840 (MD5) Previous issue date: 2018-02-07bitstream/item/172342/1/ID44293-2017v25n2p313Agrometeoros.pd