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Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

Overview of attention for article published in International Journal of Biometeorology, December 2017
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Title
Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil
Published in
International Journal of Biometeorology, December 2017
DOI 10.1007/s00484-017-1483-1
Pubmed ID
Authors

R. Battisti, P. C. Sentelhas, K. J. Boote

Abstract

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

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Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Ph. D. Student 11 20%
Student > Master 8 15%
Student > Doctoral Student 4 7%
Professor > Associate Professor 3 5%
Other 6 11%
Unknown 12 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 29%
Environmental Science 6 11%
Engineering 5 9%
Economics, Econometrics and Finance 3 5%
Earth and Planetary Sciences 2 4%
Other 2 4%
Unknown 21 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 December 2017.
All research outputs
#20,453,782
of 23,009,818 outputs
Outputs from International Journal of Biometeorology
#1,192
of 1,299 outputs
Outputs of similar age
#373,256
of 438,131 outputs
Outputs of similar age from International Journal of Biometeorology
#26
of 28 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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