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Potential increase of soybean production in Mato Grosso-Brazil based on crop modelling simulations and large-scale climate change models

Grant number: 19/10091-0
Support type:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): August 19, 2019
Effective date (End): December 18, 2019
Field of knowledge:Agronomical Sciences - Agronomy - Agricultural Meteorology
Principal researcher:Fabio Ricardo Marin
Grantee:Giulia Vitória Simioni Dias
Supervisor abroad: Marcelo Valadares Galdos
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Research place: University of Leeds, England  
Associated to the scholarship:18/21782-0 - Possible increase of soybean production in Mato Grosso based on climate change projections, BP.IC


Soybean (Glycine Max L.) is one of the most important crops worldwide for oil and protein pro-duction (FAO, 2018) and it is the most important crop for Brazilian economy. Agribusiness in the Brazilian Midwest has experienced significant growth in the last 15 years, especially soybean with an increase of around 200%. The prospected world population by 2050 is around 9 billion people, and for the same period the climate is very likely to change. The combination of population in-crease, climate change and the scarcity of new arable land around the world would require studies on how agricultural production can meet such food demand worldwide. The present project in-tends to quantify the possible increase of soybean production in the state of Mato Grosso based on projections of climate change and the expectation of technological gain until 2050, aiming to minimize the yield gap of the crop (YG) in Mato Grosso. To do so, the General Large Area Mod-el for annual crops (GLAM) will be used to produce simulations of future agricultural scenarios. The data on soybean growth and development was obtained in field in previous projects (Fapesp 2015/25702-3 and 2017/23468-9), and they will be used and compared both with actual soybean yield and potential yield provided by a well-calibrated DSSAT/CROPRO-Soybean model. These data are useful to quantify the yield gap of the crop (YG) and the possible climate change impacts if the current farming systems would be assumed to remain constant. The knowledge of the YG would therefore determine the additional production capacity that is possible in the preexisting area for a particular crop and region and the possible yield variation due to climate change will give a measure of the crop vulnerability. (AU)