Adaptation, calibration, and application of coffee crop simulation models for assessing the impact of climate change in Brazilian conditions

Author(s)

Cleverson Henrique de Freitas

• Document type: Doctoral Thesis
• Press: Piracicaba.
• Institution: Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC)
• Defense date: 2024-06-21

Examining board members

Rubens Duarte Coelho; Jéfferson de Oliveira Costa; Andre Luis Teixeira Fernandes; Danielle Barros Ferreira

• Advisor: Rubens Duarte Coelho

Abstract

Coffee is one of the most important commodities in the world, playing a crucial role in the global economy and supporting the livelihoods of millions of individuals. In Brazil, a global leader in coffee production and exportation, this crop not only drives the economy but also sustains thousands of families. However, coffee yield is highly susceptible to climatic variables, the impacts of which are intensifying due to climate change. Thus, understanding and mitigating these impacts is essential to ensure the sustainability of coffee cultivation. This study addresses the complex interaction between climatic factors, management practices, and simulation models on the phenology and yield of Coffea arabica in Brazil, under current conditions and future scenarios. Exploratory analyses, machine learning techniques, and an agrometeorological simulation model were employed to analyze and estimate the phenology and yield of coffee across different producing regions. Adaptations and calibrations were also performed, as well as a sensitivity analysis on the agrometeorological model in regions with distinct climatic characteristics. To assess the impacts of climate change, observed meteorological data from the Brazilian Daily Weather Gridded Data (BR-DWGD) and projected data from the Climate Change Dataset for Brazil (CLIMBra) were used. The results demonstrated that selecting appropriate cultivars and implementing adaptive management practices are essential to optimize production in the face of climate variations. Machine learning techniques did not perform satisfactorily in modeling coffee flowering. Alternatively, an approach using a cumulative of 1980 degree days, considering a base temperature (Tb) of 8.5°C and a minimum of 16 mm of rain to break floral bud dormancy was proposed. However, the application of machine learning models, particularly Random Forest (RF) and XGBoost (XGB), was more effective in estimating yield, despite difficulties in estimating yield values above 60.0 bags ha-1. The adaptations made to the agrometeorological model allowed for a refinement in the yield estimates of coffee, highlighting the complexity of interactions between climatic variables and agricultural management practices. The sensitivity analysis emphasized the importance of factors such as average temperature and solar radiation on potential yield, in addition to the impact of irrigation practices and management of water deficit under rainfed conditions. Moreover, this study also highlighted the sensitivity of coffee production to future climate changes, indicating that the use of irrigation will play a significant role in mitigating the adverse effects of climate changes, especially in regions of lower latitudes, such as northern Minas Gerais and Bahia. Higher yield may be concentrated in traditionally productive areas of southern Minas Gerais, and in some regions of São Paulo and northern Paraná. However, these high yields are accompanied by greater uncertainties due to changes in precipitation and extreme temperatures. Projections also indicated that, despite the potential increase in yield in some areas due to the fertilizing effect of atmospheric CO2, losses from high temperatures and water deficits could be substantial, especially in scenarios with higher emissions and in regions without irrigation. The coffee plants phenology could also be altered, with an anticipation of anthesis, especially in colder climates, and a delay in this stage in warmer regions. For maturation, there is a trend toward anticipation across all climates and scenarios analyzed. Therefore, this study contributes to a deeper understanding of the dynamics between climate, management practices, and coffee yield, offering practical and scientific guidelines to strengthen the resilience of Brazilian coffee cultivation. The findings are particularly relevant for policymakers and professionals in the coffee industry, who can use this information to develop more effective and sustainable strategies for coffee production. (AU)

• FAPESP's process: 20/11465-8 - Evaluation and adaptation of coffee productivity models to determine climate risk and management strategies to mitigate the impacts of climate change in Brazil
• Grantee: Cleverson Henrique de Freitas
• Support Opportunities: Scholarships in Brazil - Doctorate