Assessment of extreme climate impacts on large-scale commodity and family farming agriculture in Brazil - Souza, Livia Maria Brumatti de (2023)
The changes in extreme climate patterns threaten several sectors that are climate dependent, as agriculture. Climate extremes could affect agricultural production resulting in yield and area losses. Losses in the main crops of family farming of the Brazilian semiarid region and large- scale commodity agriculture of the Mato Grosso and MATOPIBA can compromise national and international food security. Unfortunately, climate change will likely aggravate this situation in the future. This study aims to evaluate the extreme climate impacts on large-scale commodity and family farming agriculture in Brazil during the past and future periods. First, I evaluated the extreme climate impacts on large-scale commodities (soybean and maize second season) and family farming agriculture (maize, bean, and cassava) during 2003-2019. Drought events predominate during this period in both regions, and vapor pressure deficit was the index that better represent the relationship between extreme climate indexes and crop yield in both agriculture types. Family farming crops were more exposed to extreme climate events than commodity agriculture crops, and they are more vulnerable to extreme climate due to low technological levels. In family farming agriculture, maize was the crop most affected by climate extremes, followed by beans and cassava. In commodity agriculture, off-season maize yield was more impacted by drought and hot events than soybean. During this period, family farmers’ agricultural output presented negative trends, while commodity farmers agricultural output presented positive trends. These results illustrate an alarming and worrying situation for family farmers of the semiarid region. Second, to improve future climate risk assessment, the best bias correction method (linear scaling and quantile mapping) was investigated and what are the best models of CMIP6/IPCC for climate (precipitation, minimum and maximum temperatures) and extreme climate variables (maximum consecutive dry days, CDD, and extreme degree days, EDD) in Brazilian regions. The results showed that non-parametric quantile mapping methods (empirical quantile and robust empirical quantile) were the best bias correction methods for almost all variables. Linear scaling presented a slightly better performance in some models and regions for the CDD index with minimal improvement, demonstrating that bias correction cannot improve indexes not well represented by climate models. The best models varied according to the variable, but ACCESS-ESM1-5, EC-Earth3-Veg, CanESM5, EC-Earth3, andCMCC-ESM2 predominated in the variables' ranking after bias correction. Third, in a complementary analysis, the extreme climate impacts were estimated for the main crops of commodity and family farming agriculture under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) in 2021-2100 period. Our results demonstrated increasing trends of hot and dry events during crop growing seasons of both agricultural types in most scenarios and periods, culminating in yield losses. Family farmers will experience a more extreme climate and greater yield losses than commodity farmers. Both agriculture types will need to increase their resilience to deal with climate change, regardless of the scenario, however, more attention and immediate actions are needed for family farmers. Keywords: Extreme climate. Family farming. Commodity agriculture. Bias correction.