High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems.

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015-2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m-2 sr-1 µm-1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m-2 sr-1 µm-1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.

Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo.

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (TAIR, °C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year-1, or PWINTER was between 90 and 180 mm year-1. The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered "backpropagation" training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the TAIR, P, and PWINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with TAIR varying between 14 and 30 °C, and P varying between < 400 and 4000 mm year-1. The ZARC showed that 87% of Brazil is climatically apt for bamboo cultivation. The states that were classified as apt in 100% of their territories were Mato Grosso do Sul, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piauí, Maranhão, Rondônia, and Acre. The regions that have restrictions due to low TAIR represent just 11% of Brazilian territory. This agroclimatic zoning allowed for the classification of regions based on aptitude of climate for bamboo cultivation and showed that 71% of the total national territory is considered to be apt for bamboo cultivation. The regions that have restrictions are part of southern Brazil due to low values of TAIR and portions of the northern region that have high levels of P which is favorable for the development of diseases.

Maturation periods for Coffea arabica cultivars and their implications for yield and quality in Brazil.

BACKGROUND: Climatic conditions directly affect the maturation period of coffee plantations, affecting yield and beverage quality. The quality of coffee beverages is highly correlated with the length of fruit maturation, which is strongly influenced by meteorological elements. The objective was to estimate the probable times of graining and maturation of the main coffee varieties in Brazil and to quantify the influences of climate on coffee maturation. We used degree days to estimate flowering/graining periods (green fruit) and flowering/maturation periods (cherry fruit) for all cultivars. We evaluated the influence of climate on the time of maturity using Pearson correlation and nonlinear regression analysis and successfully mapped the influences of these elements. RESULTS: Arabica coffee matured up to 2-3 months earlier in São Paulo, where air temperatures (TAIR ) were higher, than in Minas Gerais, which would allow earlier harvesting and the training of seedlings at the beginning of the rainy season. Catuaí-Amarelo-IAC-62 cultivar needed 205-226 days between the end of flowering and maturation at locations with high TAIR and 375-396 days at locations with low TAIR . CONCLUSION: Water surplus and deficit were generally the most important variables for coffee maturation. Coffee matured faster in regions with high TAIR and evapotranspiration, moderate altitudes and deficits. Acaiá-Cerrado-MG-1474 and Icatu-Precoce-Amarelo-3282 were cultivars with an early cycle. © 2018 Society of Chemical Industry.