Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018.

The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco2, ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO2 sources and sinks. Temporal changes indicate atmospheric Xco2 in the range from 362.2 to 403.4 ppm. Higher Xco2 values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m-2 sr-1 µm-1 explained the temporal changes of Xco2 in 58% (R2 adj = 0.58; p < 0.001) and precipitation in 27% (R2 adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco2 (ppm) anomalies were 6.19 ppm (source) and -6.29 ppm (sink), respectively. We conclude that the hot moments of Xco2 in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco2 spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest.

Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.

xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach.

Agriculture and soil management practices are closely related to CO2 emissions in crop fields. These practices directly interfere on the carbon dynamics between the land and atmosphere. In this study, we investigated the temporal variability of the column-averaged dry-air mole fraction of atmospheric CO2 (xCO2), solar-induced chlorophyll fluorescence (SIF), and the normalized difference vegetation index (NDVI) in areas with the main agroecosystems in southern-central Brazil as a way to understand if and how crops cycle and agricultural management could be associated with the temporal variability of NDVI, SIF and xCO2. The study was carried out in areas corresponding to the three agroecosystems': sugarcane (Pradópolis, State of São Paulo, Brazil), cropland with soybean-corn succession (Santo Antônio do Paraíso, State of Paraná, Brazil), and grassland (Águas Claras, State of Mato Grosso do Sul, Brazil). Air temperature, precipitation, NDVI, and SIF and xCO2 were retrieved from NASA-POWER, NASA-GIOVANNI, SATVeg-EMBRAPA, and OCO-2, respectively, during a two-year study. Trends were removed from the NDVI, SIF, and xCO2 time series applying the regression method. A negative correlation between SIF and xCO2 was found in sugarcane and cropland areas, but in grasslands, no correlation showed up. Higher SIF values were observed in grassland (2.24 W m-2 sr-1 µm-1), and lower xCO2 values were observed above grains, which varied from 396.8 to 404.2 ppm. Both xCO2 and SIF followed more a seasonal pattern in sugarcane and annual crops, but over pasture this presented an unusual pattern related to higher precipitation events. Our results indicate a potential use of SIF and xCO2 which could help identifying potential sources and sinks of the main additional greenhouse gas over agricultural areas.

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.