This study describes the performance of five gridded data sets in reproducing precipitation and/or temperature over the complex terrain in the high Chilean Andes. The relationship of instrumental observations and the gridded data sets with climate modes of variability and the trends of indices of climate extremes are also explored between the period 1980-2015. The mismatches between gridded data sets are larger in northern and southern regions in relation to precipitation, while for temperature, disagreement is higher in central region. However, better results are delivered by the Climatic Research Unit and Global Precipitation Climatology Centre followed by Re-Analysis Interim Project. The El Niño Southern Oscillation and Pacific Decadal Oscillation indices are well correlated with precipitation in North and South Chile. Additional, trend analyses reveal a significant downward (upward) tendency for precipitation (temperature), especially in central region, delivered by observed and the majority of gridded data sets. Furthermore, the consecutive number of dry days is increasing in all regions at the annual scale. This study allows a better understanding of the capacity of global data sets and thus contributes to further climate research within this Andean region.

Devido às dificuldades para obtenção de sementes de boa qualidade fisiológica e de técnicas ideais para a reprodução seminífera, este estudo teve por objetivo avaliar a eficiência da escarificação na superação da dormência de sementes de jatobá (Hymenaea oblongifolia e Hymenaea courbaril var. stilbocarpa). O delineamento experimental utilizado foi o inteiramente casualizado, com quatro repetições de 25 sementes. Os tratamentos adotados foram: sementes intactas (controle); escarificação mecânica com lixa d’água nº 100, do lado oposto ao embrião; e tratamento de escarificação com ácido sulfúrico (H2SO4) concentrado durante 30; 60; 90 e 120 minutos. A escarificação mecânica e escarificação química durante 30 e 60 minutos constituíram-se em tratamentos pré-germinativos eficientes na superação da dormência de sementes de Hymenaea oblongifolia e Hymenaea courbaril var. stilbocarpa

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RESUMOO milho (Zea mays L.) é uma das principais culturas do Brasil, o que faz da estimativa de produtividade dessa cultura uma necessidade. Os procedimentos convencionais de previsão de safra são realizados por meio de amostragens em campo, que por vezes se mostram onerosas, pouco precisas e exigentes em mão-de-obra, fazendo com que se busque técnicas alternativas a essa. Nesse sentido tem-se o sensoriamento remoto, o qual apresenta potencial para diversos usos no meio agrícola. Dessa forma, o objetivo deste trabalho foi modelar uma equação empírica, utilizando a relação de índices de vegetação (IV), obtidos via sensoriamento remoto, com a produtividade do milho e ser capaz de predizer a produtividade das safras seguintes. Para essa análise testou-se os seguintes IV: NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), SAVI (Soil Adjusted Vegetation Index) e o GVI (Green Vegetation Index). As imagens utilizadas foram do satélite Landsat-8 para as safras 2013, 2014 e 2015, e com o intuito de validação do modelo de regressão linear adotado, a equação gerada foi testada para a safra de 2016. O NDVI foi único IV a apresentar boa correlação com a produtividade. O valor do coeficiente de determinação (R 2 ) para o NDVI, foi 0,81, demostrando sua potencialidade para estimar a produtividade, para a cultura do milho. A produtividade estimada, com base no NDVI, apresentou uma subestimativa média de 11,95 sacas/hectare, subestimando o valor da produtividade observada em 6,32%. Essa diferença percentual foi considerada satisfatória em se tratando de estimativa de produtividade.Palavras-chave: sensoriamento remoto; índices de vegetação; NDVI; Landsat 8. PREDICTION OF THE PRODUCTIVITY OF CORN IRRIGATED WITH AID OF SATELLITE IMAGES ABSTRACTCorn (Zea mays L.) is one of the main crops in Brazil, it is occupying the second place in planted area and volume of production, it makes the estimate of productivity of this crop, as well of other crops, a necessity in order to measure transport and storage of agricultural crops at farm level and at national level. The usually harvest forecasting procedures are making by field sampling,

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Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

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SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b =-0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d-1 for 12 fields analyzed and NSE values < 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and-0.0013, respectively. SAFER performed well for ET estimation after calibration, with r 2 and NSE values equal to 0.91 and RMSD = 0.469 mm d-1. SAFER also showed good performance (r 2 = 0.86) after validation, with the lowest RMSD (0.58 mm d-1) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions.