Current analysis investigates genotype x environment interaction and stability performance of grain yield with nine maize genotypes in seven environments. ANOVA revealed highly significant (p-value<0.001) data for genotypes, environments and their interactions. Only PC1 (45.4%) and PC2 (35%) were significant (p ≤ 0.05). Genotype G7 had a specific adaptation to environment E7, whereas genotypes G2 and G3 were adapted to environment E1, and genotypes G8 and G9 to environment E5. Dataset was divided into group A, composed of E5 and E7, and group B composed of E1, E2, E3 and E6. Genotypes G1, G2, G3 and G6, belonging to group B, were the most productive. Further, no environment fell into the G4, G5, G7, G8 and G9 sectors, denoting these genotypes as the poorest ones across environments. GGE biplot indicated that genotype G4 was highly unstable, whereas G3 very stable. In addition, G2 was more desirable due to its small contribution to both G and GE. On the other hand, G4 and G9 were more undesirable due to large contribution to either G or GE. Finally, genotypes G2 and G9 were very different. Their dissimilarity may be due to difference in mean yield and/or in GEI.

As queimadas impactam negativamente a biodiversidade dos ecossistemas naturais, alterando os atributos físicos e biológicos e influenciando os fluxos de energia e biogeoquímicos. Sendo assim, o objetivo deste trabalho foi analisar através do sensoriamento remoto o efeito de áreas queimadas sobre os índices espectrais NDVI (Índice de Vegetação por Diferença Normalizada) e NBR (Índice de Queimada Normalizada) e na temperatura de superfície no Parque Estadual do Araguaia (PEA) em Mato Grosso, Brasil. Imagens do satélite de Landsat 8 OLI foram utilizadas para analisar a superfície no período pré-queimada (15/06/2015) e pós-queimada (21/10/2015) no Parque Estadual. Os resultados demonstraram que o NDVI apresentou maiores valores nas áreas com vegetação densa e menores valores em solo exposto, associados à vegetação seca, carbonização ou com completa ausência de vegetação. O NBR apresentou valores negativos nas áreas queimadas, devido ao aumento da refletância após passagem do fogo devido à deposição de cinzas brancas. A temperatura da superfície foi maior no pós-queimada relacionada a uma maior capacidade de absorção da superfície (cor preta das cinzas). Estes resultados são atribuídos aos efeitos combinados de maior exposição do solo, aumento da absorção da radiação pela vegetação carbonizada e redução da evapotranspiração relativa à vegetação verde existente no período pré-queimada.  Spectral Indexes and Surface Temperature on Burnt Areas at Araguaia State Park in Mato Grosso A B S T R A C TFires harm the biodiversity of natural ecosystems, changing physical and biological attributes and influencing energy and biogeochemical flows. Therefore, the objective of this work was to analyze through remote sensing the effect of burnt areas on the NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Index) spectral indexes and on the surface temperature in Araguaia State Park (PEA) in Mato Grosso, Brazil. Satellite images of Landsat 8 OLI were used to analyze the surface in the pre-burned (06/15/2015) and post-burned (10/21/2015) period in the State Park. The results showed that NDVI showed higher values in areas with dense vegetation and lower values in exposed soil, associated with dry vegetation, carbonization or with a complete absence of vegetation. The NBR showed negative values in the burnt areas, due to the increase in reflectance after the passage of the fire due to the deposition of white ash. The surface temperature was higher in the post-firing period due to a greater absorption capacity of the surface (black color of the ashes). These results are attributed to the combined effects of greater soil exposure, increased absorption of radiation by carbonized vegetation and reduced evapotranspiration relative to green vegetation in the pre-burned period.Keywords: Fire; Protected Area; Remote Sensing.

The research on precipitation is related to analysis of its spatiotemporal variability using daily, monthly and annual data. However, there is a scarcity in the availability of information on how this variable is distributed over the hours of the day. Thus, the objective of this study was to analyze the intensity and hourly patterns of precipitation in Cuiabá in the State of Mato Grosso. Precipitation data were collected at the Cuiabá weather station of the National Institute of Meteorology from 2003 to 2018. The relative frequency of hourly precipitation was analyzed by the Spiegel method. The relative frequency of precipitation in wet season was high in the afternoon from 16:00 h (6.2%) to 17:00 h (5.9%) and low in the morning from 10:00 (3.0%) to 11:00 (2.8%). The relative frequency of precipitation during the dry season was high at 05:00 h (5.0%) and 17:00 h (5.4%) and low from 12:00 h (2.6%) and 13:00 h (2.2%). In general, the intensity of precipitation in the region was predominantly weak, followed by moderate, strong and very strong. The precipitation events in Cuiabá generally occur in the late afternoon, resulting from convective activity in the region.

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

The Brazilian Midwest has significant spatiotemporal variability in terms of precipitation and air temperature, making it more vulnerable to the occurrence of extreme weather events. The objective of this study is to characterize the trend of extreme climatic events regarding precipitation and air temperature in the Brazilian Midwest, and to analyze their relationship with Pacific and Atlantic Sea Surface Temperature anomalies (SSTAs). We used daily precipitation and air temperature data measured at 24 conventional weather stations. Pacific and Atlantic SSTA data were obtained from the Climate Prediction Center. The frequency of hot extremes had increased, while that of cold extremes had decreased significantly, thus highlighting the consistent warming across the Brazilian Midwest. The precipitation extremes had greater variability than the temperature extremes. Precipitation intensity increased in Amazonia, with no change in annual precipitation volume. The precipitation extremes in the Brazilian Savanna, Pantanal, and the Atlantic Forest did not have a well-defined pattern but indicated a trend towards a decrease in days with intense precipitation events. In general, the Equatorial Pacific and Atlantic Ocean (TNAI and TSAI) SSTAs were negatively correlated with precipitation extreme indices and positively correlated with air temperature extreme indices in the Amazon. However, the North Atlantic SSTAs were positively correlated with precipitation and air temperature extreme indices in the Brazilian Savanna and Pantanal. In addition, the Pacific SSTAs were positively correlated with precipitation intensity in the Atlantic Forest. Thus, the variability of the trends of precipitation and air temperature extreme indices in the Brazilian Midwest was observed, and it was surmised that this measure was significantly related to Pacific and Atlantic SSTAs.