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Dengue is a serious infectious disease worldwide and a climate-sensitive disease. Thus, our goals were to (i) evaluate the relationship between dengue incidence and meteorological variables (rainfall and air temperature); (ii) identify the spatiotemporal pattern of dengue incidence in the municipalities of Mato Grosso from 2001 to 2020; and (iii) verify the spatial dependence of dengue incidence in the dry and wet seasons. We used dengue data from 2001 to 2020, monthly rainfall estimates from GPM, and daily air temperature estimates from ERA-5. The municipalities of the Mato Grosso state are included in 16 healthcare territories. The seasonal rainfall pattern indicates that the peak of the dengue endemic occurred in the wet season. However, drier and/or warmer places had a lower incidence of dengue in the dry season. Furthermore, a lagged effect of meteorological variables on dengue incidence has been identified, ranging from 0 to 7 months. Hotspot areas were identified which might have the potential for an intense spreading of dengue in Mato Grosso. They were mainly concentrated in the healthcare territory of Teles Pires (ID 14) in the dry season, while they were concentrated in the healthcare territories of Garças Araguaia (ID 5), Oeste (ID 11), and Teles Pires (ID 14) in the wet season. In addition, they are located in the Am climate and in the Amazon Forest and Brazilian savanna biomes, which have higher dengue incidence values. These results help to highlight which municipalities decision-makers must intervene in the public health system to prevent and control future epidemics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]