Rice phenology and development are events controlled by environmental and genetic factors, and the yield potential of the crop is defined by their interaction. This study aimed at analyzing the performance of irrigated rice genotypes in contrasting ecosystems and their effects on morphophysiological characteristics. Two ecosystems (tropical and subtropical) were analyzed, as well as cultivars recommended for tropical (BRS Catiana and BRS Jaçanã) and subtropical (BRS Pampa, BRS 7 Taim and IRGA 424) regions. The experiments were arranged in a complete randomized block design, with four replicates, being the factors the genotypes, sowing times and sites. The phenological development, biomass dynamics, radiation use efficiency and grain yield were evaluated. The accumulated degree-days demand for flowering decreased faster in the tropical ecosystem than in the subtropical ecosystem for late sowing. The radiation use efficiency values were similar in the subtropical ecosystem and yield was high for all sowing dates. On the other hand, the tropical ecosystem showed a high variation for radiation use efficiency values and yield. The higher accumulation of degree-days and solar radiation during the reproductive and grain-filling phases contributed to increase yield in both ecosystems.

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The sensitivity analysis of genetic coefficients used in the productivity simulation of sugarcane by Canegro/DSSAT model (CD) was performed in order to identify which parameters have most relevance in the model calibration. The CD uses 20 parameters that aim to capture the differences between the varieties of sugarcane. The sensitivity analysis has been developed varying one parameter at a time within a certain range and keeping the others constant. Were simulated the stalk dry weight, leaf area index of green leaves and sucrose dry weight at the end of each crop cycle in a period of twenty years, between 1995 and 2014, considering three planting dates, August 01, September 01 and 01 October. The simulations were made to the city of Pelotas -RS and the results were evaluated by the standard deviation (D). The results show that genetic coefficients with great sensitivity in the simulation of the stalk dry weight were PARCEmax (D ≈ 6 t.ha -1 ) and STKPFmax (D ≈ 5 t.ha -1 ). In the sucrose dry weight simulation, the genetic coefficients with higher sensitivity were PARCEmax (D ≈ 3,5 t.ha -1 ) and STKPFmax (D ≈ 3 t.ha -1 ). In the simulation of the maximum leaf area index, the more sensitive genetic coefficients were LFMAX (D ≈ 2 cm 2 .cm -2 ), TT_POPGROWTH (D ≈ 1,4 cm 2 .cm -2 ) and Mxlfarea (D ≈ 1 cm 2 .cm -2 ). The results also show that differences in the planting date have influence in the sensitivity of genetic coefficients. Keywords: Sensitivity analysis; genetic coefficents; canegro; sugarcane Resumo A análise de sensibilidade dos coeficientes genéticos utilizados na simulação de produtividade da cana de açúcar pelo modelo Canegro/DSSAT (CD) foi realizada com o objetivo de identificar quais parâmetros tem maior relevância na calibração do modelo. O CD faz uso de 20 parâmetros que têm como objetivo capturar as diferenças entre as cultivares de cana-de-açúcar. A análise de sensibilidade foi desenvolvida variando um parâmetro por vez dentro de um determinado intervalo e mantendo os demais constantes. Foram simulados o peso seco do colmo, índice de área foliar de folhas verdes e peso seco de sacarose ao final de cada ciclo de cultivo em um período de vinte anos, entre 1995 e 2014, considerando três datas de plantio, 01 de agosto, 01 de setembro e 01 de outubro. As simulações foram feitas para o município de Pelotas -RS e os resultados foram avaliados pelo desvio padrão (D

Precision N management using optical radiation sensors is a promising management strategy. Using a combination of three spectral reflectance bands, 22 vegetation indices (VIs) were calculated and evaluated for their efficiency in estimating N status in irrigated rice (Oryza sativa L.) during growth stages. The results obtained here have showed a promising strategy to include crop stages as covariate in generalist models to predict the N status parameters for irrigated rice. This approach is interesting because it reduces the need for specific models, with different structures, for each crop stage. In addition, including crop stages as a covariate in the prediction models allows knowing the rice N status according to the crop stage, which is essential for efficient N management in commercial crops. The results obtained here show the beginning of vegetative stage (V1–V9) significantly affects the prediction of all N status parameters. The dry leaf biomass (DLB), leaf area index (LAI), leaf nitrogen uptake (LNU), and nitrogen nutrition index (NNI) can be adequately predicted with combinations of just two VIs. These results show the importance of using active sensors with more than two fixed bands, preferably including a red‐edge band, for effective crop N status estimation.

Severe complications due to myiasis infestation Complicações graves devido a infestação por miíase