A major challenge for grain yield modeling in the context of estimates made operationally for large areas is related to the identification of periods in which annual crops show greater susceptibility to environmental stress. For soybean grown in the spring-summer period in southern Brazil, the main risk factor is the occurrence of water stress during flowering and grain filling. These subperiods occur at different times across the production region due to differences in management practices of each farmer. This study aimed to relate the soybean crop calendar to the temporal profiles of normalized difference vegetation index (NDVI/MODIS), in order to present/validate a low cost technology with adequate accuracy for crop monitoring and harvest prediction. Thus, we analyzed data from soybean crop calendar (subperiods of flowering, grain filling and maturation) from EMATER (RS) regions and NDVI MODIS images. The NDVI temporal profiles allow monitoring the development of the soybean crop biomass and determining the occurrence of subperiods. Differences in NDVI values between harvests, regions and subperiods demonstrate the sensitivity of this index in detecting the responses of soybean plants to environmental conditions. Because NDVI data are generated from MODIS images, it is possible to create maps with information about the subperiods for all harvests and throughout the State, which enables greater temporal and spatial details compared to data currently available.

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Grape production in the Serra Gaúcha region, south of Brazil, is severily constrained by several diseases such as the decline and death syndrome caused grapevine trunk (fungal) diseases (GTDs) and the grapevine leafroll-associated virus (GLRaV). As pathogens induce changes in leaf tissue that modify the reflectance, the spectral signature of asymptomatic and symptomatic grapevine leaves infected by GTDs and GLRaV was analyzed to check whether spectral responses could be useful for disease identification. This work aims at (a) defining the spectral signature of grapevine leaves asymptomatic and symptomatic to GTDs and GLRaV; b) analyzing whether the spectral response of asymptomatic leaves can be distinguished from symptomatic; and (c) defining the most useful wavelengths for discriminating spectral responses. For such, reflectance of leaves in either condition collected in a "Merlot" vineyard during three growing seasons was measured using a spectroradiometer. Principal components and partial least square discriminant analyses confirmed the spectral separation and classes discrimination. The average spectra, difference spectra, and first-order derivative (FOD) spectra indicated differences between asymptomatic and symptomatic leaves in the green peak (520-550 nm), chlorophyll-associated wavelengths (650-670 nm), red edge (700-720 nm), beginning of nearinfrared (800-900 nm), and shortwave infrared. Hyperspectral data was linked to biochemical and physiological changes described for GTD and GLRaV. Variable importance in the projection (VIP) analysis showed that some wavelengths allowed to differentiate the tested pathosystems and could serve as a basis for further validation and disease classification studies.

Estudos locais de caraterização e variabilidade climática são fundamentais para geração de informações mais adaptadas às atividades agrícolas desenvolvidas em um município ou região. O objetivo desse trabalho foi caracterizar climaticamente e analisar a influência de eventos El Niño Oscilação Sul (ENOS) na série 1956-2015 de temperatura do ar de Veranópolis, RS. Para caracterização climática foram estabelecidas estatísticas descritivas das temperaturas do ar máximas, mínimas e médias mensais, estacional e anual na série e normal climatológica padrão 1961- 1990. Para identificação de diferenças entre estações e influência de eventos ENOS, os dados foram submetidos à análise de variância e teste de Duncan. Os resultados indicaram que a temperatura média anual é de 17,3ºC, variando entre 12,7ºC (julho) e 21,8ºC (janeiro). O clima é do tipo Cfb, de acordo com a classificação climática de Köppen e TE (temperado) na classificação climática do Estado. Temperaturas mínimas médias mensais inferiores a 10ºC ocorrem de maio a setembro, período de maior variabilidade interanual das temperaturas máximas (desvio padrão entre 1,5º e 1,8ºC), mínimas (1,6-1,8ºC) e médias mensais (1,4-1,7ºC). Anos de La Niña possuem temperaturas médias estacionais inferiores as de El Niño, embora diferenciação em relação a neutros ocorra somente para temperaturas mínimas na primavera e máximas no outono.

Agrometeorological-spectral model to estimate wheat yield in the State of Rio Grande do Sul, BrazilThis study aimed to estimate the wheat yield within the Cotrijal Cooperative's operational area (northern Rio Grande do Sul), using spectral and meteorological variables. Yield data (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006), monthly agrometeorological data (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) and spectral data (NDVI/MODIS, 2000-2006 were used in the analyses. The existence of a significant increase in grain yield due to the incorporation of new technologies was analyzed (technology trend).The choice of the independent variables was based on the analysis of the correlation between yield and spectral and meteorological data. For the multiple linear regression of grain yield estimation the following independent variables were used: rainfall (October), frost damage index (September), degree-days (accumulated from May to October) and NDVI (integrated from June to October). The multiple linear regressions showed satisfactory results with estimation errors below 10%, in most examined years. Accuracy, easy implementation and low cost of regressions pointed to the possibility of joint use of spectral and agrometeorological data in estimating wheat yield in the Cotrijal region. However, further studies are recommended to verify the results of the generated models when incorporating a longer series of spectral data.