Abstract One of the major challenges for effective agricultural activity monitoring systems is defining robust indicators of spatial and temporal variability for the main risk factors associated with crop production. In this context, this study aimed to analyze the potential of the Temperature-Vegetation Dryness Index (TVDI), obtained by terrestrial and orbital sensors from soybean production areas in southern Brazil, in generating spatial and temporal patterns of the main risk factor, surface moisture, to be incorporated in operational agricultural monitoring systems. For this purpose, MODIS Terra and Landsat-8 OLI/TIRS sensor images were used, as well as data from surface positioned sensors to serve as a reference. The study area encompassed one soybean crop area, soybean mapped crop areas near the experimental area, and the municipality of Carazinho-RS. The experimental area was analyzed during the soybean growing season. As the TVDI data estimated by OLI/TIRS and MODIS sensors were coherent and robust, both sensors can be used in conjunction for agricultural risk monitoring. Its main features are continuous monitoring of large production regions by TVDIMODIS and spatial distribution detailing by TVDIOLI/TIRS in critical periods to water deficit.

Resumo Um dos importantes desafios para construção de sistemas eficazes de monitoramento agrícola é a definição de indicadores que representem a variabilidade espacial e temporal dos principais fatores de risco associados à produção. Neste contexto, se analisou a potencialidade de uso do TVDI (Temperature-Vegetation Dryness Index), obtido a partir dos sensores terrestres e orbitais em áreas de produção de soja no sul do Brasil, na geração dos padrões espaciais e temporais do principal fator de risco, a umidade da superfície, visando sua incorporação em sistemas operacionais de monitoramento agrícola. Utilizou-se imagens dos sensores MODIS Terra e OLI/TIRS Landsat 8, e dados de sensores posicionados na superfície que serviram como referência. A área de estudo abrangeu lavoura de soja, onde foi conduzido o experimento, lavouras de soja próximas e o município de Carazinho/RS. O período de análise foi o período de cultivo da soja. Dada a coerência e robustez do TVDI estimado pelos sensores orbitais, verificou-se que é possível o uso conjugado destes sensores em sistema de monitoramento de risco agrícola. A principal característica é o acompanhamento contínuo da umidade em amplas regiões agrícolas usando TVDIMODIS e detalhamento da distribuição espacial da umidade da superfície com TVDIOLI/TIRS nos períodos críticos à deficiência hídrica.

ABSTRACT: The extension of the area occupied by the inter tussock stratum and tussock stratum in natural pastures is essential for the productive performance of grazing animals. Images obtained from unmanned remote sensors can provide useful information, especially because they have a high spatial resolution. Thus, this study evaluated the performance of the supervised adaptive classification applied to aerial images obtained from an onboard drone camera to map the area covered by tussocks in a natural pasture of the Pampa biome. The study was carried out in a natural pasture area managed since 1986 under different forage allowances, considering treatments of 8, 12, and 16 kg of dry matter per 100 kg live weight (% LW). An aerial image from September 2017, obtained with a Canon S100 camera onboard a drone at an altitude of 120 m, with a spatial resolution of 5 cm, was used. The random forest and support vector machine classifiers were tested associated with specific classification rules. False-color images showed considerable visual similarity in the large patterns of the vegetation distribution and the validation performed with independent samples when compared to the classified images. The tested classifiers were able to measure the area covered by the tussock stratum, which could be an indicator of the quality vegetation in a natural grassland of the Pampa biome.

RESUMO: A quantidade de área ocupada por estrato inferior e superior em pastagens naturais tem grande importância sobre o desempenho produtivo dos animais em pastejo. Imagens obtidas de sensores remotos não tripulados podem fornecer informações úteis, especialmente por possuírem alta resolução espacial. O objetivo deste trabalho foi avaliar o desempenho de classificação supervisionada adaptativa aplicada a imagem aérea obtida por câmera a bordo de drone, no mapeamento da área coberta por touceiras em pastagem natural do bioma Pampa. O estudo foi realizado em área de pastagem natural, manejada desde 1986 sob diferentes ofertas de forragem, tendo sido considerados os tratamentos 8, 12 e 16 kg de matéria seca por 100 kg de peso vivo (% PV). Foi utilizada uma imagem aérea, de setembro de 2017, obtida com uma câmera Canon S100, a bordo de um drone a 120 m de altitude, correspondendo a resolução espacial de 5 cm. Foram testados dois classificadores, Random Forest e Support Vector Machine associados a regras específicas de classificação. As imagens de falsa cor, quando comparadas às imagens classificadas, apresentaram considerável semelhança visual nos grandes padrões de distribuição da vegetação, bem como na validação feita com amostras independentes. Os classificadores testados foram capazes de mensurar a área coberta por estrato superior, podendo ser um indicador da qualidade da vegetação, em pastagem natural do bioma Pampa.

Resumo O objetivo deste trabalho foi avaliar o uso dos dados de umidade da superfície gerados pelo índice “temperature-vegetation dryness index” (TVDI), obtido de sensores posicionados na superfície de uma área agrícola de soja (Glycine max). Os limites seco e úmido do índice foram obtidos com uso de imagens Landsat-8 que abrangem a região em torno da lavoura. Para avaliar a qualidade e a coerência do TVDI, foi realizada análise de correlação entre o TVDI, o “normalized difference vegetation index” (NDVI), a temperatura da superfície (TS), e as variáveis usualmente utilizadas para expressar a umidade da superfície. O TVDI mostrou correlação significativa com umidade do solo, armazenamento de água no solo, deficit hídrico, evapotranspiração real e relação evapotranspiração real/evapotranspiração potencial. O deslocamento dos dados radiométricos medidos na superfície (NDVI e TS) dentro do triângulo evaporativo, ajustado com dados orbitais, descreve de forma consistente a variabilidade das condições hídricas no período de estudo, sendo uma ferramenta para apoio à tomada de decisões no manejo de culturas.

Abstract The objective of this work was to evaluate the use of the surface moisture data generated by the temperature-vegetation dryness index (TVDI), obtained from sensors positioned on the surface of a soybean (Glycine max) agricultural field. The dry and wet limits of the index were obtained using Landsat-8 images covering the region around the crop. To assess the quality and consistency of the TVDI, a correlation analysis was carried out between the TVDI, the normalized difference vegetation index (NDVI), surface temperature (TS), and the variables that are usually used to express surface moisture. The TVDI showed a significant correlation with soil moisture, water storage in the soil, water deficit, real evapotranspiration, and the real evapotranspiration/potential evapotranspiration ratio. The displacement of radiometric data measured on the surface (NDVI and TS) within the evaporative triangle, adjusted with orbital data, consistently describes the variability of water conditions during the study period, being a tool to support decision-making in crop management.

RESUMO A Produtividade Primária Líquida (NPP) pode ser utilizada para quantificar o papel relativo do clima e das atividades humanas na dinâmica da vegetação. Dada sua importância, muitos modelos de estimativa de NPP foram desenvolvidos, mas parte dos dados requeridos, ainda são limitados. Diante disso, este trabalho teve como objetivo estimar a NPP potencial e real testando diferentes abordagens quanto a fonte dos dados e métodos de estimativa, assim como, avaliar a apropriação humana da NPP em uma lavoura de soja cultivada no Sul do Brasil. Para isso, foram obtidos dados de NPP medida a campo em cultivo de soja em Carazinho, no Rio Grande do Sul, e comparados às estimativas de NPP potencial e NPP real, utilizando o modelo CASA e dados do ERA-Interim. Posteriormente, com a NPP potencial e real foram avaliadas as mudanças causadas pelo uso da terra em função das atividades agrícolas, através da Apropriação Humana da NPP (HANPP). Verificou-se que não houve diferença significativa associadas às fontes de dados utilizadas, evidenciando que os dados meteorológicos de reanálise do ERA-Interim podem ser utilizados para esse fim. As estimativas da NPP real pelo modelo CASA foram consistentes com elevada associação aos dados medidos a campo. A HANPP por meio de apenas um cultivo anual de soja, representou 29% do potencial de NPP na região. Isso indica que há potencial para elevar a intensificação com cultivos anuais na região.

ABSTRACT Net primary productivity (NPP) can be used to quantify the relative role of climate and human activities in vegetation dynamics. Given its importance, many NPP estimation models have been developed, but some of the required data is still limited. Therefore, this study aimed to estimate the potential and actual NPP by testing different approaches regarding the data source and estimation methods and evaluate the human appropriation of NPP (HANPP) in a soybean field cultivated in southern Brazil. For this, data were obtained from field-measured NPP in soybean cultivation in Carazinho, Rio Grande do Sul, Brazil, and compared to the potential and actual NPP estimations using the CASA model and data from ERA-Interim. Subsequently, land use changes due to agricultural activities were evaluated from the potential and actual NPP through HANPP. No significant difference was observed associated with the used data sources, showing that the ERA-Interim reanalysis weather data can be employed for this purpose. The actual NPP estimations by the CASA model were consistent with a high association with the data measured in the field. HANPP, through only one annual soybean cultivation, represented 29% of the potential NPP in the region. It indicates the potential to increase intensification with annual crops in the region.

ABSTRACT As an indicator of surface moisture, TVDI has great potential for application in agriculture in different types of climates, but the best results have been obtained in arid climates, given the presence of areas with contrasting water conditions. Some studies under subtropical climate conditions have shown good results in the use of TVDI as an indicator of surface moisture but some uncertainties still need to be better understood. This study aimed to evaluate the coherence of temperature-vegetation dryness index (TVDI) values, obtained from sensors installed on the surface, compared to data obtained using orbital images in an agricultural area in southern Brazil under humid subtropical climate conditions. The analyses employed normalized difference vegetation index (NDVI) and surface temperature (TS) data obtained through sensors installed on the surface, with continuous monitoring, as well as Landsat OLI/TIRS images, covering the period of the soybean cycle. The TVDI values, obtained from NDVI and TS radiometric sensors installed in the field, indicated consistency of using Landsat-OLI/TIRS orbital images to map the surface moisture condition, representing the spatial and temporal variations of the water condition of soybean cultivations under subtropical climate conditions.

Abstract This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change.

Resumo O objetivo do estudo foi analisar a variabilidade no TVDI (Temperature-Vegetation Dryness Index) obtido de sensores orbitais com resoluções distintas, em região agrícola no sul do Brasil. Utilizou-se três imagens OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) do satélite Landsat 8, e 12 imagens MODIS (Moderate Resolution Imaging Spectroradiometer) do satélite Terra. Dados coletados em campo serviram como base para classificação de imagem OLI/TIRS e mapeamento de áreas de arroz, soja, campos naturais, mata ciliar e solo exposto. O TVDI foi obtido por duas parametrizações em períodos distintos, utilizando as dispersões entre Temperatura de Superfície (TS) e NDVI (Normalized Difference Vegetation Index). O TVDI obtido para ambos sensores apresentou padrão similar possibilitando diferenciar os alvos. Na média de todas as datas e classes, o TVDI obtido das imagens MODIS foi superior em 0,128 unidades ao TVDI obtido com o OLI/TIRS. Quando utilizado OLI/TIRS há um melhor detalhamento espacial das condições hídricas, mas com menor repetição ao longo da safra; já utilizando o TVDI-MODIS é possível monitorar as condições hídricas em escala regional, com menor detalhamento espacial, mas com maior repetitividade no tempo. O TVDI estimado pelos sensores OLI/TIRS e MODIS, pode ser utilizado de forma conjunta, trazendo informações complementares.

Abstract The aim of the study was to analyze the variability in the TVDI (Temperature-Vegetation Dryness Index) obtained from orbital sensors with distinct resolution in an agricultural region in southern Brazil. Three images of the Landsat 8 satellite OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) and 12 of Terra’s MODIS (Moderate Resolution Imaging Spectroradiometer), were used. Data collected in the field served as a basis for OLI/TIRS image classification and mapping of rice, soybean, grasslands, gallery forest and bare soil areas. The TVDI obtained by two parameterizations at different periods was evaluated, using the dispersions between Surface Temperature (TS) and NDVI (Normalized Difference Vegetation Index). The TVDI obtained for both sensors presented a similar pattern allowing differentiation of the targets. The average of all dates and classes, TVDI obtained from MODIS was 0.128 units higher than the obtained with the OLI/TIRS. When used OLI/TIRS there is a better detail in the representation of the moisture, but with less repetition throughout the crop cycle. Using TVDI-MODIS, it is possible to monitor moisture conditions on a regional scale, with less spatial detail, but in a continuous way over time. The TVDI estimated by the OLI/TIRS and MODIS can be used together, providing complementary information.

RESUMO Considerando que o estudo da fenologia vegetal permite estabelecer relações entre o padrão fenológico da vegetação e as alterações causadas pela variabilidade climática, o objetivo deste trabalho foi obter métricas fenológicas para as tipologias de vegetação de pastagens predominantes no Rio Grande do Sul (latitudes 27º 05’ e 33º 45’ S e longitudes 49° 43’ e 57º 39’ W) e avaliar o padrão de distribuição espaço-temporal destas métricas sob influência da variabilidade climática subtropical. A obtenção de 11 métricas fenológicas foi realizada com base na série temporal do EVI (Enhanced Vegetation Index) do sensor MODIS (Moderate Resolution Imaging Spectroradiometer), para o período de 2001 a 2014, por meio do programa Timesat. Identificou-se a presença de dois grupos espaciais de tipologias campestres no Estado do Rio Grande do Sul, um na região centro-sul e outro na região nordeste, ao longo do litoral e no oeste do Estado. Observou-se ainda que o padrão fenológico da vegetação de pastagens do Rio Grande do Sul é controlado pela sazonalidade da vegetação associada, principalmente, à variação da temperatura do ar.

ABSTRACT Considering that plant phenology studies allow establishing relationships between phenological patterns of vegetation and changes caused by climate variability, the aim of this study was to obtain phenological metrics for the predominant grassland typologies in Rio Grande do Sul State, Brazil (latitudes 27º 05’ and 33º 45’ S and longitudes 49° 43’ and 57º 39’ W) and to evaluate the spatial-temporal distribution pattern of these metrics under the influence of the subtropical climate variability. The phenological metrics were obtained based on the time series of the EVI (Enhanced Vegetation Index), of the sensor MODIS (Moderate Resolution Imaging Spectroradiometer), for the period from 2001 to 2014, through the Timesat program. Eleven phenological metrics were extracted, identifying the presence of two spatial distribution patterns of grass-dominated typologies in the Rio Grande do Sul state, Brazil, one located in the south-central region and the other located in the northeast, along the coast and in the western portion of the state. In addition, it was also observed that the phenological pattern of the grassland vegetation of the state of Rio Grande do Sul, Brazil, is controlled by the seasonality of vegetation, mainly associated with the variations in air temperature.

RESUMO Objetivou-se neste estudo verificar a aplicabilidade do uso de séries temporais do sensor MODIS-EVI para o mapeamento do uso do solo e cobertura vegetal no Bioma Pampa no Estado do Rio Grande do Sul. O período de estudo compreendeu os meses de junho de 2013 até junho de 2014. Os procedimentos incluíram a utilização de imagens do Sensor MODIS, dados altimétricos e imagens noturnas, associados ao classificador hierárquico por árvore de decisão, construído utilizando o algoritmo C4.5. A abordagem proposta tem origem na consideração de que a área de estudo apresenta características variadas e, por isso, deve ser tratada simultaneamente por classificadores diferentes e intuitivos, o que justifica a escolha da árvore de decisão. Para avaliar os resultados, foram usados dados de referências extraídos de imagens do satélite Landsat 8-OLI e dados do IBGE. Na classificação utilizando a série temporal MODIS, foi encontrada uma exatidão global de 90,09% e o índice Kappa de 0,8853. Com relação aos dados de referência do IBGE, a classe soja obteve um coeficiente de correlação de 0,94, a classe arroz 0,97 e a classe Silvicultura obteve o menor valor com 0,78. As maiores similaridades espectrais foram encontradas nas classes com cobertura vegetal, como campo, floresta e silvicultura. Portanto, com o uso de dados multitemporais do sensor MODIS, combinado ao uso de dados altimétricos e imagens noturnas, é possível a geração de um mapa de uso do solo e cobertura vegetal para o bioma Pampa com exatidão aceitável, considerando as limitações da resolução do sensor MODIS.

ABSTRACT This study aimed to verify the applicability of using MODIS-EVI sensor time series for land use and vegetation cover mapping in the Pampa biome, Rio Grande do Sul state, Brazil. The study period comprised the months from June 2013 to June 2014. The procedures included the use of MODIS Sensor images, altimetric data and nighttime images, associated with a hierarchical decision tree classifier, constructed using the C4.5 algorithm. The proposed approach stems from the consideration that the study area has varying characteristics and, therefore, should be treated simultaneously by different and intuitive classifiers, which justifies the choice of decision tree. To evaluate the results, reference data acquired from Landsat 8-OLI satellite images and IBGE data were used. The classification using the MODIS time series showed a global accuracy of 90.09% and Kappa index of 0.8885. When compared to the IBGE reference data, the Soybean class obtained a correlation coefficient of 0.94, the rice class obtained 0.97 and the silviculture class obtained the lowest value, 0.78. The highest spectral similarities were found in the vegetation cover classes, such as grassland, forest and silviculture. Therefore, with the use of multitemporal data from the MODIS sensor, combined with the use of altimetric data and nighttime images, it is possible to generate a land use and vegetation cover map for the Pampa biome with an acceptable accuracy, considering the MODIS sensor resolution limitations.

ABSTRACT In vineyards, the monitoring of leaf area is used to guide management practices in order to produce grapes with quality and enological potential. Remote sensing has been used to estimate leaf area by means of vegetation indices. This work aimed at determining the temporal evolution of the Normalized Difference Vegetation Index (NDVI) while associating it with leaf area of vineyards in the Serra Gaúcha region, Brazil. To this end, between 10 and 20 plants were evaluated monthly (September to May) in three vegetative seasons. NDVI data were obtained using the remote sensor Greenseeker while the following leaf area related parameters were obtained non-destructively: unit leaf area (ULA), average number of leaves per branch (NLB), plant leaf area (PLA), leaf area index (LAI) and chlorophyll index (CI). Data correlation was assessed by joint temporal analysis and Pearson correlations (p < 0.05). The results indicated that NDVI variation over time is coherent with the leaf area dynamics and expresses biomass variations due to phenology and management actions. The NDVI evolution pattern is described by a quadratic regression equation: the index increases rapidly at first (September to November), followed by a relative stabilization (December to February), and decreases in the final stage (March to May). The correlations were higher in the initial and final stages of the cycle, especially for NLB (r = 0.73 and 0.77), PLA (r = 0.60 and 0.61) and CI (r = 0.48 and 0.84), which are promising parameters for characterizing leaf area in vineyards by remote sensing.

RESUMO Objetivou-se nesse trabalho caracterizar climaticamente a precipitação pluvial, analisar a influência de eventos El Niño Oscilação Sul (ENOS) e a tendência temporal na série de dados 1956-2015 de Veranópolis, Rio Grande do Sul, Brasil. Para caracterização climática foram definidas estatísticas descritivas da precipitação pluvial anual, mensal e estacional. Para identificação de diferenças entre estações e influência de eventos ENOS, os dados foram submetidos à análise de variância e teste de Duncan. Tendências foram avaliadas pelo teste de Mann Kendall. Os resultados indicaram que a precipitação pluvial anual média é de 1.683 mm e a mensal 140 mm, variando entre 109 (maio) e 182 mm (setembro). Há elevada variabilidade interanual na precipitação pluvial anual (desvio-padrão de 327 mm), mensal (60-100 mm) e estacional (124-183 mm), a qual deve ser considerada especialmente em sistemas agrícolas não irrigados nos quais a chuva é o principal mecanismo de aporte de água às plantas. Embora a precipitação pluvial média do outono (346 mm) seja inferior à das demais estações, os percentuais médios em relação ao total anual são semelhantes (21-28%) e a chuva é bem distribuída nas estações. Diferenças entre eventos ENOS ocorreram na primavera, sendo que chove menos em eventos La Niña (385 mm) do que em El Niño (549 mm) e neutros (481 mm). Há tendência climática de aumento da precipitação pluvial anual em 6,3 mm ao ano (p < 0,01), assim como na primavera (2,5 mm) e inverno (1,9 mm) (p < 0,10) na série analisada.

ABSTRACT The objective of this study was to characterize the rainfall climatology in Veranópolis, Rio Grande do Sul, Brazil, through analyses of means, variabilities related to El Niño Southern Oscillation (ENSO), and temporal trends, using a 60-year data series (1956-2015). Descriptive statistics of annual, monthly and seasonal rainfall were used to characterize the rainfall climatology. The differences between seasons, and influence of ENSO were evaluated using analysis of variance and the Duncan’s test. Rainfall trends were evaluated by the Mann Kendall test. The local average annual rainfall is 1,683 mm and the average monthly rainfall is 140 mm, varying from 109 (May) to 182 mm (September). The annual rainfall has high interannual (standard deviation of 327 mm), monthly (60-100 mm) and seasonal (124-183 mm) variabilities, which should be considered in non-irrigated agricultural systems using rainfall as the main source of water supply to plants. Although autumn presents lower average rainfall (346 mm) than the other seasons, its average percentages were similar to the total annual rainfall (21-28%), and the rainfalls are well-distributed in the seasons. Differences between ENSO events occurred in the spring; La Niña years showed lower rainfall (385 mm) than El Niño (549 mm) and neutral (481 mm) years. The annual rainfall tended to increase by 6.3 mm per year (p < 0.01), with increases of 2.5 mm in spring and 1.9 mm in winter (p < 0.10) in the period analyzed.

ABSTRACT: This study aimed to characterize the average seasonal pattern of the vegetation in southern grassland in Brazil, and the variability found in the time series of vegetation indices. It also sought to identify similarities in the seasonal pattern of different grassland typologies. Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Feb to Dec, 2000 to 2014 were analyzed for ten regions. The grassland typologies studied showed EVI and NDVI profiles consistent with the seasonal dynamics of grassland vegetation under the influence of a subtropical climate, with highest values in the indices during the warm seasons of the year (spring and summer) and lowest in the colder seasons (autumn and winter). Considering the values of EVI and NDVI, grassland typologies were allocated to four groups with similar temporal profiles. Among the groups formed from the EVI index it is possible to identify differences between grassland typologies during the autumn and winter, while the NDVI showed differences only in winter as compared to the other seasons.

ABSTRACT This study evaluated the adequacy of the One-Source Energy Balance (OSEB) and Surface Energy Balance Algorithm for Land (SEBAL) models to estimate evapotranspiration in grain growing areas with humid subtropical climate in Rio Grande do Sul, southern Brazil. The dataset was obtained from a micrometeorological station (Eddy Covariance) and MODIS (Moderate Resolution Imaging Spectroradiometer) products during 84 observation days between 2009 and 2011. The OSEB and SEBAL models were used to estimate the partition of net radiation (Rn) into the components latent heat flux (LE), sensible heat flux (H), and ground heat flux (G) estimated from the MODIS images while the experimental data measured in situ were used to compare the results. Analyses indicated that the Energy Balance (EB) components were estimated from the MODIS images satisfactorily despite the spatial resolution limitations. Furthermore, the SEBAL model estimated the EB components satisfactorily only for summer crops. There are uncertainties associated with determining the hot and cold pixels due to the region humid subtropical climate and the spatial resolution of the sensor used in the other periods. The OSEB model has the lowest errors and the most adequate partitioning of the EB components throughout the year, and therefore, it is the most suitable for the climatic conditions of Rio Grande do Sul.

Resumo: O objetivo deste trabalho foi localizar e quantificar, por meio de geotecnologias, áreas atualmente utilizadas em agricultura anual no bioma Cerrado do Brasil Central, localizadas no interior dos contornos definidos pela região homogênea de adaptação de cultivares de trigo 4 e que apresentem aptidão favorável para o cultivo de trigo (Triticum aestivum) no regime sequeiro. Foram realizados cruzamentos dos seguintes planos de informação: uso e cobertura das terras do bioma Cerrado, modelo digital de elevação e índice de satisfação de necessidades de água para trigo no bioma Cerrado. Também foram considerados níveis de impacto de estresse hídrico (baixo, moderado e elevado), níveis de risco (20, 30 e 40%), capacidades de armazenamento de água disponível (CAD) no solo (35, 55 e 75 mm), ciclos de cultivares (105, 115 e 125 dias) e semeaduras (em fevereiro e março). Há maior área favorável para semeaduras no começo de fevereiro, e o grupo I de cultivares (105 dias) apresentou a maior área favorável. Acima de 800 m de altitude, 2,7 milhões de hectares foram classificados como favoráveis para a melhor combinação de fatores, isto é, semeadura em 5 de fevereiro, CAD de 75 mm, ciclo de 105 dias, nível de risco de 20%, e impactos baixo e moderado.

Abstract: The objective of this work was to locate and quantify, through geotechnologies, areas that are currently used for annual agriculture in the Cerrado biome of Central Brazil, located within the boundaries defined by the homogeneous region of adaptation of wheat cultivars 4 and that present favorable conditions for rainfed wheat (Triticum aestivum) cultivation. The following information layers were crossed: use and coverage of the Cerrado biome, digital elevation model, and water requirement satisfaction index for wheat in the Cerrado biome. In addition, different levels of water stress (low, moderate, and high), risk levels (20, 30, and 40%), available soil water capacities (ASWCs) (35, 55, and 75 mm), cultivar cycles (105, 115, and 125 days), and sowing dates (in February and March) were also considered. A greater favorable area was observed for sowing in early February, and group I of cultivars (105 days) presented the greatest favorable area. Above 800 m altitude, 2.7 million hectares were classified as favorable for the best combination of factors, i.e., sowing on February 5, ASWC of 75 mm, 105-day cycle, 20% risk level, and low and moderate impacts.

ABSTRACT The present study aimed to characterize the dynamics of NDVI and meteorological conditions, relating both to the annual dynamics of biomass accumulation in natural pastures of the Pampa biome as a way of subsidizing growth modeling. Forage accumulation rate data from a long-term experiment, NDVI data from the MODIS images, and meteorological data measured at the surface were used. We verify that the agrometeorological element associated to the accumulation of forage in the natural grasslands is different according to the season, which is typical of the subtropical climate. Winter is the critical season for livestock production due to the lower forage accumulation rate and lower values of NDVI, conditioned by the decrease of solar radiation and air temperature. In the summer, the limiting factor to forage accumulation is the hydric condition. It was also verified that the variability in the growth of grasslands can be associated with the ENSO phenomenon, being the El Niño favorable and the La Niña unfavorable, especially in the spring-summer period. Considering the verified associations, spectral indices combined with agrometeorological elements are recommended to the adjustment of models of forage accumulation in the Pampa biome natural grasslands.