PPGEA

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https://ri.ufr.edu.br/handle/123456789/81

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Navegando PPGEA por Autor "029.533.101-18"

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    Eficiência de modelos de estimativa via sensoriamento remoto na evapotranspiração e coeficiente de cultura do algodoeiro
    (Universidade Federal de Mato Grosso, 2020-02-27) Moncada, Juan Vicente Liendro; José, Jefferson Vieira; 315.083.978-56; http://lattes.cnpq.br/0180791633456689; Silva, Tonny José Araújo da; 781.203.064-49; http://lattes.cnpq.br/0651075688988405; Silva, Tonny José Araújo da; 781.203.064-49; http://lattes.cnpq.br/0651075688988405; José, Jefferson Vieira; 315.083.978-56; http://lattes.cnpq.br/0180791633456689; Fenner, William; 029.533.101-18; http://lattes.cnpq.br/2509207331637862
    Brazil is the fourth largest global producer of cotton and the second largest exporter of this fiber, in addition to having the second place in terms of productivity. In this world panorama, the State of Mato Grosso (MT) is the number one cotton producer in the country with 66,61% of the total Brazilian production. Thus, the problem of spatial and temporal estimation of water needs for cotton cultivation in extensive agricultural production areas arises. Therefore, the objective of the study was to determine the efficiency of estimation models by means of remote sensing in evapotranspiration (ETc) and crop coefficient (Kc) of cotton (Gossypium sp. L.) during the stages of the plant's phenological cycle. The research was carried out on eight cotton fields located in the upper part of the Rio das Mortes (MT) hydrographic basin using data and information accessible from the Campo Verde and Primavera do Leste meteorological stations, associated with the National Institute of Meteorology (INMET) from Brazil to determine the reference evapotranspiration (ETo) by the FAO PenmanMonteith method in the study area. The surface energy balance algorithms SEBAL (Surface Energy Balance Algorithm for Land) and METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) were implemented using satellite images from the Landsat 8 program. The development of the research took place in a Geographic Information Systems environment, using the capabilities of the free software QGIS 3.6.2 and GRASS 7.6.1, and the EEFlux platform (Earth Engine Evapotranspiration Flux) version 0.10.10 of the Google Earth Engine system. The algorithm estimates were compared with determinations made by the FAO 56 method, by simple differences in the case of Kc, and through statistical analysis of simple linear regression for ETc. The results of the research show that in the set of fields analyzed, the SEBAL model reached an average daily ETa close to 5,61 and 3,21 mm d-1, in the intermediate and final stages of the cotton phenological cycle, with an overall efficiency around 67%. The average Kc of the algorithm was close to 1,27 and 0,73 in the intermediate and final stages, with global performances of approximately 92 and 96% respectively. The average total water consumption was 775,43 mm. The model showed an absence of data and information in the initial phase of the cycle, due to the occurrence of the rainy season in the study area. Regarding the METRIC model, the results indicate that it reached an average daily ETa close to 4,14 (initial stage); 3,68 (development stage); 3,28 (intermediate stage) and 2,86 mm d-1 (final stage), with overall efficiency around 20%. The average Kc of the algorithm was close to 0,89 (initial phase); 0,83 (intermediate phase) and 0,62 (final phase), with overall performances of approximately 91; 73 and 100% respectively. The average of total water consumption was 567,64 mm. In general, the SEBAL model surpassed the METRIC EEFlux model in efficiency, in the ETc and Kc estimates of the cotton in the study area, when compared with the FAO 56 method.
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    Parametrização do modelo AquaCrop e simulação da transpiração e produtividade do algodoeiro sob lâminas de irrigação e nitrogênio
    (Universidade Federal de Mato Grosso, 2021-08-31) Fernandes, Werlen de Souza; Duarte, Thiago Franco; 011.291.431.47; http://lattes.cnpq.br/7076042826792327; Duarte, Thiago Franco; 011.291.431.47; http://lattes.cnpq.br/7076042826792327; Silva, Tonny José Araújo da; 781.203.064-49; http://lattes.cnpq.br/0651075688988405; Fenner, William; 029.533.101-18; http://lattes.cnpq.br/2509207331637862
    The cotton plant Gossypium L. is an agricultural species of great economic and social importance for Brazil, whose demand for technologies that enhance productivity combined with sustainability, should be constantly sought. Productivity simulation models, such as AquaCrop, allow the determination of the response of the crop as a function of variation in water availability and nitrogen fertilization. Thus, this work aimed to parameterize the AquaCrop model to evaluate the yield of cotton as a function of water and nitrogen levels. The experiment was conducted in the experimental area of the Federal University of Rondonópolis. The soil is classified as dystrophic Red Latosol and the cultivar used was the IMA5801B2RF. Irrigation was by drip system with irrigation rates of 30%, 90%, and 150% of crop evapotranspiration. Nitrogen was applied via fertigation at doses of 20, 110, and 200% of the recommended dose for cotton. The model input parameters determined locally were soil physical parameters: field capacity (10 kPa), permanent wilting point (1500 kPa), saturated hydraulic conductivity (constant load permeameter); vegetative parameters: canopy cover (%); duration of phenological stages (days); maximum depth of the root system (m); meteorological parameters: maximum and minimum air temperature (ºC), relative humidity (%), wind speed (m s-1 ), global solar radiation (MJ m2 d -1 ). After parameterization, the model was calibrated to simulate the effect of nutrient (N) stress. Seed cotton yields were 3495 kg ha-1 (ET90N110), 2027 kg ha-1 (ET90N20), and 2075 kg ha-1 (ET30N110). Regarding the simulation, it was observed that the simulation of the productive variables, biomass, and seed cotton, by the AquaCrop model were higher than the measured data. In general, the largest errors occur for the simulation of treatments with water or nutrient stress. Despite this, the statistical parameters R2 , d and c, were greater than 0.90, which classifies the model performance as "optimal".