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Sirvi Autor "Dias, D.L.S." järgi

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  • Pisipilt
    Kirje
    Carbohydrate and protein metabolism of marandu grass affected by nitrogen fertilisation and number of cuts
    (Estonian University of Life Sciences, 2023) Seixas, A.A.; Fries, D.D.; Dias, D.L.S.; Santos, I. A.P.S.; Cruz, N.T.; Teixeira, F.A.; Bonomo, P.; Amaral Júnior, F.P.
    Understanding the metabolism of tropical grasses in response to management practises imposed in pastoral environments allows for improvements in the management and use of mineral fertilisers. This study aimed to quantify metabolite content in different plant parts of Marandu grass (Urochloa brizantha), with a specific focus on the influence of nitrogen fertilisation and its effects following successive cuts. The treatments corresponded to four nitrogen (N) rates (0, 75, 150, and 225 kg N ha-1 ) and the number of cuts (one, two and three cuts). The plants were fractionated into leaves, stems, and roots to assess the content of water-soluble carbohydrates (WSC), starch, albumin, globulin, prolamin, and glutelin content. N fertilisation influenced the WSC and starch content in different parts of the plant, varying according to the cuts made. In the leaves and roots, fertilisation reduced the content of WSC and starch with one cut, as these were utilised as energy sources for assimilating the excess nitrogen in the soil. There was an increase in the concentration of all protein groups with nitrogen fertilisation in all parts of the plant with one cut. In plants cut two and three times, N fertilisation led to specific increases and decreases in different parts of the plants as an adaptive strategy for allocating resources as the number of cuts increased. Our results broaden our understanding of carbohydrate and protein metabolism in tropical grasses, thereby providing subsidies for the rational use of nitrogen fertilisers.
  • Pisipilt
    Kirje
    Development of tropical grassland biomass prediction model based on UAV RGB images
    (Estonian University of Life Sciences, 2025) Ramos, B.L.P.; Seixas, A.A.; Nascimento, L.M.G.; Dias, D.L.S.; Amorim, J.M.S.; Lemos, O.L.; Pedreira, M.S.
    The objective of this study is to assess the predictive potential of indices derived from RGB images captured by a camera mounted on a remotely piloted vehicle (RPV) to estimate the fresh and dry forage yield of grasses from the Urochloa genus. The experiment was conducted between December 2021 and January 2023, involving four cultivars of the Urochloa genus (U. brizantha cv. Braúna, U. brizantha cv. Paiaguás, U. hybrid cv. Camello, and U. decumbens cv. Basilisk), with flights conducted at two heights (20 and 100 metres). The values of the Green Leaf Index (GLI) and Digital Vegetation Model (DVM) extracted were correlated with the yields of fresh (FFY), dry forage yield (DFY), dry matter content (DM), and crude protein (CP). The results showed that DVM exhibited greater efficiency in estimating DM and CP at a flight altitude of 20 m. In contrast, GLI proved more efficient in estimating FFY and DFY at 100 m altitude, suggesting the potential for combining DVM and GLI to develop predictive models. The RGB images obtained via RPV have potential for estimating forage productivity and quality, expanding the possibilities of pasture management techniques.