-Two cultivars of Brachiaria brizantha (Hochst ex. A. Rich) Stapf. (Syn. Urochloa) were evaluated for their adaptation to water deficit and the stress response mechanisms in a greenhouse experiment. The experimental design was in completely randomized blocks with a 2 × 2 × 4 factorial arrangement. The Marandu and BRS Piatã cultivars were evaluated under two water availability conditions, with or without water restriction. The harvests were carried out 0, 7, 14 and 28 days after the start of water restriction. For both cultivars, the water deficit stress caused a reduction in shoot biomass and leaf area and an increase in the percentage of roots in the deeper soil layers. The B. brizantha cv. Marandu reached critical levels of leaf water potential in a shorter period of water restriction than did the B. brizantha cv. BRS Piatã. The osmoregulation and deepening of the root system are mechanisms of adaptation to water stress observed in both Marandu and BRS Piatã cultivars. Besides that, the Marandu cultivar also increases its leaf senescence and, consequentially, decreases its leaf area, as a response to water deficit.
In this study, the microclimate changes in silvopastoral systems are quantified at different time and spatial scales and compared with single-pasture systems. The study was conducted in a silvopastoral system in São Carlos, Brazil (22°01' South and 47°53' West). The silvopastoral system consisted of grass pastures [Urochloa (sin. Brachiaria) decumbens cv. Basilik] lined with strips of native trees spaced at 17 m. Continuous microclimate measurements (air temperature, relative humidity, wind speed, and incident photosynthetically active radiation) were carried out from September 2010 to September 2012 at two positions of the silvopastoral system (2.0 and 8.5 m from the rows of trees) and at a pasture under full sunlight, located in an adjacent area. The soil moisture was monitored weekly from 24 March 2010 to 1 April 2012 at distances of 0.0, 2.0, 4.75, and 8.5 m from the rows of trees. The rows of native trees reduced wind speeds (46% reduction) and changed the photosynthetically active radiation incidence pattern on the pasture (up to 40% reduction of incidence at the point nearest to the trees). The combined action of these factors promoted thermal and air humidity changes on the pasture at the different points measured. With respect to soil moisture, there was greater soil moisture removal at the sampling points near the rows of native trees under the silvopastoral system, mainly due to enhanced exploration by tree roots at greater depths in dry spells or early dry periods, when compared with the midpoints between the rows.
RESUMO O experimento foi desenvolvido em casa de vegetação na Embrapa Pecuária Sudeste, utilizando acessos de
Tropical grasses are economically important for cattle production in Brazil, and accurate simulation models for tropical pastures can benefit forage researchers and farm managers by improving tropical forage production systems. This research calibrated and validated four modeling approaches of contrasting complexity to simulate mass production of Mombaça Guinea grass (Panicum maximum Jacq.). The models included three empirical agro‐climatic models (i.e., using cumulative degree days, photothermal units, and a climatic growth index) and a biophysical simulation model, Agricultural Production Systems Simulator (APSIM)‐Growth. Data sets for calibration and independent validation included frequent records of aboveground dry matter production during the 2005–2006 and 2010–2011 growing seasons from three trials. All models performed well during calibration (R2 = 0.78–0.86; coefficient of variation = 26–32.1%). During model validation, the R2 varied between 0.69 and 0.78, the agreement index was between 0.88 and 0.93, the coefficient of variation between 37.6 and 50.2%, and the mean bias error was between 6 and 470 kg ha−1. Even though all models were in agreement between simulated and observed results, APSIM‐Growth was able to simulate Guinea grass production across broader climatic, soil, and management (e.g., N fertilization) conditions.
Resumo -O objetivo deste estudo foi testar modelos empíricos de regressão linear, para a predição do acúmulo de matéria seca (TAMS) de Urochloa brizantha cv. Marandu, em função de variáveis agrometeorológicas. Para gerar os modelos, foi utilizada a taxa média de acúmulo de matéria seca, em condições de sequeiro, entre 1998 e 2002. As variáveis avaliadas foram: temperaturas mínima, máxima e média, radiação global (Rg), graus-dia, evapotranspiração real (ETR) e potencial (ETP) obtidas a partir do balanço hídrico, unidades fototérmicas (UF) e índice climático de crescimento (ICC RQMR, 17,85; CIA, 236,9). A correção das variáveis agrometeorológicas pela relação entre evapotranspiração real e potencial (ETR/ETP), em geral, melhora a predição da TAMS pelos modelos.Termos para indexação: Brachiaria brizantha, Urochloa brizantha, regressão linear, regressão multivariada. Empirical models to estimate the accumulation of dry matter in Marandu palisade grass using agrometeorological variablesAbstract -The objective of this work was to test empirical linear regression models, to predict dry matter accumulation rates (DMAR) of Urochloa brizantha cv. Marandu, using agrometeorological variables. To generate the models, the average dry matter accumulation under rainfed conditions, between 1998 and 2002, was used. The evaluated variables were: minimum, maximum and average temperatures, global radiation (GR), degree-days, actual (AET) and potential evapotranspiration (PET) obtained from the water balance, photothermal units (PU) and the climatic growth index (CGI). Except for the PU, the univariate and multivariate regressions showed good predictive ability. The best results were for the multivariate regression, with T mín , GR and AET: R 2 , 0.84; root mean square residual (RMSR), 14.72; and Akaike's information criterium (AIC), 222.5. In the univariate regression, the following variables stood out: corrected degree-days (R 2 , 0.75; RMSR, 17.84; CIA, 242.6), corrected minimum temperature (R 2 , 0.75; RMSR, 17.82; AIC, 244.1); and CGI (R 2 , 0.74; RMSR, 17.85; AIC, 236.9). The correction of the agrometeorological variables using the relation between real and potential evapotranspiration (AET/PET) enhances, in general, the model prediction of DMAR.
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