Índice climático de crescimento para gramíneas forrageiras no Estado de São Paulo

Mendonça

et al. 2012

Pesq. agropec. bras.

-The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry-field conditions in São Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree-days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in São Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree-days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.Index terms: Panicum maximum, climatic growth index, degree-days, evapotranspiration, modeling. Produção de matéria seca de capim-tanzânia em função de variáveis agrometeorológicasResumo -O objetivo deste trabalho foi desenvolver e validar modelos de regressão linear para a estimativa de produção de matéria seca de capim-tanzânia (Megathyrsus maximus, cultivar Tanzania) em função de variáveis agrometeorológicas. Para tanto, dados de períodos de crescimento da forragem entre 2000 e 2005, em condições de sequeiro em São Carlos, SP, foram correlacionados aos seguintes parâmetros climáticos: temperaturas mínima e média, graus-dia, evapotranspiração potencial e atual. Foram realizadas regressões lineares simples entre as variáveis agrometeorológicas (independentes) e a taxa média de acúmulo (dependente). As estimativas foram validadas com dados independentes obtidos em São Carlos e Piracicaba, SP. Os melhores resultados estatísticos observados no desenvolvimento e na validação dos modelos foram obtidos para parâmetros agrometeorológicos que levem em consideração o efeito térmico e hídrico conjuntamente, como evapotranspiração real, acúmulo de graus-dia corrigido pela disponibilidade hídrica e índice climático de crescimento, baseado na temperatura média, na radiação solar e na disponibilidade hídrica. Essas variáveis podem ser utilizadas em simulações e modelos para prever a produção do capim-tanzânia.Termos para indexação: Panicum maximum, índice climático de crescimento, graus-dia, evapotranspiração, modelagem.

Section: Resultsmentioning

confidence: 99%

Dry matter production of Tanzania grass as a function of agrometeorological variables

Mendonça

et al. 2012

Pesq. agropec. bras.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil

Cruz²,

et al. 2013

Int J Biometeorol

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

Simulating tropical forage growth and biomass accumulation: an overview of model development and application

Andrade

Grass and Forage Science

et al. 2015

Crop models can aid the synthesis and application of knowledge, planning of experiments and forecasting in agricultural systems. Few studies have reviewed the uses and applications of these models for tropical forages. The purpose of this study was to review the information available in this scientific area, highlighting the main models, their applications and limitations. Several empirical models have been developed to predict the growth and biomass accumulation of tropical forages, especially for the genera Cynodon, Paspalum, Panicum and Brachiaria. Their application, however, is often location or region specific. The adaptation of mechanistic models to accurately predict biomass accumulation in tropical grasses is still limited. Recent advances have been made on the plot‐scale and farm‐scale process‐based models ALMANAC, CROPGRO Perennial Forage and agricultural production systems simulator (APSIM), with promising results. In addition, global‐scale process‐based models, such as the Century Agroecosystem Model and the Orchidee Grassland Management Model, have been tested for tropical grassland areas. A greater number of region‐specific calibrations of empirical models can enhance their use, and improved databases and model parameterizations for a wide range of tropical grasses will enable the continuous improvement of mechanistic models.