Thermal time in sprinkler-irrigated lowland rice

Abstract

-The objective of this work was to evaluate methods of thermal time calculation and the duration of the development stages of lowland rice (Oryza sativa) irrigated by sprinkling. The experiment was conducted during three growing seasons (2010/2011, 2011/2012, and 2014/2015), with five irrigation water depths, six cultivars, and four replicates. Six methods of thermal time calculation were tested: two using the minimum basal temperature; two using the minimum and optimum temperatures; and two using the minimum, optimum, and maximum basal temperatures. For the thermal time calculation, the crop development cycle was divided into the vegetative, reproductive, and grain-filling phases. The methods that used the three cardinal temperatures showed the lowest coefficients of variation for most of the developmental phases. Both irrigation water depths and rice cultivars affected the thermal time of the development stages. The greater the water availability, the lower the duration of the development cycle. Thermal time values depend on the calculation method.

Thermal Time as a Bioclimatic Index for Development Characterization of Sunflower Cultivars in Southern Brazil

“…According to Souza et al (2016), the thermal time is an attractive and widely used method to represent biological time in plants. Studies on the thermal time were developed in many crops (Bartz et al, 2017;Carvalho et al, 2014;Deprá et al, 2016;Miranda and Junior, 2010;Lucas et al, 2015;Paula et al, 2005;Steinmetz et al, 2017;Tomazetti et al, 2015;Trentin et al, 2008).…”

“…Sentelhas and Ungaro (1998) assessed bioclimatic indexes of sunflower crop and concluded that the thermal time presented the lowest total variability in comparison to other indexes, being deployed to study the phenology of Brazilian sunflower cultivars. Other researches were also developed based on thermal time in sunflower (Lucas et al, 2015, Souza et al, 2016, potato (Paula et al, 2005), watermelon (Trentin et al 2008), maize (Deprá et al, 2016), common bean (Miranda and Junior, 2010), rice (Bartz et al, 2017, Steinmetz et al, 2017, grapevine (Tomazetti et al, 2015), and coffee (Carvalho et al, 2014).…”

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

“…Synthesis of some cardinal limits for temperature on the phenology development in the main crops. These estimates were adapted from Soltani andSinclar (2012), Lago et al (2009), and Bartz et al (2017). .289] * For M1 model, GxE is based on the Kronecker product between an identity environment matrix and the genomic kinship matrix.…”

Enviromics, nonlinear kernels and optimized training sets for a climate-smart genomic prediction of yield plasticity in maize

“…Synthesis of some cardinal limits for temperature on the phenology development in the main crops. These estimates were adapted from Soltani and Sinclar (2012), Lago et al (2009), and Bartz et al (2017). Tabela 3.…”