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Biochar is considered as a soil amendment for enhancing crop productivity. However, limited information is available on the residual effect of biochar application on rice grain yield and yield attributes. In this study, a fixed field experiment was conducted in a double rice-cropping system from 2017 to 2019. The dynamics of rice grain yield and yield attributes were monitored in the six growing seasons with 0, 20, and 40 t/ha biochar application. The results showed that the averaged grain yields in the first four seasons were increased by 2.56-16.84% and 6.15-10.77% with 20 and 40 t/ha biochar application. The trend of increased grain yield in rice with biochar application during the first seasons was mainly attributable to an increase in total biomass, panicles per m2 and spikelets per panicle. Nonetheless, the grain yields in the sixth season were not influenced by biochar addition due to decreases in panicles per m2 and spikelets per panicle. Thus, it can be seen that the positive effects of biochar application on rice yield and yield attributes depend on the duration of biochar application.

Abstract Patterns in, and the underlying dynamics of, species co‐occurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterizations have been published that could be used with the same fundamental modeling framework that accounts for imperfect detection, although each parameterization has different advantages and disadvantages. We propose a parameterization based on log‐linear modeling that does not require a species hierarchy to be defined (in terms of dominance) and enables a numerically robust approach for estimating covariate effects. Conceptually, the parameterization is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, “symmetric,” interpretations of parameter estimates. The parameterization can be applied to many species, in either a maximum likelihood or Bayesian estimation framework. We illustrate the method using camera‐trapping data collected on three mesocarnivore species in South Texas.

Patterns in, and the underlying dynamics of, species co‐occurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterizations have been published that could be used with the same fundamental modeling framework that accounts for imperfect detection, although each parameterization has different advantages and disadvantages.We propose a parameterization based on log‐linear modeling that does not require a species hierarchy to be defined (in terms of dominance) and enables a numerically robust approach for estimating covariate effects.Conceptually, the parameterization is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, "symmetric," interpretations of parameter estimates.The parameterization can be applied to many species, in either a maximum likelihood or Bayesian estimation framework. We illustrate the method using camera‐trapping data collected on three mesocarnivore species in South Texas. [ABSTRACT FROM AUTHOR]