Analysis of rhizobacterial community associated with the occurrence of Ganoderma basal stem rot disease in oil palm by Illumina next-generation sequencing.

The fungus Ganoderma boninense is a causal pathogen of basal stem rot, a serious disease of oil palm plantation systems. As previously observed, some oil palm trees show no appearance of disease symptoms (asymptomatic oil palm), although they have grown close to a tree that showed severe symptoms of basal stem rot disease (symptomatic oil palm). The microbial community difference between asymptomatic and symptomatic oil palm will help understand disease suppression. Thus, in this study, rhizosphere soil was sampled around asymptomatic (OP - G) and symptomatic (OP + G) oil palm trees in Ganoderma-infected oil palm orchards. Illumina next-generation sequencing (NGS), bioinformatics analysis, bacterial diversity, and soil physicochemical properties were evaluated. The results demonstrated that soil physicochemical properties and species richness around rhizosphere soil of OP - G and OP + G samples were not significantly different. The age of the oil palm trees and oil palm variety showed negligible correlation and were not significant with bacterial diversity. However, the top ten most abundant analysis of the bacterial communities showed that phyla Actinobacteria and Firmicutes were significantly increased in rhizosphere soil around OP - G samples relative to the OP+ G samples. The unique operational taxonomic units (OTUs) of OP - G (2137) were higher than in the OP+ G samples (1747 OTUs). These bacterial communities have been reported as biological control agents and/or plant growth-promoting rhizosphere bacteria that are related to disease suppression. Thus, the data provided are useful for developing suppressive soil to biologically control G. boninense.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.