The relative importance of soil moisture in predicting bacterial wilt disease occurrence

Abstract

Soil-borne plant diseases cause major economic losses globally. This is partly because their epidemiology is difficult to predict in agricultural fields, where multiple environmental factors could determine disease outcomes. Here we used a combination of field sampling and direct experimentation to identify key abiotic and biotic soil properties that can predict the occurrence of bacterial wilt caused by pathogenic Ralstonia solanacearum. By analyzing 139 tomato rhizosphere soils samples isolated from six provinces in China, we first show a clear link between soil properties, pathogen density and plant health. Specifically, disease outcomes were positively associated with soil moisture, bacterial abundance and bacterial community composition. Based on soil properties alone, random forest machine learning algorithm could predict disease outcomes correctly in 75% of cases with soil moisture being the most significant predictor. The importance of soil moisture was validated causally in a controlled greenhouse experiment, where the highest disease incidence was observed at 60% of maximum water holding capacity. Together, our results show that local soil properties can predict disease occurrence across a wider agricultural landscape, and that management of soil moisture could potentially offer a straightforward method for reducing crop losses to R. solanacearum.

Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions

“…Abiotic physicochemical properties included pH, soil moisture content (% moisture), electric conductivity (us·cm −1 ), soil organic matter (g·kg −1 ), total nitrogen (mg·kg −1 ), total phosphorus (mg·kg −1 ), available nitrogen (mg·kg −1 ), available phosphorus (mg·kg −1 ) and available potassium (mg·kg −1 ). Soil physicochemical properties were mainly measured as described previously ( 83 ) following the Chinese standard for soil property determination ( www.chinesestandard.net ): soil pH (HJ 962–2018), EC (HJ 802–2016), SOM (NY/T 1121.6–2006), TN (LY/T 1228–2015), TP (GB/T 9837–1988), AN (LY/T 1229–1999). The difference between fresh and air-dried soil sample weights was used as a proxy of soil moisture for each rhizosphere sample.…”

Tomato growth stage modulates bacterial communities across different soil aggregate sizes and disease levels

“…5c ). This preference might be linked to the stronger water storage capacity of micro-aggregates [ 67 ], while higher soil moisture can support a greater density of R. solanacearum [ 85 ]. More populations of potential pathogen-inhibiting taxa (Fig.…”

Development of a machine‐learning model to identify the impacts of pesticides characteristics on soil microbial communities from high‐throughput sequencing data

“…Machine learning has the capability to identify meaningful and regular patterns in a complex set of data, consistently predicting various biological responses (Ahneman et al, 2018; Jiang et al, 2021; Jordan & Mitchell, 2015). By a machine learning approach, we identified biomarker taxa that can serve to discriminate the pesticides impact on soil biodiversity from a complex chemical soil system, which can be a novel quality indicator for agricultural soil remediation effectiveness of pesticides‐pollution.…”