Bacterial Indicator of Agricultural Management for Soil under No-Till Crop Production

Abstract

The rise in the world demand for food poses a challenge to our ability to sustain soil fertility and sustainability. The increasing use of no-till agriculture, adopted in many areas of the world as an alternative to conventional farming, may contribute to reduce the erosion of soils and the increase in the soil carbon pool. However, the advantages of no-till agriculture are jeopardized when its use is linked to the expansion of crop monoculture. The aim of this study was to survey bacterial communities to find indicators of soil quality related to contrasting agriculture management in soils under no-till farming. Four sites in production agriculture, with different soil properties, situated across a west-east transect in the most productive region in the Argentinean pampas, were taken as the basis for replication. Working definitions of Good no-till Agricultural Practices (GAP) and Poor no-till Agricultural Practices (PAP) were adopted for two distinct scenarios in terms of crop rotation, fertilization, agrochemicals use and pest control. Non-cultivated soils nearby the agricultural sites were taken as additional control treatments. Tag-encoded pyrosequencing was used to deeply sample the 16S rRNA gene from bacteria residing in soils corresponding to the three treatments at the four locations. Although bacterial communities as a whole appeared to be structured chiefly by a marked biogeographic provincialism, the distribution of a few taxa was shaped as well by environmental conditions related to agricultural management practices. A statistically supported approach was used to define candidates for management-indicator organisms, subsequently validated using quantitative PCR. We suggest that the ratio between the normalized abundance of a selected group of bacteria within the GP1 group of the phylum Acidobacteria and the genus Rubellimicrobium of the Alphaproteobacteria may serve as a potential management-indicator to discriminate between sustainable vs. non-sustainable agricultural practices in the Pampa region.

Major cereal crops benefit from biological nitrogen fixation when inoculated with the nitrogen‐fixing bacterium Pseudomonas protegens Pf‐5 X940

“…This soil, under good agricultural practices, contains diverse indigenous microorganisms (Figuerola et al, 2012). The soil was collected from the Pampean Region, Buenos Aires, Argentina.…”

Crop yield and soil carbon responses to tillage method changes in North China

“…However, after several successive years of these reduced tillage (i.e., only tilling to a depth less than 20 cm) or no-tillage systems, subsoil compaction at 15-30 cm depths appears to be increasing due to the increase of bulk density resulted by water infiltration and sowing and harvesting machineries (Kong et al, 2010;. Furthermore, although adopting some form of conservation tillage is generally beneficial for increasing SOC levels and sequestering C in the topsoil (Ussiri and Lal, 2009;Martinez et al, 2013), the reduced incorporation of crop residues has been reported to increase subsoil bulk density and reduce crop root proliferation (Figuerola et al, 2012), thereby limiting water and nutrient availability (He et al, 2009) and resulting in reduced crop yields (Bhatia et al, 2010;Arvidsson et al, 2014). For example, many studies have demonstrated that decreases in crop yield in response to long-term NT adoption may be caused by reduced seed germination and emergence, lower early season soil temperatures, below-optimal plant populations, poorer weed control, delayed plant development and maturity, increased grain moisture content, and lower grain yield potential following adoption of NT (Toliver et al, 2012;Kovar et al, 1992;Fortin, 1993;Swan et al, 1994).…”

Looking for Rhizobacterial Ecological Indicators in Agricultural Soils Using 16S rRNA metagenomic Amplicon Data

“…The Indicator Value method has been successfully applied to test for indicator species using metagenomic data [30] of tilled vs. non-tilled crop production. Its application to our datasets is questionable as the classification of pesticide impact is normally based, among other parameters, on the effect on taxonomic diversity at higher (usually class or phylum) levels.…”