Influence of conservation agriculture-based production systems on bacterial diversity and soil quality in rice-wheat-greengram cropping system in eastern Indo-Gangetic Plains of India

Abstract

Introduction: Conservation agriculture (CA) is gaining attention in the South Asia as an environmentally benign and sustainable food production system. The knowledge of the soil bacterial community composition along with other soil properties is essential for evaluating the CA-based management practices for achieving the soil environment sustainability and climate resilience in the rice-wheat-greengram system. The long-term effects of CA-based tillage-cum-crop establishment (TCE) methods on earthworm population, soil parameters as well as microbial diversity have not been well studied.

Methods: Seven treatments (or scenarios) were laid down with the various tillage (wet, dry, or zero-tillage), establishment method (direct-or drill-seeding or transplantation) and residue management practices (mixed with the soil or kept on the soil surface). The soil samples were collected after 7 years of experimentation and analyzed for the soil quality and bacterial diversity to examine the effect of tillage-cum-crop establishment methods.

Results and discussion: Earthworm population (3.6 times), soil organic carbon (11.94%), macro (NPK) (14.50-23.57%) and micronutrients (Mn, and Cu) (13.25 and 29.57%) contents were appreciably higher under CA-based TCE methods than tillage-intensive farming practices. Significantly higher number of OTUs (1,192 ± 50) and Chao1 (1415.65 ± 14.34) values were observed in partial CA-based production system (p ≤ 0.05). Forty-two (42) bacterial phyla were identified across the scenarios, and Proteobacteria, Actinobacteria, and Firmicutes were the most dominant in all the scenarios. The CA-based scenarios harbor a high abundance of Proteobacteria (2-13%), whereas the conventional tillage-based scenarios were dominated by the bacterial phyla Acidobacteria and Chloroflexi and found statistically differed among the scenarios (p ≤ 0.05). Composition of the major phyla, i.e., Proteobacteria, Actinobacteria, and Firmicutes were associated differently with either CA or farmers-based tillage management practices. Overall, the present study indicates the importance of CA-based tillage-cum-crop establishment methods in shaping the bacterial diversity, earthworms population, soil organic carbon, and plant nutrient availability, which are crucial for sustainable agricultural production and resilience in agro-ecosystem.

Keywords: DNA sequencing; bacterial diversity; conservation agriculture; earthworm; metagenomics; rice-wheat-greengram; soil quality.

Figure 1

Soil quality index (SQI) as affected by different tillage-cum-crop establishments scenarios (sc). Vertical bars represent standard error of mean. Means followed by different small letters are significantly different at p ≤ 0.05.

Figure 2

Principal coordinate analysis (PCoA) based on the Bray–Curtis dissimilarity matrix showing significantly different microbial compositions among different scenarios (ANOSIM R = 0.61818, value of p < 0.001).

Figure 3

Distribution of the dominating bacterial phyla (>1% relative abundance) in different tillage-cum-crop establishments scenarios. The values are average of the five replicates (n = 5); whiskers represent standard error of mean (SEm); same letters over bars across different scenarios indicates non-significant differences within phyla (Duncan’s multiple range test; p ≤ 0.05).

Figure 4

Relative abundance at phylum, Order, and OTU level in different tillage-cum-crop establishments. (A) Relative abundance of dominant bacterial phyla (B) Relative abundance of dominant bacterial orders. Taxa below 1% relative abundance were clubbed together as “others.” Taxa with asterisk (*) mark are statistically differed among treatments (scenarios) (Kruskal–Wallis test; p ≤ 0.05).

Figure 5

Distribution of dominating classes (>1% relative abundance) in different tillage-cum-crop establishments scenarios. The values are average of five replicates (n = 5); whiskers represent standard error of mean (SEm).

Figure 6

Dominating orders (>1% relative abundance) under the different tillage-cum-crop establishments scenarios. The values are average of the five replicates (n = 5); whiskers represent standard error of the mean (SEm).

Figure 7

Venn diagram showing unique and shared OTUs between different tillage- cum- crop establishment and residue management production systems.

Figure 8

Relationship among the major bacterial phyla (>1% relative abundance) of tillage-cum crop establishment and residue management production system scenarios. Size of nodes is based on number of connections to that node (phylum). Two phyla are connected through bars which size is based on magnitude of correlation. Blue color of bars represents negative correlations, while red represents the positive correlations.

Figure 9

Scatter plot of seven scenarios of agricultural managements on PCA coordinates based on major bacterial phyla, earthworm counts and different soil properties.