Abstract Harnessing the potential of biochar in the rhizosphere to enhance plant health, growth and soil fertility is a promising avenue in agriculture. However, conventional research falls short in elucidating the underlying mechanisms of biochar’s actions. Hence, the advent of multi-omics technologies becomes imperative in unravelling the multifaceted interplay among biochar, plants, and microbes within the dynamic rhizosphere. Metagenomics sheds light on microbial population dynamics following biochar application, while metatranscriptomics unveils gene expression and pathway regulation within microbial communities, offering insights into their metabolic intricacies. At the same time, metaproteomics and metametabolomics delve into protein products and metabolic profiles within the rhizobiome, respectively. Understanding the interactions of biochar with the rhizobiome holds promise in constructing predictive models and developing novel strategies to nurture soil health. This review focuses on using metaomics approaches to enhance biochar integration in agriculture, highlighting existing challenges in their application and emphasizing the need to overcome these barriers to improve soil fertility and microbial ecology and contribute to soil remediation.
Supplementary Material 7
Salinity is a major abiotic stress that causes substantial agricultural losses worldwide. Chickpea (<i>Cicer arietinum</i> L.) is an important legume crop but is salt-sensitive. Previous physiological and genetic studies revealed the contrasting response of two desi chickpea varieties, salt-sensitive Rupali and salt-tolerant Genesis836, to salt stress. To understand the complex molecular regulation of salt tolerance mechanisms in these two chickpea genotypes, we examined the leaf transcriptome repertoire of Rupali and Genesis836 in control and salt-stressed conditions. Using linear models, we identified categories of differentially expressed genes (DEGs) describing the genotypic differences: salt-responsive DEGs in Rupali (1,604) and Genesis836 (1,751) with 907 and 1,054 DEGs unique to Rupali and Genesis836, respectively, salt responsive DEGs (3,376), genotype-dependent DEGs (4,170), and genotype-dependent salt-responsive DEGs (122). Functional DEG annotation revealed that the salt treatment affected genes involved in ion transport, osmotic adjustment, photosynthesis, energy generation, stress and hormone signalling, and regulatory pathways. Our results showed that while Genesis836 and Rupali have similar primary salt response mechanisms (common salt-responsive DEGs), their contrasting salt response is attributed to the differential expression of genes primarily involved in ion transport and photosynthesis. Interestingly, variant calling between the two genotypes identified SNPs/InDels in 768 Genesis836 and 701 Rupali salt-responsive DEGs with 1,741 variants identified in Genesis836 and 1,449 variants identified in Rupali. In addition, the presence of premature stop codons was detected in 35 genes in Rupali. This study provides valuable insights into the molecular regulation underpinning the physiological basis of salt tolerance in two chickpea genotypes and offers potential candidate genes for the improvement of salt tolerance in chickpeas.
This chapter focuses on integrated crop management strategies to increase grain legume production in rainfed, resource-poor farming systems. Generally, grain legumes fail to reach even half of their potential yields in these systems. For rainfed grain legumes the major contributor to the yield gap is sub-optimal soil moisture, along with a suite of nutrient, pest and disease constraints. The challenge is to identify remedial action within the means of resource-poor farmers. This requires greater emphasis on farmer-participatory research to identify local constraints, and engaging farmers in trialling locally feasible solutions. Examples of this approach are documented. Particular areas in need of intensive on-farm research include adapting grain legume farming to conservation agriculture and exploring means to increase cropping intensity of grain legumes in cereal-dominated cropping systems. It is suggested that a concerted shift in international and national efforts to support farmer-participatory approaches is needed.
Pulses such as chickpea, faba bean and lentil have hypogeal emergence and their cotyledons remain where the seed is sown, while only the shoot emerges from the soil surface. The effect of three sowing depths (2.5, 5 and 10 cm) on the growth and yield of these pulses was studied at three locations across three seasons in the cropping regions of south‐western Australia, with a Mediterranean‐type environment. There was no effect of sowing depth on crop phenology, nodulation or dry matter production for any species. Mean seed yields across sites ranged from 810 to 2073 kg ha −1 for chickpea, 817–3381 kg ha −1 for faba bean, and 1173–2024 kg ha −1 for lentil. In general, deep sowing did not reduce seed yields, and in some instances, seed yield was greater at the deeper sowings for chickpea and faba bean. We conclude that the optimum sowing depth for chickpea and faba bean is 5–8 cm, and for lentil 4–6 cm. Sowing at depth may also improve crop establishment where moisture from summer and autumn rainfall is stored in the subsoil below 5 cm, by reducing damage from herbicides applied immediately before or after sowing, and by improving the survival of Rhizobium inoculated on the seed due to more favourable soil conditions at depth.
Dates of ear initation and anthesis were recorded for 16 wheat cultivars at a wide range of sowing dates in four field experiments conducted over four years.In general for the majority of cultivars number of days from sowing to ear initiation increased as sowing was delayed through May and then declined with sowings after June. The effects of sowing date and cultivar on anthesis were similar to those observed for ear initiation. Maximum time to anthesis was observed from sowings in early May.A linear regression model relating rate of development to mean temperature and photoperiod accounted for 47-98% of the variation in rate of development from sowing to ear initiation and from 68 to 98% of the variation from ear initiation to anthesis. A five-parameter non-linear model was also tested but was not superior. Observations in a single year were sufficient to characterize a cultivar provided the range of mean temperature and photoperiod was large.Comparison with data from other field sites of ear initiation and anthesis showed that the regression equations gave a good fit to the occurrence of these events when used in the incremental sense, that is, by summing increments of development rate calculated from daily temperature and photoperiod.The prediction model is discussed in relation to its application in simulation models of crop growth, analysis of cultivar adaptation to environments and in day-to-day crop management.