The impact of polymer-based slow-release urea formulations on soil microbial N dynamics in potatoes has been sparingly deciphered. The present study investigated the effect of a biodegradable nano-polymer urea formulation on soil enzymatic activities and microflora involved in the N cycling of potato (Solanum tuberosum L.). The nano-chitosan-urea composite (NCUC) treatment significantly increased the soil dehydrogenase activity, organic carbon content and available potassium compared to the conventional urea (CU) treatment. The soil ammonical nitrogen (NH₄⁺-N) and nitrate nitrogen (NO₃⁻-N) contents and urease activity were significantly decreased in the NCUC-amended soil. The slow urea hydrolysis rate led to low concentrations of NH₄⁺-N and NO₃⁻-N in the tested potato soil. Furthermore, these results corroborate the low count of ammonia oxidizer and nitrate reducer populations. Quantitative PCR (q-PCR) studies revealed that the relative abundance of eubacterial (AOB) and archaeal ammonia-oxidizing (AOA) populations was reduced in the NCUC-treated soil compared to CU. The abundance of AOA was particularly lower than AOB, probably due to the more neutral and alkaline conditions of the tested soil. Our results suggest that the biodegradable polymer urea composite had a significant effect on the microbiota associated with soil N dynamics. Therefore, the developed NCUC could be used as a slow N-release fertilizer for enhanced growth and crop yields of potato.

Rice is a principal food crop for more than half of the global population. Grain number and grain weight (2Gs) are the two complex traits controlled by several quantitative trait loci (QTLs) and are considered the most critical components for yield enhancement in rice. Novel molecular biology and QTL mapping strategies can be utilized in dissecting the complex genetic architecture of these traits. Discovering the valuable genes/QTLs associated with 2Gs traits hidden in the rice genome and utilizing them in breeding programs may bring a revolution in rice production. Furthermore, the positional cloning and functional characterization of identified genes and QTLs may aid in understanding the molecular mechanisms underlying the 2Gs traits. In addition, knowledge of modern genomic tools aids the understanding of the nature of plant and panicle architecture, which enhances their photosynthetic activity. Rice researchers continue to combine important yield component traits (including 2Gs for the yield ceiling) by utilizing modern breeding tools, such as marker-assisted selection (MAS), haplotype-based breeding, and allele mining. Physical co-localization of GW7 (for grain weight) and DEP2 (for grain number) genes present on chromosome 7 revealed the possibility of simultaneous introgression of these two genes, if desirable allelic variants were found in the single donor parent. This review article will reveal the genetic nature of 2Gs traits and use this knowledge to break the yield ceiling by using different breeding and biotechnological tools, which will sustain the world's food requirements.

Background: Heat tolerance is becoming increasingly important where maize is grown under spring season in India which coincide with grain filling stage of crop resulting in tassel blast, reduced pollen viability, pollination failure and barren ears that causes devastating yield losses. So, there is need to identify the genomic regions associated with heat tolerance component traits which could be further employed in maize breeding program. Results: An association mapping panel, consisting of 662 doubled haploid (DH) lines, was evaluated for yield contributing traits under normal and natural heat stress conditions. Genome wide association studies (GWAS) carried out using 187,000 SNPs and 130 SNPs significantly associated for grain yield (GY), days to 50% anthesis (AD), days to 50% silking (SD), anthesis-silking interval (ASI), plant height (PH), ear height (EH) and ear position (EPO) were identified under normal conditions. A total of 46 SNPs strongly associated with GY, ASI, EH and EPO were detected under heat stress conditions. Fifteen of the SNPs was found to have common association with more than one trait such as two SNPs viz. S10_1,905,273 and S10_1,905,274 showed colocalization with GY, PH and EH whereas S10_7,132,845 SNP associated with GY, AD and SD under normal conditions. No such colocalization of SNP markers with multiple traits was observed under heat stress conditions. Haplotypes trend regression analysis revealed 122 and 85 haplotype blocks, out of which, 20 and 6 haplotype blocks were associated with more than one trait under normal and heat stress conditions, respectively. Based on SNP association and haplotype mapping, nine and seven candidate genes were identified respectively, which belongs to different gene models having different biological functions in stress biology. Conclusions: The present study identified significant SNPs and haplotype blocks associated with yield contributing traits that help in selection of donor lines with favorable alleles for multiple traits. These results provided insights of genetics of heat stress tolerance. The genomic regions detected in the present study need further validation before being applied in the breeding pipelines.

Maize is the third most important cereal crop worldwide. However, its production is vulnerable to heat stress, which is expected to become more and more severe in coming years. Germplasm resilient to heat stress has been identified, but its underlying genetic basis remains poorly understood. Genomic mapping technologies can fill the void, provided robust markers are available to tease apart the genotype-phenotype relationship. In the present investigation, we used data from an RNA-seq experiment to identify single nucleotide polymorphisms (SNPs) between two contrasting lines, LM11 and CML25, sensitive and tolerant to heat stress, respectively. The libraries for RNA-seq were made following heat stress treatment from three separate tissues/organs, comprising the top leaf, ovule, and pollen, all of which are highly vulnerable to damage by heat stress. The single nucleotide variants (SNVs) calling used STAR mapper and GATK caller pipelines in a combined approach to identify highly accurate SNPs between the two lines. A total of 554,423, 410,698, and 596,868 SNVs were discovered between LM11 and CML25 after comparing the transcript sequence reads from the leaf, pollen, and ovule libraries, respectively. Hundreds of these SNPs were then selected to develop into genome-wide Kompetitive Allele-Specific PCR (KASP) markers, which were validated to be robust with a successful SNP conversion rate of 71%. Subsequently, these KASP markers were used to effectively genotype an F₂ mapping population derived from a cross of LM11 and CML25. Being highly cost-effective, these KASP markers provide a reliable molecular marker toolkit to not only facilitate the genetic dissection of the trait of heat stress tolerance but also to accelerate the breeding of heat-resilient maize by marker-assisted selection (MAS).

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.