Weeds are among the major constraints to any crop production system, reducing productivity and profitability. Herbicides are among the most effective methods to control weeds, and reliance on herbicides for weed control has increased significantly with the advent of herbicide-resistant crops. Unfortunately, over-reliance on herbicides leads to environmental-health issues and herbicide-resistant weeds, causing human health and ecological concerns. Crop diversification can help manage weeds sustainably in major crop production systems. It acts as an organizing principle under which technological innovations and ecological insights can be combined to manage weeds sustainably. Diversified cropping can be defined as the conscious inclusion of functional biodiversity at temporal and/or spatial levels to improve the productivity and stability of ecosystem services. Crop diversification helps to reduce weed density by negatively impacting weed seed germination and weed growth. Additionally, diversified farming systems are more resilient to climate change than monoculture systems and provide better crop yield. However, there are a few challenges to adopting a diversified cropping system, ranging from technology innovations, government policies, farm-level decisions, climate change, and market conditions. In this review, we discuss how crop diversification supports sustainable weed management, the challenges associated with it, and the future of weed management with respect to the diversification concept.

The use of herbicide-resistant (HR) Clearfield rice (Oryza sativa) to control weedy rice has increased in the past 12 years to constitute about 60% of rice acreage in Arkansas, where most U.S. rice is grown. To assess the impact of HR cultivated rice on the herbicide resistance and population structure of weedy rice, weedy samples were collected from commercial fields with a history of Clearfield rice. Panicles from each weedy type were harvested and tested for resistance to imazethapyr. The majority of plants sampled had at least 20% resistant offspring. These resistant weeds were 97 to 199 cm tall and initiated flowering from 78 to 128 d, generally later than recorded for accessions collected prior to the widespread use of Clearfield rice (i.e. historical accessions). Whereas the majority (70%) of historical accessions had straw-colored hulls, only 30% of contemporary HR weedy rice had straw-colored hulls. Analysis of genotyping-by-sequencing data showed that HR weeds were not genetically structured according to hull color, whereas historical weedy rice was separated into straw-hull and black-hull populations. A significant portion of the local rice crop genome was introgressed into HR weedy rice, which was rare in historical weedy accessions. Admixture analyses showed that HR weeds tend to possess crop haplotypes in the portion of chromosome 2 containing the ACETOLACTATE SYNTHASE gene, which confers herbicide resistance to Clearfield rice. Thus, U.S. HR weedy rice is a distinct population relative to historical weedy rice and shows modifications in morphology and phenology that are relevant to weed management.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Rice (Oryza sativa L.) cultivars show impairment of growth in response to environmental stresses such as cold at the early seedling stage. Locally adapted weedy rice is able to survive under adverse environmental conditions, and can emerge in fields from greater soil depth. Cold-tolerant weedy rice can be a good genetic source for developing cold-tolerant, weed-competitive rice cultivars. An in-depth analysis is presented here of diverse indica and japonica rice genotypes, mostly weedy rice, for cold stress response to provide an understanding of different stress adaptive mechanisms towards improvement of the rice crop performance in the field. We have tested a collection of weedy rice genotypes to: 1) classify the subspecies (ssp.) grouping (japonica or indica) of 21 accessions; 2) evaluate their sensitivity to cold stress; and 3) analyze the expression of stress-responsive genes under cold stress and a combination of cold and depth stress. Seeds were germinated at 25°C at 1.5- and 10-cm sowing depth for 10d. Seedlings were then exposed to cold stress at 10°C for 6, 24 and 96h, and the expression of cold-, anoxia-, and submergence-inducible genes was analyzed. Control plants were seeded at 1.5cm depth and kept at 25°C. The analysis revealed that cold stress signaling in indica genotypes is more complex than that of japonica as it operates via both the CBF-dependent and CBF-independent pathways, implicated through induction of transcription factors including OsNAC2, OsMYB46 and OsF-BOX28. When plants were exposed to cold + sowing depth stress, a complex signaling network was induced that involved cross talk between stresses mediated by CBF-dependent and CBF-independent pathways to circumvent the detrimental effects of stresses. The experiments revealed the importance of the CBF regulon for tolerance to both stresses in japonica and indica ssp. The mechanisms for cold tolerance differed among weedy indica genotypes and also between weedy indica and cultivated japonica ssp. as indicated by the up/downregulation of various stress-responsive pathways identified from gene expression analysis. The cold-stress response is described in relation to the stress signaling pathways, showing complex adaptive mechanisms in different genotypes.

Herbicide-resistantEchinochloaspp. pose a significant threat to U.S. rice production. Two surveys were conducted to characterizeEchinochloaresistance to common rice herbicides and provide important demographic information on the populations in Arkansas: one was theEchinochloa Herbicide Resistance Confirmation Surveyconducted annually since 2006; the other was theEchinochloa Herbicide Resistance Demographics Surveyconducted since 2010. TheResistance Confirmation Surveyshowed that resistance to propanil (50%) was most prevalent, followed by quinclorac (23%), imazethapyr (13%), and cyhalofop (3%). Multiple resistance increased with time, with 27% of accessions being multiple-resistant, mostly to propanil+quinclorac (12%). The parallelResistance Demographics Surveytested resistance by species. Of the 264 accessions collected, 73% were junglerice, 14% were rough barnyardgrass, and 11% were barnyardgrass. Overall, this survey also showed resistance to propanil (53%) and quinclorac (28%) being most prevalent, with low frequencies of resistance to cyhalofop (12%) and imazethapyr (6%). Resistance to herbicides was less frequent with barnyardgrass (54%) and rough barnyardgrass (28%) than with junglerice (73%). Multiple resistance was most frequent with junglerice (33%) and least frequent with rough barnyardgrass (8%). Across both surveys, the resistance cases were clustered in the northeast and Grand Prairie regions of the state. Herbicide resistance amongEchinochloapopulations in rice fields is continuing to increase in frequency and complexity. This is a consequence of sequential selection with different major herbicide sites of action, starting with propanil followed by quinclorac and others.