Artificial intelligence in plant breeding.

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Exploring impact of recombination landscapes on breeding outcomes.

Plant breeding relies on crossing-over to create novel combinations of alleles needed to confer increased productivity and other desired traits in new varieties. However, crossover (CO) events are rare, as usually only one or two of them occur per chromosome in each generation. In addition, COs are not distributed evenly along chromosomes. In plants with large genomes, which includes most crops, COs are predominantly formed close to chromosome ends, and there are few COs in the large chromosome swaths around centromeres. This situation has created interest in engineering CO landscape to improve breeding efficiency. Methods have been developed to boost COs globally by altering expression of anti-recombination genes and increase CO rates in certain chromosome parts by changing DNA methylation patterns. In addition, progress is being made to devise methods to target COs to specific chromosome sites. We review these approaches and examine using simulations whether they indeed have the capacity to improve efficiency of breeding programs. We found that the current methods to alter CO landscape can produce enough benefits for breeding programs to be attractive. They can increase genetic gain in recurrent selection and significantly decrease linkage drag around donor loci in schemes to introgress a trait from unimproved germplasm to an elite line. Methods to target COs to specific genome sites were also found to provide advantage when introgressing a chromosome segment harboring a desirable quantitative trait loci. We recommend avenues for future research to facilitate implementation of these methods in breeding programs.

Wheat Ym2 originated from Aegilops sharonensis and confers resistance to soil-borne Wheat yellow mosaic virus infection to the roots.

Wheat yellow mosaic virus (WYMV) is a pathogen transmitted into its host's roots by the soil-borne vector Polymyxa graminis. Ym1 and Ym2 genes protect the host from the significant yield losses caused by the virus, but the mechanistic basis of these resistance genes remains poorly understood. Here, it has been shown that Ym1 and Ym2 act within the root either by hindering the initial movement of WYMV from the vector into the root and/or by suppressing viral multiplication. A mechanical inoculation experiment on the leaf revealed that the presence of Ym1 reduced viral infection incidence, rather than viral titer, while that of Ym2 was ineffective in the leaf. To understand the basis of the root specificity of the Ym2 product, the gene was isolated from bread wheat using a positional cloning approach. The candidate gene encodes a CC-NBS-LRR protein and it correlated allelic variation with respect to its sequence with the host's disease response. Ym2 (B37500) and its paralog (B35800) are found in the near-relatives, respectively, Aegilops sharonensis and Aegilops speltoides (a close relative of the donor of bread wheat's B genome), while both sequences, in a concatenated state, are present in several accessions of the latter species. Structural diversity in Ym2 has been generated via translocation and recombination between the two genes and enhanced by the formation of a chimeric gene resulting from an intralocus recombination event. The analysis has revealed how the Ym2 region has evolved during the polyploidization events leading to the creation of cultivated wheat.

Toward plant breeding for multicrop systems.

Increasing cropping system diversity has great potential to address environmental problems associated with modern agriculture, such as erosion, soil carbon loss, nutrient runoff, water pollution, and loss of biodiversity. As with other agricultural sciences, plant breeding has primarily been conducted in the context of dominant monoculture cropping systems, with little focus on multicrop systems. Multicrop systems have increased temporal and/or spatial diversity and include a diverse set of crops and practices. In order to support a transition to multicrop systems, plant breeders must shift their breeding programs and objectives to better represent more diverse systems, including diverse rotations, alternate-season crops, ecosystem service crops, and intercropping systems. The degree to which breeding methods need to change will depend on the cropping system context in question. Plant breeding alone, however, cannot drive adoption of multicrop systems. Alongside shifts in breeding approaches, changes are needed within broader research, private sector, and policy contexts. These changes include policies and investments that support a transition to multicrop systems, increased collaboration across disciplines to support cropping system development, and leadership from both the public and private sectors to develop and promote adoption of new cultivars.

The genomics of linkage drag in inbred lines of sunflower.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products.

This perspective describes the opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South. Data-driven approaches build on larger volumes of data and flexible analyses that link different datasets across domains and disciplines. This can lead to more information-rich management of crop diversity, which can address the complex interactions between crop diversity, production environments, and socioeconomic heterogeneity and help to deliver more suitable portfolios of crop diversity to users with highly diverse demands. We describe recent efforts that illustrate the potential of data-driven approaches for crop diversity management. A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.

A protocol for rapid generation cycling (speed breeding) of hemp (Cannabis sativa) for research and agriculture.

Hemp (Cannabis sativa) is a highly versatile crop with a multitude of applications, from textiles, biofuel and building material to high-value food products for consumer markets. Furthermore, non-hallucinogenic cannabinoids like cannabidiol (CBD), which can be extracted from female hemp flowers, are potentially valuable pharmacological compounds. In addition, hemp has high carbon sequestration potential associated with its rapid growth rate. Therefore, the hemp industry is gaining more traction and breeding hemp cultivars adapted to local climate conditions or bred for specific applications is becoming increasingly important. Here, we present a method for the rapid generation cycling (speed breeding) of hemp. The speed breeding protocol makes use of the photoperiod sensitivity of Cannabis. It encompasses vegetative growth of the plants for 2 weeks under continuous light, followed by 4 weeks under short-day conditions, during which flower induction, pollination and seed development proceed, and finally a seed ripening phase under continuous light and water stress. With the protocol described here, a generation time of under 9 weeks (61 days) from seed to seed can be achieved. Furthermore, our method synchronises the flowering time of different hemp cultivars, thus facilitating crosses between cultivars. The extremely short generation time will enable hemp researchers and breeders to perform crosses in a time-efficient way and generate new hemp cultivars with defined genetic characteristics over a short period of time.

HASCH - A high-throughput amplicon-based SNP-platform for medicinal cannabis and industrial hemp genotyping applications.

BACKGROUND: Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. RESULTS: The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. CONCLUSION: HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

Methylomes as key features for predicting recombination in some plant species.

Knowing how chromosome recombination works is essential for plant breeding. It enables the design of crosses between different varieties to combine desirable traits and create new ones. This is because the meiotic crossovers between homologous chromatids are not purely random, and various strategies have been developed to describe and predict such exchange events. Recent studies have used methylation data to predict chromosomal recombination in rice using machine learning models. This approach proved successful due to the presence of a positive correlation between the CHH context cytosine methylation and recombination rates in rice chromosomes. This paper assesses the question if methylation can be used to predict recombination in four plant species: Arabidopsis, maize, sorghum, and tomato. The results indicate a positive association between CHH context methylation and recombination rates in certain plant species, with varying degrees of strength in their relationships. The CG and CHG methylation contexts show negative correlation with recombination. Methylation data was key effectively in predicting recombination in sorghum and tomato, with a mean determination coefficient of 0.65 ± 0.11 and 0.76 ± 0.05, respectively. In addition, the mean correlation values between predicted and experimental recombination rates were 0.83 ± 0.06 for sorghum and 0.90 ± 0.05 for tomato, confirming the significance of methylomes in both monocotyledonous and dicotyledonous species. The predictions for Arabidopsis and maize were not as accurate, likely due to the comparatively weaker relationships between methylation contexts and recombination, in contrast to sorghum and tomato, where stronger associations were observed. To enhance the accuracy of predictions, further evaluations using data sets closely related to each other might prove beneficial. In general, this methylome-based method holds great potential as a reliable strategy for predicting recombination rates in various plant species, offering valuable insights to breeders in their quest to develop novel and improved varieties.

Genotypic variation in blueberry flower morphology and nectar reward content affects pollinator attraction in a diverse breeding population.

BACKGROUND: Pollination is crucial to obtaining optimal blueberry yield and fruit quality. Despite substantial investments in seasonal beekeeping services, blueberry producers consistently report suboptimal pollinator visitation and fruit set in some cultivars. Flower morphology and floral rewards are among the key factors that have shown to contribute to pollinator attraction, however little is known about their relative importance for improving yield in the context of plant breeding. Clarifying the relationships between flower morphology, nectar reward content, pollinator recruitment, and pollination outcomes, as well as their genetic components, can inform breeding priorities for enhancing blueberry production. In the present study, we measured ten flower and nectar traits and indices of successful pollination, including fruit set, seed count, and fruit weight in 38 southern highbush blueberry genotypes. Additionally, we assessed pollinator visitation frequency and foraging behavior over two growing seasons. Several statistical models were tested to optimize the prediction of pollinator visitation and pollination success, including partial least squares, BayesB, ridge-regression, and random forest. RESULTS: Random forest models obtained high predictive abilities for pollinator visitation frequency, with values of 0.54, 0.52, and 0.66 for honey bee, bumble bee, and total pollinator visits, respectively. The BayesB model provided the most consistent prediction of fruit set, fruit weight, and seed set, with predictive abilities of 0.07, -0.08, and 0.42, respectively. Variable importance analysis revealed that genotypic differences in nectar volume had the greatest impact on honey bee and bumble bee visitation, although preferences for flower morphological traits varied depending on the foraging task. Flower density was a major driving factor attracting nectar-foraging honey bees and bumble bees, while pollen-foraging bumble bees were most influenced by flower accessibility, specifically corolla length and the length-to-width ratio. CONCLUSIONS: Honey bees comprised the majority of pollinator visits, and were primarily influenced by nectar volume and flower density. Corolla length and the length-to-width ratio were also identified as the main predictors of fruit set, fruit weight, seed count, as well as pollen-foraging bumble bee visits, suggesting that these bees and their foraging preferences may play a pivotal role in fruit production. Moderate to high narrow-sense heritability values (ranging from 0.30 to 0.77) were obtained for all floral traits, indicating that selective breeding efforts may enhance cultivar attractiveness to pollinators.

Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding.

Inversions, a type of chromosomal structural variation, significantly influence plant adaptation and gene functions by impacting gene expression and recombination rates. However, compared with other structural variations, their roles in functional biology and crop improvement remain largely unexplored. In this review, we highlight technological and methodological advancements that have allowed a comprehensive understanding of inversion variants through the pangenome framework and machine learning algorithms. Genome editing is an efficient method for inducing or reversing inversion mutations in plants, providing an effective mechanism to modify local recombination rates. Given the potential of inversions in crop breeding, we anticipate increasing attention on inversions from the scientific community in future research and breeding applications.

Development of TALE-adenine base editors in plants.

Base editors enable precise nucleotide changes at targeted genomic loci without requiring double-stranded DNA breaks or repair templates. TALE-adenine base editors (TALE-ABEs) are genome editing tools, composed of a DNA-binding domain from transcription activator-like effectors (TALEs), an engineered adenosine deaminase (TadA8e), and a cytosine deaminase domain (DddA), that allow A•T-to-G•C editing in human mitochondrial DNA. However, the editing ability of TALE-ABEs in plants apart from chloroplast DNA has not been described, so far, and the functional role how DddA enhances TadA8e is still unclear. We tested a series of TALE-ABEs with different deaminase fusion architectures in Nicotiana benthamiana and rice. The results indicate that the double-stranded DNA-specific cytosine deaminase DddA can boost the activities of single-stranded DNA-specific deaminases (TadA8e or APOBEC3A) on double-stranded DNA. We analysed A•T-to-G•C editing efficiencies in a ß-glucuronidase reporter system and showed precise adenine editing in genomic regions with high product purity in rice protoplasts. Furthermore, we have successfully regenerated rice plants with A•T-to-G•C mutations in the chloroplast genome using TALE-ABE. Consequently, TALE-adenine base editors provide alternatives for crop improvement and gene therapy by editing nuclear or organellar genomes.

Celebrating Professor Rajeev K. Varshney's transformative research odyssey from genomics to the field on his induction as Fellow of the Royal Society.

Professor Rajeev K. Varshney's transformative impact on crop genomics, genetics, and agriculture is the result of his passion, dedication, and unyielding commitment to harnessing the potential of genomics to address the most pressing challenges faced by the global agricultural community. Starting from a small town in India and reaching the global stage, Professor Varshney's academic and professional trajectory has inspired many scientists active in research today. His ground-breaking work, especially his effort to list orphan tropical crops to genomic resource-rich entities, has been transformative. Beyond his scientific achievements, Professor Varshney is recognized by his colleagues as an exemplary mentor, fostering the growth of future researchers, building institutional capacity, and strengthening scientific capability. His focus on translational genomics and strengthening seed system in developing countries for the improvement of agriculture has made a tangible impact on farmers' lives. His skills have been best utilized in roles at leading research centres where he has applied his expertise to deliver a new vision for crop improvement. These efforts have now been recognized by the Royal Society with the award of the Fellowship (FRS). As we mark this significant milestone in his career, we not only celebrate Professor Varshney's accomplishments but also his wider contributions that continue to transform the agricultural landscape.

Prime editing: Mechanism insight and recent applications in plants.

Prime editing (PE) technology utilizes an extended prime editing guide RNA (pegRNA) to direct a fusion peptide consisting of nCas9 (H840) and reverse transcriptase (RT) to a specific location in the genome. This enables the installation of base changes at the targeted site using the extended portion of the pegRNA through RT activity. The resulting product of the RT reaction forms a 3' flap, which can be incorporated into the genomic site through a series of biochemical steps involving DNA repair and synthesis pathways. PE has demonstrated its effectiveness in achieving almost all forms of precise gene editing, such as base conversions (all types), DNA sequence insertions and deletions, chromosomal translocation and inversion and long DNA sequence insertion at safe harbour sites within the genome. In plant science, PE could serve as a groundbreaking tool for precise gene editing, allowing the creation of desired alleles to improve crop varieties. Nevertheless, its application has encountered limitations due to efficiency constraints, particularly in dicotyledonous plants. In this review, we discuss the step-by-step mechanism of PE, shedding light on the critical aspects of each step while suggesting possible solutions to enhance its efficiency. Additionally, we present an overview of recent advancements and future perspectives in PE research specifically focused on plants, examining the key technical considerations of its applications.

Genome optimization via virtual simulation to accelerate maize hybrid breeding.

The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.

A CRISPR/Cas9-based vector system enables the fast breeding of selection-marker-free canola with Rcr1-rendered clubroot resistance.

Breeding for disease resistance in major crops is of crucial importance for global food security and sustainability. However, common biotechnologies such as traditional transgenesis or genome editing do not provide an ideal solution, whereas transgenic crops free of selection markers such as cisgenic/intragenic crops might be suitable. In this study, after cloning and functional verification of the Rcr1 gene for resistance to clubroot (Plasmodiophora brassicae), we confirmed that the genes Rcr1, Rcr2, Rcr4, and CRa from Brassica rapa crops and the resistance gene from B. napus oilseed rape cv. 'Mendel' on chromosome A03 were identical in their coding regions. We also determined that Rcr1 has a wide distribution in Brassica breeding materials and renders potent resistance against multiple representative clubroot strains in Canada. We then modified a CRISPR/Cas9-based cisgenic vector system and found that it enabled the fast breeding of selection-marker-free transgenic crops with add-on traits, with selection-marker-free canola (B. napus) germplasms with Rcr1-rendered stable resistance to clubroot disease being successfully developed within 2 years. In the B. napus background, the intragenic vector system was able to remove unwanted residue sequences from the final product with high editing efficiency, and off-target mutations were not detected. Our study demonstrates the potential of applying this breeding strategy to other crops that can be transformed by Agrobacterium. Following the streamlined working procedure, intragenic germplasms can be developed within two generations, which could significantly reduce the breeding time and labor compared to traditional introgression whilst still achieving comparable or even better breeding results.

Breeding for water-use efficiency in wheat: progress, challenges and prospects.

Drought poses a significant challenge to wheat production globally, leading to substantial yield losses and affecting various agronomic and physiological traits. The genetic route offers potential solutions to improve water-use efficiency (WUE) in wheat and mitigate the negative impacts of drought stress. Breeding for drought tolerance involves selecting desirable plants such as efficient water usage, deep root systems, delayed senescence, and late wilting point. Biomarkers, automated and high-throughput techniques, and QTL genes are crucial in enhancing breeding strategies and developing wheat varieties with improved resilience to water scarcity. Moreover, the role of root system architecture (RSA) in water-use efficiency is vital, as roots play a key role in nutrient and water uptake. Genetic engineering techniques offer promising avenues to introduce desirable RSA traits in wheat to enhance drought tolerance. These technologies enable targeted modifications in DNA sequences, facilitating the development of drought-tolerant wheat germplasm. The article highlighted the techniques that could play a role in mitigating drought stress in wheat.

Phloem and Xylem Responses Are Both Implicated in Huanglongbing Tolerance of Sugar Belle.

Although huanglongbing (HLB) is a devastating citrus disease, improved tolerant cultivars, such as Sugar Belle (SB) mandarin, have been identified. To understand the responses that HLB-affected SB undergoes, we compared 14CO2 fixation, carbohydrate export, phloem callose accumulation, relative expression of plant defense activators, and anatomical changes between healthy and infected SB trees versus susceptible Pineapple (PA) sweet orange. Eight- to ten-week-old leaves of infected SB showed a 2.5-fold increase in 14CO2 fixation and a 13% decrease in 14C-carbohydrate export, whereas HLB-affected PA presented a decrease of 33 and 50%, respectively. The mean distance of a callose deposit to its closest neighbor was 36% smaller in infected SB versus healthy, whereas in HLB-affected PA, it was 33% higher. Expression of papain-like cysteine proteases (PLCPs) was upregulated in SB but downregulated in PA. Infected SB showed minor alterations in the number of xylem vessels, a 16% larger xylem vessel lumen area, and a 14% increase in the proportional area of the xylem. In contrast, PA showed a 2.4-fold increase in the xylem vessel number and a 2% increase in the proportional xylem area. Three complementary mechanisms of tolerance in SB are hypothesized: (i) increased carbohydrate availability induced by greater CO2 fixation, mild effect in carbohydrate export, and local accumulation of callose in the phloem; (ii) activation of defense response via upregulation of PLCPs, and (iii) increased investment in the xylem structure. Thus, phloem and xylem modifications seem to be involved in SB tolerance.

Attaining the promise of plant gene editing at scale.

Crop improvement relies heavily on genetic variation that arises spontaneously through mutation. Modern breeding methods are very adept at combining this genetic variation in ways that achieve remarkable improvements in plant performance. Novel traits have also been created through mutation breeding and transgenesis. The advent of gene editing, however, marks a turning point: With gene editing, synthetic variation will increasingly supplement and, in some cases, supplant the genetic variation that occurs naturally. We are still in the very early stages of realizing the opportunity provided by plant gene editing. At present, typically only one or a few genes are targeted for mutation at a time, and most mutations result in loss of gene function. New technological developments, however, promise to make it possible to perform gene editing at scale. RNA virus vectors, for example, can deliver gene-editing reagents to the germ line through infection and create hundreds to thousands of diverse mutations in the progeny of infected plants. With developmental regulators, edited somatic cells can be induced to form meristems that yield seed-producing shoots, thereby increasing throughput and shrinking timescales for creating edited plants. As these approaches are refined and others developed, they will allow for accelerated breeding, the domestication of orphan crops and the reengineering of metabolism in a more directed manner than has ever previously been possible.

The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective.

Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers' priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot's recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers' preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.