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Rebecca Nelson
School of Integrative Plant Science, Plant Pathology and Plant-Microbe Biology Section, Cornell University, Ithaca, NY 14853

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Journal article
Published: 01 October 2020 in G3 Genes|Genomes|Genetics
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Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines are a powerful genetic tool to dissect quantitative trait loci. We analyzed an introgression library of maize (Zea mays) near-isogenic lines, termed a nested near-isogenic line library for resistance to northern leaf blight caused by the fungal pathogen Setosphaeria turcica. The population was comprised of 412 BC5F4 near-isogenic lines that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing were used to define introgressions and for association analysis. Near-isogenic lines that conferred resistance and susceptibility to northern leaf blight were comprised of introgressions that overlapped known northern leaf blight quantitative trait loci. Genome-wide association analysis and stepwise regression further resolved five quantitative trait loci regions, and implicated several candidate genes, including Liguleless1, a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of liguleless1 inoculated with S. turcica showed enhanced susceptibility to northern leaf blight. In the maize nested association mapping population, leaf angle was positively correlated with resistance to northern leaf blight in five recombinant inbred line populations, and negatively correlated with northern leaf blight in four recombinant inbred line populations. This study demonstrates the power of an introgression library combined with high density marker coverage to resolve quantitative trait loci. Furthermore, the role of liguleless1 in leaf architecture and in resistance to northern leaf blight has important applications in crop improvement.

ACS Style

Judith M. Kolkman; Josh Strable; Kate Harline; Dallas E. Kroon; Tyr Wiesner-Hanks; Peter J. Bradbury; Rebecca J. Nelson. Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight. G3 Genes|Genomes|Genetics 2020, 10, 3611 -3622.

AMA Style

Judith M. Kolkman, Josh Strable, Kate Harline, Dallas E. Kroon, Tyr Wiesner-Hanks, Peter J. Bradbury, Rebecca J. Nelson. Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight. G3 Genes|Genomes|Genetics. 2020; 10 (10):3611-3622.

Chicago/Turabian Style

Judith M. Kolkman; Josh Strable; Kate Harline; Dallas E. Kroon; Tyr Wiesner-Hanks; Peter J. Bradbury; Rebecca J. Nelson. 2020. "Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight." G3 Genes|Genomes|Genetics 10, no. 10: 3611-3622.

Journal article
Published: 22 May 2020 in Food Control
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The widespread contamination of foods by mycotoxins continues to be a public health hazard in sub-Saharan Africa, with maize and groundnut being major sources of contamination. This study was undertaken to assess the hypothesis that grain sorting can be used to reduce mycotoxin contamination in grain lots by removing toxic kernels. We tested a set of sorting principles and methods for reducing mycotoxin levels in maize and groundnut from a variety of genotypes and environments. We found that kernel bulk density (KBD) and 100-kernel weight (HKW) were associated with the levels of aflatoxins (AF) and fumonisins (FUM) in maize grain. A low-cost sorter prototype (the ‘DropSort’ device) that separated maize grain based on KBD and HKW was more effective in reducing FUM than AF. We then evaluated the effectiveness of DropSorting when combined with either size or visual sorting. Size sorting followed by DropSorting was the fastest method for reducing FUM to under 2 ppm, but was not effective in reducing AF levels in maize grain to under 20 ppb, especially for heavily AF-contaminated grain. Analysis of individual kernels showed that high -AF maize kernels had lower weight, volume, density, length, and width and higher sphericity than those with low AF. Single kernel weight was the most significant predictor of AF concentration. The DropSort excluded kernels with lower single kernel weight, volume, width, depth, and sphericity. We also found that visual sorting and bright greenish-yellow fluorescence sorting of maize single kernels were successful in separating kernels based on AF levels. For groundnut, the DropSort grouped grain based on HKW and did not significantly reduce AF concentrations, whereas size sorting and visual sorting were much more effective.

ACS Style

Meriem Aoun; William Stafstrom; Paige Priest; John Fuchs; Gary L. Windham; W. Paul Williams; Rebecca J. Nelson. Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut. Food Control 2020, 118, 107363 .

AMA Style

Meriem Aoun, William Stafstrom, Paige Priest, John Fuchs, Gary L. Windham, W. Paul Williams, Rebecca J. Nelson. Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut. Food Control. 2020; 118 ():107363.

Chicago/Turabian Style

Meriem Aoun; William Stafstrom; Paige Priest; John Fuchs; Gary L. Windham; W. Paul Williams; Rebecca J. Nelson. 2020. "Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut." Food Control 118, no. : 107363.

Journal article
Published: 30 April 2020 in Food Policy
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Like many other quality attributes, food safety tends to degrade after harvest, but unlike losses in quantity and many quality attributes, food safety losses are not readily observable by market actors. This implies the absence of incentives to address food safety losses specifically. To the extent that food safety is correlated with valued and observable food attributes, food safety losses may affect price indirectly, partially correcting this information failure. We analyze aflatoxin, a carcinogenic fungal contaminant, and visible quality data from 1500 maize samples and associated consumer surveys collected from clients of small-scale hammer mills in rural Kenya. We find that while losses in both food safety and observable quality increase with storage duration, only observable quality is rewarded by higher prices. There is no correlation between price and aflatoxin, implying an absence of market incentives to manage this aspect of quality loss. Further, of the two observable qualities on which data were collected, the one correlated most strongly with aflatoxin affects both prices and consumers’ subjective quality assessment the least. Providing consumers with information about the correlation between visible grain attributes and contamination could allow consumers to direct contaminated grain to uses that minimize aflatoxin exposure and improve the incentives for provision of food safety in this market.

ACS Style

Vivian Hoffmann; Samuel K. Mutiga; Jagger W. Harvey; Rebecca J. Nelson; Michael G. Milgroom. Observability of food safety losses in maize: Evidence from Kenya. Food Policy 2020, 98, 101895 .

AMA Style

Vivian Hoffmann, Samuel K. Mutiga, Jagger W. Harvey, Rebecca J. Nelson, Michael G. Milgroom. Observability of food safety losses in maize: Evidence from Kenya. Food Policy. 2020; 98 ():101895.

Chicago/Turabian Style

Vivian Hoffmann; Samuel K. Mutiga; Jagger W. Harvey; Rebecca J. Nelson; Michael G. Milgroom. 2020. "Observability of food safety losses in maize: Evidence from Kenya." Food Policy 98, no. : 101895.

Resource
Published: 29 April 2020 in The Plant Journal
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Genome-wide association (GWA) studies can identify quantitative trait loci (QTL) putatively underlying traits of interest, and nested association mapping (NAM) can further assess allelic series. Near-isogenic lines (NILs) can be used to characterize, dissect and validate QTL, but the development of NILs is costly. Previous studies have utilized limited numbers of NILs and introgression donors. We characterized a panel of 1270 maize NILs derived from crosses between 18 diverse inbred lines and the recurrent inbred parent B73, referred to as the nested NILs (nNILs). The nNILs were phenotyped for flowering time, height and resistance to three foliar diseases, and genotyped with genotyping-by-sequencing. Across traits, broad-sense heritability (0.4–0.8) was relatively high. The 896 genotyped nNILs contain 2638 introgressions, which span the entire genome with substantial overlap within and among allele donors. GWA with the whole panel identified 29 QTL for height and disease resistance with allelic variation across donors. To date, this is the largest and most diverse publicly available panel of maize NILs to be phenotypically and genotypically characterized. The nNILs are a valuable resource for the maize community, providing an extensive collection of introgressions from the founders of the maize NAM population in a B73 background combined with data on six agronomically important traits and from genotyping-by-sequencing. We demonstrate that the nNILs can be used for QTL mapping and allelic testing. The majority of nNILs had four or fewer introgressions, and could readily be used for future fine mapping studies.

ACS Style

Laura Morales; A. C. Repka; Kelly L. Swarts; William C. Stafstrom; Yijian He; Shannon M. Sermons; Qin Yang; Luis O. Lopez‐Zuniga; Elizabeth Rucker; Wade E. Thomason; Rebecca J. Nelson; Peter J. Balint‐Kurti. Genotypic and phenotypic characterization of a large, diverse population of maize near‐isogenic lines. The Plant Journal 2020, 103, 1246 -1255.

AMA Style

Laura Morales, A. C. Repka, Kelly L. Swarts, William C. Stafstrom, Yijian He, Shannon M. Sermons, Qin Yang, Luis O. Lopez‐Zuniga, Elizabeth Rucker, Wade E. Thomason, Rebecca J. Nelson, Peter J. Balint‐Kurti. Genotypic and phenotypic characterization of a large, diverse population of maize near‐isogenic lines. The Plant Journal. 2020; 103 (3):1246-1255.

Chicago/Turabian Style

Laura Morales; A. C. Repka; Kelly L. Swarts; William C. Stafstrom; Yijian He; Shannon M. Sermons; Qin Yang; Luis O. Lopez‐Zuniga; Elizabeth Rucker; Wade E. Thomason; Rebecca J. Nelson; Peter J. Balint‐Kurti. 2020. "Genotypic and phenotypic characterization of a large, diverse population of maize near‐isogenic lines." The Plant Journal 103, no. 3: 1246-1255.

Research article
Published: 01 February 2020 in Phytopathology®
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The challenge of feeding the current and future world population is widely recognized, and the management of plant diseases has an important role in overcoming this. This paper explores the ways in which international plant pathology has contributed and continues to support efforts to secure adequate, safe and culturally appropriate nourishment and livelihoods for present and future generations. For the purposes of this paper, “international plant pathology” refers to the work that plant pathologists do when they work across international borders, with a focus on enhancing food security in tropical regions. Significant efforts involve public and philanthropic resources from the global North for addressing plant disease concerns in the global South, where food security is a legitimate and pressing concern. International disease management efforts are also aimed at protecting domestic food security, for example when pathogens of major staples migrate across national borders. In addition, some important crops are largely produced in tropical countries and consumed globally, including in industrialized countries; the diseases of these crops are of international interest, and they are largely managed by the private sector. Finally, host−microbe interactions are fascinating biological systems, and basic research on plant diseases of international relevance has often yielded insights and technologies with both scientific and practical implications.

ACS Style

Rebecca Nelson. International Plant Pathology: Past and Future Contributions to Global Food Security. Phytopathology® 2020, 110, 245 -253.

AMA Style

Rebecca Nelson. International Plant Pathology: Past and Future Contributions to Global Food Security. Phytopathology®. 2020; 110 (2):245-253.

Chicago/Turabian Style

Rebecca Nelson. 2020. "International Plant Pathology: Past and Future Contributions to Global Food Security." Phytopathology® 110, no. 2: 245-253.

Journal article
Published: 08 January 2020 in Food Policy
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ACS Style

Rebecca Nelson. Viewpoint: International agriculture’s needed shift from energy intensification to agroecological intensification. Food Policy 2020, 91, 101815 .

AMA Style

Rebecca Nelson. Viewpoint: International agriculture’s needed shift from energy intensification to agroecological intensification. Food Policy. 2020; 91 ():101815.

Chicago/Turabian Style

Rebecca Nelson. 2020. "Viewpoint: International agriculture’s needed shift from energy intensification to agroecological intensification." Food Policy 91, no. : 101815.

Preprint content
Published: 24 October 2019
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Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines (NILs) are a powerful genetic tool to dissect quantitative trait loci (QTL). We analyzed an introgression library of maize near-isogenic lines (NILs), termed a nested NIL (nNIL) library for resistance to northern leaf blight (NLB) caused by the fungal pathogenSetosphaeria turcica. The nNIL library was comprised of 412 BC5F4NILs that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing (GBS) were used to define introgressions and for association analysis. NILs that conferred resistance and susceptibility to NLB were comprised of introgressions that overlapped known NLB QTL. Genome-wide association analysis and stepwise regression further resolved five QTL regions, and implicated several candidate genes, includingLiguleless1(Lg1), a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles oflg1inoculated withS. turcicashowed enhanced susceptibility to NLB. In the maize nested association mapping population, leaf angle was positively correlated with NLB in five recombinant inbred line (RIL) populations, and negatively correlated with NLB in four RIL populations. This study demonstrates the power of a nNIL library combined with high density SNP coverage to resolve QTLs. Furthermore, the role oflg1in leaf architecture and in resistance to NLB has important applications in crop improvement.Significance Statement (120 words)Understanding the genetic basis of disease resistance is important for crop improvement. We analyzed response to northern leaf blight (NLB) in a maize population consisting of 412 near-isogenic lines (NILs) derived from 18 diverse donor parents backcrossed to a recurrent parent, B73. NILs were genotyped by sequencing to detect introgressed segments. We identified NILs with greater resistance or susceptibility to NLB than B73. Genome-wide association analysis, coupled with stepwise regression, identified 5 candidate loci for NLB resistance, including theliguleless1gene. The LIGULELESS1 transcription factor is critical in development of the leaf ligular region and influences leaf angle. We found thatliguleless1mutants are significantly more susceptible to NLB, uncovering a pleiotropic role forliguleless1in development and disease resistance.

ACS Style

Judith M. Kolkman; Josh Strable; Kate Harline; Dallas E. Kroon; Tyr Wiesner-Hanks; Peter J. Bradbury; Rebecca J. Nelson. Maize nested introgression library provides evidence for the involvement ofliguleless1in resistance to northern leaf blight. 2019, 818518 .

AMA Style

Judith M. Kolkman, Josh Strable, Kate Harline, Dallas E. Kroon, Tyr Wiesner-Hanks, Peter J. Bradbury, Rebecca J. Nelson. Maize nested introgression library provides evidence for the involvement ofliguleless1in resistance to northern leaf blight. . 2019; ():818518.

Chicago/Turabian Style

Judith M. Kolkman; Josh Strable; Kate Harline; Dallas E. Kroon; Tyr Wiesner-Hanks; Peter J. Bradbury; Rebecca J. Nelson. 2019. "Maize nested introgression library provides evidence for the involvement ofliguleless1in resistance to northern leaf blight." , no. : 818518.

Technical note
Published: 21 September 2019 in Remote Sensing
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Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

ACS Style

Ethan L. Stewart; Tyr Wiesner-Hanks; Nicholas Kaczmar; Chad DeChant; Harvey Wu; Hod Lipson; Rebecca J. Nelson; Michael A. Gore. Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sensing 2019, 11, 2209 .

AMA Style

Ethan L. Stewart, Tyr Wiesner-Hanks, Nicholas Kaczmar, Chad DeChant, Harvey Wu, Hod Lipson, Rebecca J. Nelson, Michael A. Gore. Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning. Remote Sensing. 2019; 11 (19):2209.

Chicago/Turabian Style

Ethan L. Stewart; Tyr Wiesner-Hanks; Nicholas Kaczmar; Chad DeChant; Harvey Wu; Hod Lipson; Rebecca J. Nelson; Michael A. Gore. 2019. "Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning." Remote Sensing 11, no. 19: 2209.

Journal article
Published: 01 February 2019 in Toxins
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The fungus Fusarium verticillioides can infect maize ears, causing Fusarium ear rot (FER) and contaminating the grain with fumonisins (FUM), which are harmful to humans and animals. Breeding for resistance to FER and FUM and post-harvest sorting of grain are two strategies for reducing FUM in the food system. Kernel and cob tissues have been previously associated with differential FER and FUM. Four recombinant inbred line families from the maize nested associated mapping population were grown and inoculated with F. verticillioides across four environments, and we evaluated the kernels for external and internal infection severity as well as FUM contamination. We also employed publicly available phenotypes on innate ear morphology to explore genetic relationships between ear architecture and resistance to FER and FUM. The four families revealed wide variation in external symptomatology at the phenotypic level. Kernel bulk density under inoculation was an accurate indicator of FUM levels. Genotypes with lower kernel density—under both inoculated and uninoculated conditions—and larger cobs were more susceptible to infection and FUM contamination. Quantitative trait locus (QTL) intervals could be classified as putatively resistance-specific and putatively shared for ear and resistance traits. Both types of QTL mapped in this study had substantial overlap with previously reported loci for resistance to FER and FUM. Ear morphology may be a component of resistance to F. verticillioides infection and FUM accumulation.

ACS Style

Laura Morales; Charles T. Zila; Danilo E. Moreta Mejía; Melissa Montoya Arbelaez; Peter J. Balint-Kurti; James B. Holland; Rebecca J. Nelson. Diverse Components of Resistance to Fusarium verticillioides Infection and Fumonisin Contamination in Four Maize Recombinant Inbred Families. Toxins 2019, 11, 86 .

AMA Style

Laura Morales, Charles T. Zila, Danilo E. Moreta Mejía, Melissa Montoya Arbelaez, Peter J. Balint-Kurti, James B. Holland, Rebecca J. Nelson. Diverse Components of Resistance to Fusarium verticillioides Infection and Fumonisin Contamination in Four Maize Recombinant Inbred Families. Toxins. 2019; 11 (2):86.

Chicago/Turabian Style

Laura Morales; Charles T. Zila; Danilo E. Moreta Mejía; Melissa Montoya Arbelaez; Peter J. Balint-Kurti; James B. Holland; Rebecca J. Nelson. 2019. "Diverse Components of Resistance to Fusarium verticillioides Infection and Fumonisin Contamination in Four Maize Recombinant Inbred Families." Toxins 11, no. 2: 86.

Journal article
Published: 09 July 2018 in BMC Research Notes
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Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

ACS Style

Naser AlKhalifah; Darwin A. Campbell; Celeste M. Falcon; Jack M. Gardiner; Nathan D. Miller; Maria Cinta Romay; Ramona Walls; Renee Walton; Cheng-Ting Yeh; Martin Bohn; Jessica Bubert; Edward S. Buckler; Ignacio Ciampitti; Sherry Flint-Garcia; Michael A. Gore; Christopher Graham; Candice Hirsch; James B. Holland; David Hooker; Shawn Kaeppler; Joseph Knoll; Nick Lauter; Elizabeth C. Lee; Aaron Lorenz; Jonathan P. Lynch; Stephen P. Moose; Seth C. Murray; Rebecca Nelson; Torbert Rocheford; Oscar Rodriguez; James C. Schnable; Brian Scully; Margaret Smith; Nathan Springer; Peter Thomison; Mitchell Tuinstra; Randall J. Wisser; Wenwei Xu; David Ertl; Patrick S. Schnable; Natalia De Leon; Edgar P. Spalding; Jode Edwards; Carolyn J. Lawrence-Dill. Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes 2018, 11, 452 .

AMA Style

Naser AlKhalifah, Darwin A. Campbell, Celeste M. Falcon, Jack M. Gardiner, Nathan D. Miller, Maria Cinta Romay, Ramona Walls, Renee Walton, Cheng-Ting Yeh, Martin Bohn, Jessica Bubert, Edward S. Buckler, Ignacio Ciampitti, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice Hirsch, James B. Holland, David Hooker, Shawn Kaeppler, Joseph Knoll, Nick Lauter, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, Oscar Rodriguez, James C. Schnable, Brian Scully, Margaret Smith, Nathan Springer, Peter Thomison, Mitchell Tuinstra, Randall J. Wisser, Wenwei Xu, David Ertl, Patrick S. Schnable, Natalia De Leon, Edgar P. Spalding, Jode Edwards, Carolyn J. Lawrence-Dill. Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Research Notes. 2018; 11 (1):452.

Chicago/Turabian Style

Naser AlKhalifah; Darwin A. Campbell; Celeste M. Falcon; Jack M. Gardiner; Nathan D. Miller; Maria Cinta Romay; Ramona Walls; Renee Walton; Cheng-Ting Yeh; Martin Bohn; Jessica Bubert; Edward S. Buckler; Ignacio Ciampitti; Sherry Flint-Garcia; Michael A. Gore; Christopher Graham; Candice Hirsch; James B. Holland; David Hooker; Shawn Kaeppler; Joseph Knoll; Nick Lauter; Elizabeth C. Lee; Aaron Lorenz; Jonathan P. Lynch; Stephen P. Moose; Seth C. Murray; Rebecca Nelson; Torbert Rocheford; Oscar Rodriguez; James C. Schnable; Brian Scully; Margaret Smith; Nathan Springer; Peter Thomison; Mitchell Tuinstra; Randall J. Wisser; Wenwei Xu; David Ertl; Patrick S. Schnable; Natalia De Leon; Edgar P. Spalding; Jode Edwards; Carolyn J. Lawrence-Dill. 2018. "Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets." BMC Research Notes 11, no. 1: 452.

Data note
Published: 03 July 2018 in BMC Research Notes
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Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data. This data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.

ACS Style

Tyr Wiesner-Hanks; Ethan L. Stewart; Nicholas Kaczmar; Chad DeChant; Harvey Wu; Rebecca J. Nelson; Hod Lipson; Michael A. Gore. Image set for deep learning: field images of maize annotated with disease symptoms. BMC Research Notes 2018, 11, 440 .

AMA Style

Tyr Wiesner-Hanks, Ethan L. Stewart, Nicholas Kaczmar, Chad DeChant, Harvey Wu, Rebecca J. Nelson, Hod Lipson, Michael A. Gore. Image set for deep learning: field images of maize annotated with disease symptoms. BMC Research Notes. 2018; 11 (1):440.

Chicago/Turabian Style

Tyr Wiesner-Hanks; Ethan L. Stewart; Nicholas Kaczmar; Chad DeChant; Harvey Wu; Rebecca J. Nelson; Hod Lipson; Michael A. Gore. 2018. "Image set for deep learning: field images of maize annotated with disease symptoms." BMC Research Notes 11, no. 1: 440.

Journal article
Published: 14 July 2016 in Experimental Agriculture
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SUMMARYThe agricultural research and development institutions in most developing countries are poorly equipped to support the needs of millions of smallholder farmers that depend upon them. The research approaches taken by these systems explicitly or implicitly seek simple, one-size-fits-all solutions for problems and opportunities that are extremely diverse. Radical change is needed to facilitate the agroecological intensification of smallholder farming. We propose that large-scale participatory approaches, combined with innovations in information and communications technology (ICT), could enable the effective matching of diverse options to the wide spectrum of socio-ecological context that characterize smallholder agriculture. We consider the requirements, precedents and issues that might be involved in the development of farmer research networks (FRNs). Substantial institutional innovation will be needed to support FRNs, with shifts in roles and relationships amongst researchers, extension providers and farmers. Where farmers’ organizations have social capital and strong facilitation skills, such alignments may be most feasible. Novel information management capabilities will be required to introduce options and principles, enable characterization of contexts, manage data related to option-by-context interactions and enable farmers to visualize their findings in useful and intelligible ways. FRNs could lead to vastly greater capacity for technical innovation, which could in turn enable greater productivity and resilience, and enhance the quality of rural life.

ACS Style

Rebecca Nelson; Richard Coe; Bettina I. G. Haussmann. FARMER RESEARCH NETWORKS AS A STRATEGY FOR MATCHING DIVERSE OPTIONS AND CONTEXTS IN SMALLHOLDER AGRICULTURE. Experimental Agriculture 2016, 55, 125 -144.

AMA Style

Rebecca Nelson, Richard Coe, Bettina I. G. Haussmann. FARMER RESEARCH NETWORKS AS A STRATEGY FOR MATCHING DIVERSE OPTIONS AND CONTEXTS IN SMALLHOLDER AGRICULTURE. Experimental Agriculture. 2016; 55 (S1):125-144.

Chicago/Turabian Style

Rebecca Nelson; Richard Coe; Bettina I. G. Haussmann. 2016. "FARMER RESEARCH NETWORKS AS A STRATEGY FOR MATCHING DIVERSE OPTIONS AND CONTEXTS IN SMALLHOLDER AGRICULTURE." Experimental Agriculture 55, no. S1: 125-144.

Journal article
Published: 09 July 2014 in Genetics
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Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line “Tx303” conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart’s wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart’s wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and “B73,” the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart’s wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.

ACS Style

Tiffany Jamann; Jesse Poland; Judith M Kolkman; Laurie G Smith; Rebecca J Nelson. Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize. Genetics 2014, 198, 333 -344.

AMA Style

Tiffany Jamann, Jesse Poland, Judith M Kolkman, Laurie G Smith, Rebecca J Nelson. Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize. Genetics. 2014; 198 (1):333-344.

Chicago/Turabian Style

Tiffany Jamann; Jesse Poland; Judith M Kolkman; Laurie G Smith; Rebecca J Nelson. 2014. "Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize." Genetics 198, no. 1: 333-344.

Journal article
Published: 11 April 2011 in Proceedings of the National Academy of Sciences
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Quantitative resistance to plant pathogens, controlled by multiple loci of small effect, is important for food production, food security, and food safety but is poorly understood. To gain insights into the genetic architecture of quantitative resistance in maize, we evaluated a 5,000-inbred-line nested association mapping population for resistance to northern leaf blight, a maize disease of global economic importance. Twenty-nine quantitative trait loci were identified, and most had multiple alleles. The large variation in resistance phenotypes could be attributed to the accumulation of numerous loci of small additive effects. Genome-wide nested association mapping, using 1.6 million SNPs, identified multiple candidate genes related to plant defense, including receptor-like kinase genes similar to those involved in basal defense. These results are consistent with the hypothesis that quantitative disease resistance in plants is conditioned by a range of mechanisms and could have considerable mechanistic overlap with basal resistance.

ACS Style

Jesse Poland; P. J. Bradbury; Edward Buckler; R. J. Nelson. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proceedings of the National Academy of Sciences 2011, 108, 6893 -6898.

AMA Style

Jesse Poland, P. J. Bradbury, Edward Buckler, R. J. Nelson. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proceedings of the National Academy of Sciences. 2011; 108 (17):6893-6898.

Chicago/Turabian Style

Jesse Poland; P. J. Bradbury; Edward Buckler; R. J. Nelson. 2011. "Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize." Proceedings of the National Academy of Sciences 108, no. 17: 6893-6898.

Journal article
Published: 09 March 2010 in Theoretical and Applied Genetics
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As part of a larger effort to capture diverse alleles at a set of loci associated with disease resistance in maize, DK888, a hybrid known to possess resistance to multiple diseases, was used as a donor in constructing near-isogenic lines (NILs). A NIL pair contrasting for resistance to northern leaf blight (NLB), caused by Setosphaeria turcica, was identified and associated with bin 8.06. This region of the maize genome had been associated in previous studies with both qualitative and quantitative resistance to NLB. In addition, bins 8.05–8.06 had been associated with quantitative resistance to several other diseases, as well as resistance gene analogs and defense response gene homologs. To test the hypothesis that the DK888 allele at bin 8.06 (designated qNLB8.06 DK888 ) conditions the broad-spectrum quantitative resistance characteristic of the donor, the NILs were evaluated with a range of maize pathogens and different races of S. turcica. The results revealed that qNLB8.06 DK888 confers race-specific resistance exclusively to NLB. Allelism analysis suggested that qNLB8.06 DK888 is identical, allelic, or closely linked and functionally related to Ht2. The resistance conditioned by qNLB8.06 was incompletely dominant and varied in effectiveness depending upon allele and/or genetic background. High-resolution breakpoint analysis, using ~2,800 individuals in F9/F10 heterogeneous inbred families and 98 F10/F11 fixed lines carrying various recombinant events, delimited qNLB8.06 DK888 to a region of ~0.46 Mb, spanning 143.92–144.38 Mb on the B73 physical map. Three compelling candidate genes were identified in this region. Isolation of the gene(s) will contribute to better understanding of this complex locus.

ACS Style

Chia-Lin Chung; Tiffany Jamann; Joy Longfellow; Rebecca Nelson. Characterization and fine-mapping of a resistance locus for northern leaf blight in maize bin 8.06. Theoretical and Applied Genetics 2010, 121, 205 -227.

AMA Style

Chia-Lin Chung, Tiffany Jamann, Joy Longfellow, Rebecca Nelson. Characterization and fine-mapping of a resistance locus for northern leaf blight in maize bin 8.06. Theoretical and Applied Genetics. 2010; 121 (2):205-227.

Chicago/Turabian Style

Chia-Lin Chung; Tiffany Jamann; Joy Longfellow; Rebecca Nelson. 2010. "Characterization and fine-mapping of a resistance locus for northern leaf blight in maize bin 8.06." Theoretical and Applied Genetics 121, no. 2: 205-227.

Journal article
Published: 01 January 2010 in BMC Plant Biology
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Studies on host-pathogen interactions in a range of pathosystems have revealed an array of mechanisms by which plants reduce the efficiency of pathogenesis. While R-gene mediated resistance confers highly effective defense responses against pathogen invasion, quantitative resistance is associated with intermediate levels of resistance that reduces disease progress. To test the hypothesis that specific loci affect distinct stages of fungal pathogenesis, a set of maize introgression lines was used for mapping and characterization of quantitative trait loci (QTL) conditioning resistance to Setosphaeria turcica, the causal agent of northern leaf blight (NLB). To better understand the nature of quantitative resistance, the identified QTL were further tested for three secondary hypotheses: (1) that disease QTL differ by host developmental stage; (2) that their performance changes across environments; and (3) that they condition broad-spectrum resistance.

ACS Style

Chia-Lin Chung; Joy M. Longfellow; Ellie K. Walsh; Zura Kerdieh; George Van Esbroeck; Peter Balint-Kurti; Rebecca J. Nelson. Resistance loci affecting distinct stages of fungal pathogenesis: use of introgression lines for QTL mapping and characterization in the maize - Setosphaeria turcica pathosystem. BMC Plant Biology 2010, 10, 103 -103.

AMA Style

Chia-Lin Chung, Joy M. Longfellow, Ellie K. Walsh, Zura Kerdieh, George Van Esbroeck, Peter Balint-Kurti, Rebecca J. Nelson. Resistance loci affecting distinct stages of fungal pathogenesis: use of introgression lines for QTL mapping and characterization in the maize - Setosphaeria turcica pathosystem. BMC Plant Biology. 2010; 10 (1):103-103.

Chicago/Turabian Style

Chia-Lin Chung; Joy M. Longfellow; Ellie K. Walsh; Zura Kerdieh; George Van Esbroeck; Peter Balint-Kurti; Rebecca J. Nelson. 2010. "Resistance loci affecting distinct stages of fungal pathogenesis: use of introgression lines for QTL mapping and characterization in the maize - Setosphaeria turcica pathosystem." BMC Plant Biology 10, no. 1: 103-103.

Review article
Published: 31 January 2009 in Trends in Plant Science
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A thorough understanding of quantitative disease resistance (QDR) would contribute to the design and deployment of durably resistant crop cultivars. However, the molecular mechanisms that control QDR remain poorly understood, largely due to the incomplete and inconsistent nature of the resistance phenotype, which is usually conditioned by many loci of small effect. Here, we discuss recent advances in research on QDR. Based on inferences from analyses of the defense response and from the few isolated QDR genes, we suggest several plausible hypotheses for a range of mechanisms underlying QDR. We propose that a new generation of genetic resources, complemented by careful phenotypic analysis, will produce a deeper understanding of plant defense and more effective utilization of natural resistance alleles.

ACS Style

Jesse Poland; Peter Balint-Kurti; Randall J. Wisser; Richard C. Pratt; Rebecca J. Nelson. Shades of gray: the world of quantitative disease resistance. Trends in Plant Science 2009, 14, 21 -29.

AMA Style

Jesse Poland, Peter Balint-Kurti, Randall J. Wisser, Richard C. Pratt, Rebecca J. Nelson. Shades of gray: the world of quantitative disease resistance. Trends in Plant Science. 2009; 14 (1):21-29.

Chicago/Turabian Style

Jesse Poland; Peter Balint-Kurti; Randall J. Wisser; Richard C. Pratt; Rebecca J. Nelson. 2009. "Shades of gray: the world of quantitative disease resistance." Trends in Plant Science 14, no. 1: 21-29.

Journal article
Published: 01 April 2005 in Genetics
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Much research has been devoted to understanding the biology of plant-pathogen interactions. The extensive genetic analysis of disease resistance in rice, coupled with the sequenced genome and genomic resources, provides the opportunity to seek convergent evidence implicating specific chromosomal segments and genes in the control of resistance. Published data on quantitative and qualitative disease resistance in rice were synthesized to evaluate the distributions of and associations among resistance loci. Quantitative trait loci (QTL) for resistance to multiple diseases and qualitative resistance loci (R genes) were clustered in the rice genome. R genes and their analogs of the nucleotide binding site–leucine-rich repeat class and genes identified on the basis of differential representation in disease-related EST libraries were significantly associated with QTL. Chromosomal segments associated with broad-spectrum quantitative disease resistance (BS-QDR) were identified. These segments contained numerous positional candidate genes identified on the basis of a range of criteria, and groups of genes belonging to two defense-associated biochemical pathways were found to underlie one BS-QDR region. Genetic dissection of disease QTL confidence intervals is needed to reduce the number of positional candidate genes for further functional analysis. This study provides a framework for future investigations of disease resistance in rice and related crop species.

ACS Style

Randall J. Wisser; Qi Sun; Scot H. Hulbert; Stephen Kresovich; Rebecca J. Nelson. Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance. Genetics 2005, 169, 2277 -2293.

AMA Style

Randall J. Wisser, Qi Sun, Scot H. Hulbert, Stephen Kresovich, Rebecca J. Nelson. Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance. Genetics. 2005; 169 (4):2277-2293.

Chicago/Turabian Style

Randall J. Wisser; Qi Sun; Scot H. Hulbert; Stephen Kresovich; Rebecca J. Nelson. 2005. "Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance." Genetics 169, no. 4: 2277-2293.