Traditional freezing damage quantification on plants often relies on human visual assessment, which can be inconsistent between individuals and even for the same person at different times. Additionally, this approach is time-consuming and labor-intensive. A computer system and application utilizing computer vision-based freezing-damage quantification artificial intelligence models have the potential to significantly streamline and improve the consistency of freezing damage assessment. This study focuses on the development of lightweight deep learning models with reduced numbers of parameters for freezing damage quantification. These models utilize semantic segmentation techniques to accurately classify and quantify background, dead tissue, and healthy tissue in plant images. Several deep learning image segmentation architectures, including DeepLabv3, UPerNet, PSPNet, U-Net, and SegNet, along with modification of UperNetResNet101 with attention mechanism and backbone architectures (densenet, efficientnet, convNeXt, and mobilenetv3) were trained on a greenhouse collected strawberry dataset. The UperNet model modified with a densenet backbone achieved the highest mean Intersection Over Union value of 0.77, surpassing the performance of the base model (0.76). Notably, the less complex ConvNeXt-tiny, model, which has 65.8% fewer parameters than the UperNetResNet101 model, exhibited only a 2.63% drop in performance, while improving fps by 53.88%. This inference speed improvement can be attributed to a combination of factors, including variations in model complexity, model hardware compatibility, and efficient dataflow in model. Overall, testing of this AI-based model demonstrated its potential for freezing damage quantification in plant species beyond strawberries.

Camelina [Camelina sativa (L.) Crantz] is an oilseed of interest as a feedstock for sustainable aviation fuels due to its low carbon intensity. Camelina is reported to have salinity tolerance and able to establish in marginal lands but there is no knowledge on how spring and winter biotypes tolerate exposure to different salts and salt concentrations. The objective of this study was to determine seed germination and vigor of spring camelina (C046) and winter camelina (Joelle) under salinity and sodicity. A set of 50 seeds and a subset of 15 seeds were germinated in Petri dishes saturated with of NaCl, CaCl2, and Na2SO4 solutions at concentrations of 0, 40, 80, 120, and 160 mM L-1, in an incubator set to a constant temperature of 20 degrees C. The experimental design was a randomized complete block with three replicates. Germinated seeds were counted daily for 7 days. With the exception of seedling dry weight and hypocotyl length, the winter biotype of camelina produced significantly lower values for measured parameters than the spring biotype. Averaged across varieties and salt concentrations, Na2SO4 reduced germination, vigor, and seedling dry weight more than NaCl and CaCl2. In addition, Na2SO4 almost completely inhibited radicle and hypocotyl growth at concentrations > 80 mM L-1. This is of significance, because Na2SO4 is commonly present in sodic soils in the northern Great Plains and sodium is known to disrupt soil structure and reduce water infiltration, which can inhibit root growth. Results from future studies using an advanced and genotyped recombinant inbred line (RIL) population from a cross between Joelle and C046 will help to identify loci and candidate genes associated with salinity tolerance, and provide breeders and genetic engineers knowledge for improving salinity tolerance in camelina.

Spring wheat (Triticum aestivum L.) yields in continental cropping systems of the U.S. Midwest have shown a consistent upward trajectory over the past century due to successful breeding efforts and improvements in crop management. However, the looming threat of increasingly extreme temperature trends and the rising evaporative demand (vapor pressure deficit, VPD) during the cropping season in that region may require adaptive water use strategies to maintain or further increase wheat yield potential. Therefore, our objective was to investigate whether the observed increase in yield over the last 122 years coincides with a shift in plant water use strategies, i.e., the transpiration rate (TR) sensitivity to rising VPD.In this study, we selected 15 spring wheat genotypes from Minnesota with a year of release (YOR) spanning from 1898 to 2020 to capture the genetic yield gains achieved during that period by the local breeding program. We tested plant transpiration rate in response to rising VPD ranging from 0.5 to 2.8 kPa in a climate chamber in wet soil and potted conditions. Additionally, we measured several traits that capture plant hydraulic properties, including stomatal conductance, plant hydraulic conductance, leaf area, above- and belowground biomass, and stomatal morphological properties.Our investigation revealed that at a critical VPD beyond 1.83 ± 0.17 kPa, a significant portion of the tested genotypes expressed a limited increase in TR with increasing VPD, indicating a decline in stomatal conductance. No discernible correlation was observed between parameters characterizing plant water use strategies or the plant hydraulic system and YOR over the whole 122-year window. However, a moving window analysis unveiled that post the green revolution (around 1960 ± 15 years), breeding for yield indirectly favored less hydraulically conductive plants with a reduced leaf area and a linearization of the transpiration rate response to increasing VPD, as evidenced by a decreasing difference in slopes beyond a critical VPD. This resulted in a less pronounced reduction in water use due to a restricted TR response to increasing VPD and, thus, a lower sensitivity to rising VPD. Our study indicates that hydraulic traits such as the TR sensitivity to VPD might have been under the control of a cryptic selection mechanism by breeders as they increased wheat yield potential in the region, at least from the 1960s onwards. This points to the promising possibility of using such traits to improve yields under drier climates.

Flowering time is an important agronomic trait for canola breeders, as it provides growers with options for minimizing exposure to heat stress during flowering and to more effectively utilize soil moisture. Plants have evolved various systems to control seasonal rhythms in reproductive phenology including an internal circadian clock that responds to environmental signals. In this study, we used canola cultivar ‘Westar’ as a recurrent parent and canola cultivar ‘Surpass 400’ as the donor parent to generate a chromosome segment substitution line (CSSL) and to map a flowering time locus on chromosome A10 using molecular marker-assisted selection. This CSSL contains an introgressed 4.6 mega-bases (Mb) segment (between 13 and 17.6 Mb) of Surpass 400, which substantially delayed flowering compared with Westar. To map flowering time gene(s) within this locus, eight introgression lines (ILs) were developed carrying a series of different lengths of introgressed chromosome A10 segments using five co-dominant polymorphic markers located at 13.5, 14.0, 14.5, 15.0, 15.5, and 16.0 Mb. Eight ILs were crossed with Westar reciprocally and flowering time of resultant 16 F1 hybrids and parents were evaluated in a greenhouse (2021 and 2022). Four ILs (IL005, IL017, IL035, and IL013) showed delayed flowering compared to Westar (P < 0.0001), and their reciprocal crosses displayed a phenotype intermediate in flowering time of both homozygote parents. These results indicated that flowering time is partial or incomplete dominance, and the flowering time locus mapped within a 1 Mb region between two co-dominant polymorphic markers at 14.5–15.5 Mb on chromosome A10. The flowering time locus was delineated to be between 14.60 and 15.5 Mb based on genotypic data at the crossover site, and candidate genes within this region are associated with flowering time in canola and/or Arabidopsis. The co-dominant markers identified on chromosome A10 should be useful for marker assisted selection in breeding programs but will need to be validated to other breeding populations or germplasm accessions of canola.

Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction's potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.

Finding safe sprout inhibitors to mitigate premature sprouting of potato tubers during postharvest storage is critical for preserving tuber quality and marketability within the potato industry. Investigating the impact of promising sprout inhibitor treatments on molecular mechanisms and metabolic pathways that regulate tuber dormancy has wider implications, especially to find relevant biomarkers and to identify their modes of action. In this study, dormant cv. Russet Burbank tubers were treated with 1,4-dimethylnaphthalene (DMN) and methyl jasmonate (MeJa) alone or in combination to determine their effects on sprout growth. Changes in expression of genes involved in phytohormonal pathways, cell cycle, and dormancy-related processes were determined 0–21 days after treatments. Biochemical regulation associated with carbohydrate metabolism and plant stress responses were also determined. Significant sprout suppression was observed with MeJa and DMN+MeJa treatments during long-term storage. These sprout inhibitor treatments also resulted in increased abundance of transcripts associated with abscisic acid, brassinosteroids, and dormancy regulation. While transcript abundance of cytokinin and select cell cycle genes decreased, especially with DMN treatment, lower metabolic activity related to carbohydrate metabolism was observed in bud meristem tissues when compared to tuber flesh. Overall, MeJa enhanced stress protective metabolites such as phenolic acids and increased antioxidant enzyme responses in bud meristem tissues. Collectively, results of this study indicate a stress inducive mode of action of MeJa, which might have played a role in sprout suppression during long-term storage of potato tubers.

Direct competition for resources is generally considered the primary mechanism for weed-induced yield loss. A re-evaluation of physiological evidence suggests weeds initially impact crop growth and development through resource-independent interference. We suggest weed perception by crops induce a shift in crop development, before resources become limited, which ultimately reduce crop yield, even if weeds are subsequently removed. We present the mechanisms by which crops perceive and respond to weeds and discuss the technologies used to identify these mechanisms. These data lead to a fundamental paradigm shift in our understanding of how weeds reduce crop yield and suggest new research directions and opportunities to manipulate or engineer crops and cropping systems to reduce weed-induced yield losses.

Homozygosity mapping is an effective tool for detecting genomic regions responsible for a given trait when the phenotype is controlled by a limited number of dominant or co-dominant loci. Freezing tolerance is a major attribute in agricultural crops such as camelina. Previous studies indicated that freezing tolerance differences between a tolerant (Joelle) and susceptible (CO46) variety of camelina were controlled by a small number of dominant or co-dominant genes. We performed whole genome homozygosity mapping to identify markers and candidate genes responsible for freezing tolerance difference between these two genotypes. A total of 28 F3 RILs were sequenced to ∼30× coverage, and parental lines were sequenced to >30-40× coverage with Pacific Biosciences high fidelity technology and 60× coverage using Illumina whole genome sequencing. Overall, about 126k homozygous single nucleotide polymorphism markers were identified that differentiate both parents. Moreover, 617 markers were also homozygous in F3 families fixed for freezing tolerance/susceptibility. All these markers mapped to two contigs forming a contiguous stretch of chromosome 11. The homozygosity mapping detected 9 homozygous blocks among the selected markers and 22 candidate genes with strong similarity to regions in or near the homozygous blocks. Two such genes were differentially expressed during cold acclimation in camelina. The largest block contained a cold-regulated plant thionin and a putative rotamase cyclophilin 2 gene previously associated with freezing resistance in arabidopsis (Arabidopsis thaliana). The second largest block contains several cysteine-rich RLK genes and a cold-regulated receptor serine/threonine kinase gene. We hypothesize that one or more of these genes may be primarily responsible for freezing tolerance differences in camelina varieties.

Winter oilseed cash cover crops are gaining popularity in integrated weed management programs for suppressing weeds. A study was conducted at two field sites (Fargo, North Dakota, and Morris, Minnesota) to determine the freezing tolerance and weed-suppressing traits of winter canola/rapeseed (Brassica napus L.) and winter camelina [Camelina sativa (L.) Crantz] in the Upper Midwestern USA. The top 10 freezing tolerant accessions from a phenotyped population of winter canola/rapeseed were bulked and planted at both locations along with winter camelina (cv. Joelle) as a check. To phenotype our entire winter B. napus population (621 accessions) for freezing tolerance, seeds were also bulked and planted at both locations. All B. napus and camelina were no-till seeded at Fargo and Morris at two planting dates, late August (PD1) and mid-September (PD2) 2019. Data for winter survival of oilseed crops (plants m−2) and their corresponding weed suppression (plants m−2 and dry matter m−2) were collected on two sampling dates (SD) in May and June 2020. Crop and SD were significant (p < 0.05) for crop plant density at both locations, and PD in Fargo and crop x PD interaction in Morris were significant for weed dry matter. At Morris and Fargo, PD1 produced greater winter B. napus survival (28% and 5%, respectively) and PD2 produced higher camelina survival (79% and 72%, respectively). Based on coefficient of determination (r2), ~50% of weed density was explained by camelina density, whereas ≤20% was explained by B. napus density at both locations. Camelina from PD2 suppressed weed dry matter by >90% of fallow at both locations, whereas weed dry matter in B. napus was not significantly different from fallow at either PD. Genotyping of overwintering canola/rapeseed under field conditions identified nine accessions that survived at both locations, which also had excellent freezing tolerance under controlled conditions. These accessions are good candidates for improving freezing tolerance in commercial canola cultivars.

AbstractThe U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

We would like to thank Dr Colbach et al. [ 1. Colbach N. et al. Weed-induced yield loss through resource competition cannot be sidelined. Trends Plant Sci. 2023; 28: 1329-1330 Abstract Full Text Full Text PDF Scopus (1) Google Scholar ] for their rebuttal to our article [ 2. Horvath D. et al. Weed-induced crop yield loss: a new paradigm and new challenges. Trends Plant Sci. 2023; 28: 567-582 Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar ] as it provides us with an opportunity to clarify the limitations of the findings and add additional information to further support our hypothesis that competition for resources per se is not the primary mechanism by which weeds reduce crop yields. As with any new paradigm, the underlying assumptions of the paradigm should be rigorously tested. Experiments that are designed to parse out the contribution of various weed impacts to neighboring crops – specifically those that can fully compensate for and/or quantify resource availability with and without weeds are needed. Careful experimental design should help tease out the proportion of yield losses due to alterations in developmental reprograming, independent of resource availability, from those due to reduced resource availability.

AbstractWinter biotypes of rapeseed (Brassica napus L.) require a vernalization treatment to enter the reproductive phase and generally produce greater yields than spring rapeseed. To find genetic loci associated with freezing tolerance in rapeseed, we first performed genotyping‐by‐sequencing (GBS) on a diversity panel consisting of 222 rapeseed accessions originating primarily from Europe, which identified 69,554 high‐quality single‐nucleotide polymorphisms (SNPs). Model‐based cluster analysis suggested that there were eight subgroups. The diversity panel was then phenotyped for freezing survival (visual damage and Fv/Fo and Fv/Fm) after 2 months of cold acclimation (5°C) and a freezing treatment (−15°C for 4 h). The genotypic and phenotypic data for each accession in the rapeseed diversity panel was then used to conduct a genome‐wide association study (GWAS). GWAS results showed that 14 significant markers were mapped to seven chromosomes for the phenotypes scored. Twenty‐four candidate genes located within the mapped loci were identified as previously associated with lipid, photosynthesis, flowering, ubiquitination, and cytochrome P450 in rapeseed or other plant species.

NDOLA-2 (Reg. no. GP-9, PI 698655) is an open-pollinated, non-genetically modified, spring-type canola (Brassica napus L.) developed at North Dakota State University (NDSU) and approved for specialty release by the North Dakota Agricultural Experiment Station. NDOLA-2 was tested as experimental line NDC-E16198 from 2017 to 2020 (16 location-years) in NDSU canola breeding nurseries and in 2019 and 2020 (8 location-years) in North Dakota Canola Variety Trials (ND-CVTs). NDOLA-2 has been identified as a germplasm with higher seed yield potential and disease tolerance. NDOLA-2 had 5.5% higher seed yield and 2.1% lower oil contents compared with those of commercial hybrid checks in NDSU canola breeding trials. Also, NDOLA-2 had 5.4% higher seed yield and 1.6% lower oil content over the means of commercial cultivars/hybrids in ND-CVTs. It has been identified as resistant to blackleg disease (Pathogenicity Group 4) and Sclerotinia stem rot disease of canola. NDOLA-2 flowers within 44 d from seeding, has a flowering period that lasts 22 d, and reaches maturity within 93 d from seeding with plants that are 103 cm tall. It is resistant to lodging, with a score of 2.1 out of 9, and has an overall breeder score of 8.0, where the trial mean score for all locations was 7.7.

Double-cropping as a means of sustainably intensifying oilseed production could promote farmer adoption of new and alternative use oil crops like camelina [Camelina sativa (L.) Crantz]. Previous research indicated that double-cropping sunflower (Helianthus annuus L.) after winter camelina in the upper Midwest USA tended to reduce sunflower seed yield and oil content as compared with monocrop sunflower. However, new semidwarf hybrid sunflower genotypes with early maturity may be better suited for developing double-cropping systems for oil production in the upper Midwest and northern Great Plains. To evaluate seed and oil yields of winter camelina (cv. Joelle) and double-cropped sunflower compared with monocrop sunflower, a field study was conducted across two growing seasons (2017–2019) in west central Minnesota USA. One early maturing semidwarf oil sunflower hybrid, Honeycomb NS, and three common commercial full season oil hybrids were evaluated. Additionally, soil moisture to a 1-m depth was monitored during both growing seasons, which did not appear to limit production of the two crops in a single season at the study site. Although delayed sowing associated with double-cropping generally reduced sunflower seed yield, oil content, and the oleic/linoleic acid ratio compared with monocrop controls, these decreases were less for the early maturing hybrid Honeycomb NS. During one season of the study, total oil yield (winter camelina + sunflower) of double-cropped Honeycomb NS was 1.5 times greater than its monocrop counterpart. In addition to sustainably intensifying oil production, double-cropping sunflower with winter camelina might appeal to producers wanting to reduce the risk of one or the other crop failing in any given year. Nevertheless, double-cropping in the upper Midwest USA can be risky due to year-to-year variations in weather and length of growing season.

Camelina ( Camelina sativa L. Crantz) is a short-season oilseed crop of the Brassicaceae family that consists of both summer and winter annual biotypes. Winter biotypes require non-freezing cold conditions for acquiring freezing tolerance (cold acclimation) and floral initiation (vernalization). Transcriptome profiles of a summer (CO46) biotype with poor freezing tolerance after acclimation and a winter (Joelle) biotype with excellent freezing tolerance after acclimation were compared prior to and after an 8-week cold treatment to identify key molecular pathways and genes responsive to cold acclimation and vernalization and potentially associated with freezing tolerance. Gene-set enrichment analyses identified AraCyc pathways involved in photosynthesis and lipid and hormone biosynthesis that were different between the two biotypes. Sub-network enrichment analyses identified hubs of molecular networks such as circadian clock, flowering, and hormone and stress responsive genes that were likely involved in vernalization but may also overlap with cold-induced freezing tolerance. A microRNA involved in floral initiation (MIR172A) was identified as a central hub for microRNA targets among upregulated genes for Joelle post-acclimation. Combined results are generally consistent with many previously identified molecular pathways and genes acting together to control vernalization, cold acclimation, and freezing tolerance. Our research provides new insights into the regulation of cold acclimation and molecular genetic mechanisms underlying cold tolerance and floral induction for the winter biotype Joelle.

Winter canola generally produces greater yields than spring canola. However, its range is limited due to its inability to withstand the harsh winter conditions that occur in many northern regions of the U.S.A. To identify loci associated with freezing tolerance in canola, we conducted a genome-wide association study (GWAS) using a genotyped diversity panel containing 399 accessions consisting primarily of winter canola. One-month-old greenhouse grown plants were subsequently cold-acclimated for two months in an environmental growth chamber prior to phenotyping for freezing survival using a visual damage scale and chlorophyll fluorescence (Fv/Fo). There was reasonable correlation observed between visual damage and chlorophyll fluorescence ratings among the top associated loci; the results indicated that some loci contributed to both freezing damage/tolerance and photosynthetic efficiency. The resulting numerical values for phenotypes were used for association analyses with the identified SNPs. Thirteen significant markers were identified on nine chromosomes for the phenotypes scored, with several showing significance for multiple phenotypes. Twenty-five candidate genes were identified as previously associated with freezing tolerance, photosynthesis, or cold-responsive in canola or Arabidopsis.

Unmanned aerial vehicles (UAV) and the sensors they can be equipped with are becoming more technologically advanced and common place within the agricultural industry. The output analyses from UAV captured data helps drive decisions for improving input efficiency in agricultural systems, which can result in maximum return on investment and reduced environmental impact. Advances in UAV technologies provides producers with options for assessment of crucial factors impacting crop yield and quality including crop water status and nutrient stress, competition from weeds, insects, and pathogens, and soil characteristics and precision field maps. Thus, integrating UAV technologies as a component of production agriculture enables producers to identify and locate problem areas in their fields in near real time and take corrective actions once these areas are verified. UAV technologies also offers researchers with a non-destructive, objective manner for obtaining phenotypic measurements such as height assessment, biomass estimation, canopy reflectance, and abiotic and biotic stress tolerance, which can greatly expedite field data collection for advancing germplasm with desired agronomic traits. This review covers more than a hundred articles that were obtained from the existing literature and illustrates common UAV types, common sensor types, imagery processing options, and their practical application and pitfalls in the modern agricultural industry.

Forage sorghum (FS) (Sorghum bicolor (L.) Moench) is a warm-season biomass crop used as forage for hay or silage with the potential to become a bioenergy feedstock or for dual-purpose (forage and energy). The objective of this study was to screen potential forage sorghum genotypes for increased chilling tolerance and biomass productivity. Seventy-one genotypes of FS were first ranked for high to low vigor index under controlled conditions at 24, 12, and 10 °C. Field experiments were also conducted on a subset of 12 genotypes in Fargo and Hickson, ND, USA, in 2017 and 2018, using two different seeding dates: early (10 May) and late (27 May). Field emergence index values were greater for the late-seeding compared with the early seeding date. Under field conditions, seed mortality and biomass yield were affected by the seeding date and biomass yield correlated with emergence index and normalized vegetative index. Chemical composition of forage sorghum biomass was not affected by the seeding dates. The results of this study suggest that some forage sorghum genotypes carry genetic traits for increased chilling tolerance and produce greater biomass yield when seeded earlier than normal, which could allow for breeding chilling tolerance into forage sorghum.

A diverse population (429 member) of canola (Brassica napus, L.) consisting primarily of winter biotypes was assembled and used in genome-wide association studies. Genotype by sequencing analysis of the population identified and mapped 290,972 high-quality markers ranging from 18.5 to 82.4% missing markers per line and an average of 36.8%. After interpolation, 251,575 high-quality markers remained. After filtering for markers with low minor allele counts (count > 5), we were left with 190,375 markers. The average distance between these markers is 4463 bases with a median of 69 and a range from 1 to 281,248 bases. The heterozygosity among the imputed population ranges from 0.9 to 11.0% with an average of 5.4%. The filtered and imputed dataset was used to determine population structure and kinship, which indicated that the population had minimal structure with the best K value of 2–3. These results also indicated that the majority of the population has substantial sequence from a single population with sub-clusters of, and admixtures with, a very small number of other populations. Analysis of chromosomal linkage disequilibrium decay ranged from ~7 Kb for chromosome A01 to ~68 Kb for chromosome C01. Local linkage decay rates determined for all 500 kb windows with a 10kb sliding step indicated a wide range of linkage disequilibrium decay rates, indicating numerous crossover hotspots within this population, and provide a resource for determining the likely limits of linkage disequilibrium from any given marker in which to identify candidate genes. This population and the resources provided here should serve as helpful tools for investigating genetics in winter canola.

Protein content in Cassava (Manihot esculenta Crantz) Storage Root (CSR) is low but variable. Detailed characterization of this variability is missing due to past inappropriate protein quantification procedures. Here we used conventional protein analytical procedures to access variation in Total Buffer Extractable Proteins (TBEPs) associated with Cassava Storage Root (CSR) color in landraces from the Amazon (Brazil). TBEPs values varied according to storage root color from 7.5 (mg/g DWt) in intense yellow landraces to less than 2 (mg/g DWt) in white CSR, an enhancement of up to 4x were detected in intense yellow compared to white CSR, represent up to 55% more protein in the bulk of CSR. Correlations of total buffer extractable proteins and total carotenoid content produced R2 values of 0.4757, 0.6849 and 0.8958 depending on the extraction procedure. Protein polymorphisms were accessed by buffer fraction, carotenoid-protein complex separation in size exclusion chromatography, and protein size separation in SDS-PAGE profile analysis. Total buffer extractable protein molecular weight varied from 10 to 260 Kda in chromoplast enriched suspensions, while purified chromoplast carotenoid-protein complex had specific protein molecular weights of 18 kDa in white CSR, 38 and 43 kDa for yellow CSR, and 23 and 58 kDa in pink CSR phenotypes. Furthermore, of the 143 protein spots observed from purified chromoplasts using 2DE, up to 30 protein spot variations were identified among the four CSR color categories. Furthermore, -carotene and lycopene were the major carotenoid types present in the carotenoid-protein complexes, which was dependent on the CSR color phenotype.