@article{chaudhari_jennings_monks_mehra_2021, title={Interaction of common purslane (Portulaca oleracea) and Palmer amaranth (Amaranthus palmeri) with sweet potato (Ipomoea batatas) genotypes}, volume={101}, ISSN={["1918-1833"]}, DOI={10.1139/cjps-2020-0138}, abstractNote={ Greenhouse replacement series studies were conducted to determine the relative competitiveness of NC10-275 (unreleased, drought tolerant; upright, bushy, and vining growth with large leaves) and Covington (the most commonly grown genotype in North Carolina; vining growth with smaller leaves) sweet potato genotypes with weeds. Sweet potato genotypes were grown with Palmer amaranth (tall growing) or common purslane (low growing) at five planting (sweet potato to weed) proportions of 100:0, 75:25, 50:50, 25:75, and 0:100 at a density of four plants per pot. Reduction in common purslane shoot dry biomass was greater when growing with NC10-275 than when growing with Covington or alone. When growing with common purslane, shoot dry and root fresh biomass of Covington was 18% and 26% lower, respectively, than NC10-275. Relative yield (shoot dry biomass) and aggressivity index (AI) of common purslane was lower than both sweet potato genotypes. Palmer amaranth shoot dry biomass was similar when growing alone or with Covington, whereas it was reduced by 10% when growing with NC10-275 than alone. Palmer amaranth competition reduced shoot dry biomass and root fresh biomass of Covington by 23% and 42%, respectively, relative to NC10-275. Relative yield and AI of Palmer amaranth was greater than Covington but lower than NC10-275. This research indicates that sweet potato genotypes differ in their ability to compete with weeds. Both sweet potato genotypes were more competitive than common purslane, and the following species hierarchy exists: NC10-275 > Covington > common purslane. In contrast, NC10-275 was more competitive than Covington with Palmer amaranth, and the following species hierarchy exists: NC10-275 ≥ Palmer amaranth > Covington. }, number={4}, journal={CANADIAN JOURNAL OF PLANT SCIENCE}, author={Chaudhari, Sushila and Jennings, Katherine M. and Monks, David W. and Mehra, Lucky K.}, year={2021}, month={Aug}, pages={447–455} }
 @article{mehra_cowger_ojiambo_2017, title={A Model for Predicting Onset of Stagonospora nodorum Blotch in Winter Wheat Based on Preplanting and Weather Factors}, volume={107}, ISSN={["1943-7684"]}, DOI={10.1094/phyto-03-16-0133-r}, abstractNote={ Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum is a serious disease of wheat worldwide. In the United States, the disease is prevalent on winter wheat in many eastern states, and its management relies mainly on fungicide application after flag leaf emergence. Although SNB can occur prior to flag leaf emergence, the relationship between the time of disease onset and yield has not been determined. Such a relationship is useful in identifying a threshold to facilitate prediction of disease onset in the field. Disease occurred in 390 of 435 disease cases that were recorded across 11 counties in North Carolina from 2012 to 2014. Using cases with disease occurrence, the effect of disease onset on yield was analyzed to identify a disease onset threshold that related time of disease onset to yield. Regression analysis showed that disease onset explained 32% of the variation in yield (P < 0.0001) and from this relationship, day of year (DOY) 102 was identified as the disease onset threshold. Below-average yield occurred in 87% of the disease cases when disease onset occurred before DOY 102 but in only 28% of those cases when onset occurred on or after DOY 102. Subsequently, binary logistic regression models were developed to predict the occurrence and onset of SNB using preplanting factors and cumulative daily infection values (cDIV) starting 1 to 3 weeks prior to DOY 102. Logistic regression showed that previous crop, latitude, and cDIV accumulated 2 weeks prior to DOY 102 (cDIV.2) were significant (P < 0.0001) predictors of disease occurrence, and wheat residue, latitude, longitude, and cDIV.2 were significant (P < 0.0001) predictors of disease onset. The disease onset model had a correct classification rate of 0.94 and specificity and sensitivity rates >0.90. Performance of the disease onset model based on the area under the receiver operating characteristic curve (AUC), κ, and the true skill statistic (TSS) was excellent, with prediction accuracy values >0.88. Similarly, internal validation of the disease onset model based on AUC, κ, and TSS indicated good performance, with accuracy values >0.88. This disease onset prediction model could serve as a useful decision support tool to guide fungicide applications to manage SNB in wheat. }, number={6}, journal={PHYTOPATHOLOGY}, publisher={Scientific Societies}, author={Mehra, L. K. and Cowger, C. and Ojiambo, P. S.}, year={2017}, month={Jun}, pages={635–644} }
 @article{ojiambo_gent_mehra_christie_magarey_2017, title={Focus expansion and stability of the spread parameter estimate of the power law model for dispersal gradients}, volume={5}, ISSN={["2167-8359"]}, url={http://europepmc.org/abstract/med/28649473}, DOI={10.7717/peerj.3465}, abstractNote={Empirical and mechanistic modeling indicate that pathogens transmitted via aerially dispersed inoculum follow a power law, resulting in dispersive epidemic waves. The spread parameter (b) of the power law model, which is an indicator of the distance of the epidemic wave front from an initial focus per unit time, has been found to be approximately 2 for several animal and plant diseases over a wide range of spatial scales under conditions favorable for disease spread. Although disease spread and epidemic expansion can be influenced by several factors, the stability of the parameter b over multiple epidemic years has not been determined. Additionally, the size of the initial epidemic area is expected to be strongly related to the final epidemic extent for epidemics, but the stability of this relationship is also not well established. Here, empirical data of cucurbit downy mildew epidemics collected from 2008 to 2014 were analyzed using a spatio-temporal model of disease spread that incorporates logistic growth in time with a power law function for dispersal. Final epidemic extent ranged from 4.16 ×108 km2 in 2012 to 6.44 ×108 km2 in 2009. Current epidemic extent became significantly associated (P < 0.0332; 0.56 < R2 < 0.99) with final epidemic area beginning near the end of April, with the association increasing monotonically to 1.0 by the end of the epidemic season in July. The position of the epidemic wave-front became exponentially more distant with time, and epidemic velocity increased linearly with distance. Slopes from the temporal and spatial regression models varied with about a 2.5-fold range across epidemic years. Estimates of b varied substantially ranging from 1.51 to 4.16 across epidemic years. We observed a significant b ×time (or distance) interaction (P < 0.05) for epidemic years where data were well described by the power law model. These results suggest that the spread parameter b may not be stable over multiple epidemic years. However, b ≈ 2 may be considered the lower limit of the distance traveled by epidemic wave-fronts for aerially transmitted pathogens that follow a power law dispersal function.}, journal={PEERJ}, author={Ojiambo, Peter S. and Gent, David H. and Mehra, Lucky K. and Christie, David and Magarey, Roger}, year={2017}, month={Jun} }