Individual Plant Data Research Articles

UAV time-series imagery show diversity treatment effects on cabbage growth

The demand for more sustainable farming is driving interest in alternative cropping systems, such as strip intercropping. In such systems, two or more crops are grown simultaneously on the same field, offering advantages such as increased biocontrol of weed, pest, diseases, and increasing productivity in resource-limited ecosystems. However, with strip intercropping, complexity increases and quantitative data to study the competition between plants are still limited due to the current manual process of acquiring data. While individual-plant data would facilitate this study, the manual acquisition is not feasible for large-scale experiments. Alternatively, unmanned aerial vehicles (UAV) equipped with high-resolution camera can cover large areas and estimate individual-plant growth from RGB imagery using automated image-processing methods. This study investigated its applicability to monitor the plant-height development of individual cabbage plants in space and time with sufficient accuracy and to identify the potential differences between strip intercropping treatments. Using RGB imagery and structure-from-motion analysis, a digital surface model (DSM) was created. Individual plant-height was calculated from the DSM by estimating the height of the vegetation and the height of the soil. Comparing the height estimations with ground-truth height measurements showed an overall root mean square error (RMSE) of 4.67 cm, which is in the same range as the 4 cm standard deviation between measurements of multiple observers. The UAV-based height estimation of individual plants was used not only to compare the development in a strip intercropping field to that in a monoculture but also to compare with various treatments in the strip intercropping system. The results show that the plants grew faster in intercropping conditions than in monocropping conditions, with a subtle difference between treatments. Our results illustrate that with a UAV-based imaging approach we can go beyond current experimental practice and collect vast amounts of data on individual plants with high spatial and temporal resolution with an accuracy similar to that of manual measurements.

Estimation of waste heat and its recovery potential from energy-intensive industries

The recovery and reuse of waste heat offers a significant opportunity for any country to reduce its overall primary energy usage. Reuse of waste heat improves the ambient air quality by reducing both industrial pollution and greenhouse gas emissions from industries. This paper presents an estimation of thermal waste heat potential in five energy-intensive industrial sectors (i.e., iron and steel, chemical and petrochemical, paper and pulp, cement, and glass), based on data available in the extant literature and government reports. The findings show that both the chemical and petrochemical industries have the highest theoretical waste heat to power generation capacity for the selected industries. In addition, six individual plant data were collected for case study to determine their waste heat potentials. These estimates were further used to identify the power generation potential using the organic Rankine cycle based on economic advantages, whereby the iron and steel industries were found to have the maximum power generation potential of 66.5 TWh using its waste heat.

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.

Assessment of a high-resolution NOX emission inventory using satellite observations: A case study of southern Jiangsu, China

To evaluate the bottom-up NOX emission inventories with different data sources and spatial scales, an updated product of tropospheric NO2 vertical column densities (VCDs) from Ozone Monitoring Instrument (OMI), POMINO, was applied in chemistry transport modeling (CTM) and Gaussian function model for southern Jiangsu, a typical developed and polluted region in eastern China. Compared to the national emission inventory (MEIC), better correlation was found between spatial distributions of the high-resolution provincial inventory (JS) and POMINO VCDs. When applied in CTM, the simulated VCDs using JS were closer to POMINO data than those using MEIC, indicating the advantage of the provincial inventory that incorporated detailed information of individual plants. The simulated VCDs, however, were generally larger than observed ones, particularly for regions with high NO2 levels, partly because the improved NOX control measures for power sector were not fully considered in both national and provincial inventories. The “top-down” NOX emissions were estimated for four cities/city combinations in southern Jiangsu, using a Gaussian function model based on POMINO NO2 VCDs. The results were found to be most consistent with the estimates in JS among bottom-up inventories with different data sources. To further harmonize emissions and satellite observations at relatively small spatial scale, the on-line emission measurement data for individual plants are recommended for emission inventory development, and the products of satellite observation data with finer horizontal resolution are encouraged.

Analysis of flowering dynamics heritability in the perennial warm‐season grass Paspalum dilatatum

AbstractUnderstanding the flowering cycles of perennial warm‐season grass species may be very important to the design of management practices and breeding. However, developmental dynamics are not well understood. As most plant traits associated with flowering dynamics do not follow a normal distribution, the use of general linear models to describe and compare these variables might be misleading. The aims of this study were (i) to find a methodology to compare panicle accumulation curves and (ii) to estimate heritabilities for flowering curve attributes. Panicle counts were recorded during a complete flowering cycle on a diverse collection of dallisgrass (Paspalum dilatatum). We compared the efficiency of different linear mixed models based on whole plot or individual plant data; then, we adjusted nonlinear regression curves for individual plants to estimate several curve attributes and compared this approach to the area under the curve. Finally, we calculated the broad‐sense heritabilities of the estimated curve parameters. The following reproductive curve attributes were obtained: panicle production potential, panicle accumulation rate and days until 3, 5, 10 and 15 panicles. We found that monitoring individual plants is more efficient when studying flowering attributes. Significant differences among genotypes for several flowering cycle attributes were found. Heritabilities were very high for all flowering cycle initiation and duration attributes. We also showed that the number of days until the emergence of a given low number of panicles can be used as a highly heritable measure to characterize flowering cycles.

Marker-based estimation of heritability in immortal populations.

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.

Use of precise spatial data for describing spatial patterns and plant interactions in a diverse Great Basin shrub community

Community-structuring processes continue to be of great interest to plant ecologists, and plant spatial patterns have been linked to processes including disturbance, dispersal, environmental heterogeneity, and plant interactions. Under the assumption that the analysis of the spatial structure of plant communities can help to elucidate the type and importance of the predominant community-structuring processes, many studies have analyzed point pattern data on various plant species. A variety of methods have been devised to acquire point pattern data for individual plants, however, the classic tradeoff between the speed of acquisition and the precision of spatial data has meant that large and precise datasets on plant locations are difficult to obtain. The primary goal of this study was to develop a GPS-based methodology for the rapid collection of precise spatial data on plant locations in a semi-arid shrubland in the Great Basin, USA. The secondary goal was to demonstrate a potential application of this approach by using recently developed univariate and bivariate spatial statistics to test for aggregation within the shrub community, as observed in other semi-arid shrublands. We efficiently mapped 2,358 individuals of five shrub species with a spatial error of ≤0.02 m, and found strong statistical evidence of fine-scale aggregation (1) independent of species, (2) within species, and (3) between two species pairs. Our approach is useful for rapidly collecting precise point pattern data in plant communities, and has other applications related to population modeling, GIS analysis, and conservation.

Uptake of the pharmaceutical Fluoxetine Hydrochloride from growth medium by Brassicaceae

Since the European Union banned disposal of sewage sludge (SS) at sea in 1998 the application rate of SS to land has risen significantly. Land application is thus possibly an important transport route for SS-associated organic chemicals, including pharmaceuticals, to soils and perhaps also to plants. The potential for the selective serotonin re-uptake inhibitor, Fluoxetine HCl, to undergo uptake into Brassicaceae tissues was therefore investigated in a tissue culture study under laboratory conditions for 12 weeks. From growth medium containing 280 ng Fluoxetine HCl mL −1, translocation into Brassica oleracea var. botrytis (cauliflower) stems (5% mean uptake of applied burden; 0.49 μg g wet weight −1) and leaves (3% mean uptake; 0.26 μg g wet weight −1) was confirmed, but no evidence of uptake into the curd was found; other possible explanations of the observations are also discussed. Although the data for individual plants were highly variable, as was the recovery of spiked internal standard (deuterated Fluoxetine HCl), the results nonetheless suggest uptake of Fluoxetine may indeed be a potential transport route to plants. A similar study of uptake from soils rather than from an artificial medium should now be undertaken, with greater numbers of replicates and improved analytical methods. Such studies have already demonstrated uptake of some antibiotics from manured soils by a variety of plants including Brassicaceae, suggesting that the uptake mechanisms may be more general.

Geographical agglomeration: Ellison and Glaeser's index applied to the case of Belgian manufacturing industry

Bertinelli L. and Decrop J. (2005) Geographical agglomeration: Ellison and Glaeser's index applied to the case of Belgian manufacturing industry, Regional Studies 39 , 567–583. The aim of this study is to provide a comprehensive discussion of the use of the Ellison and Glaeser index and subsequently to carry out a descriptive analysis of the geographical concentration of the manufacturing sector in Belgium. To assess the degree of concentration, individual plant data on wage and salary earners are used, with a relatively fine geographical breakdown (districts and townships). It is shown that more than 30% of the industries are excessively agglomerated.

Influence of water stress on cyanogenic capacity in Eucalyptus cladocalyx.

Cyanogenesis in many plant species is an effective herbivore deterrent, which appears to be influenced by a range of environmental variables. There is evidence that one such variable, soil water availability, increases cyanogenic capacity (i.e. leaf cyanogenic glycoside concentration), but it is not clear whether this is a relatively direct or indirect effect. To shed light on this issue, we compared the cyanogenic capacity of individuals from two populations of Eucalyptus cladocalyx F.Muell. from areas of South Australia that differ markedly in rainfall. Stable carbon isotope analysis confirmed that trees at the drier site were more water-stressed. We found a large range in leaf cyanogenic capacities, from 0 to 1.01 mg cyanide g-1 dry weight. Importantly, this is the first record of acyanogenic E. cladocalyx. Mean cyanogenic capacity was 30% higher in trees from the drier site, and they suffered less damage from herbivores. However, these trees also contained higher concentrations of leaf nitrogen (N). Correlative analysis of data for individual plants from both sites showed that leaf N was able to account for a significant amount of the variation in cyanogenic glycoside concentration (28%). Water availability on its own, however, was not able to account significantly for any such variation. We conclude that most of the variation in cyanogenic capacity is due to genetic differences between individuals, while the remaining variation is due to differences in leaf N.

AFLP based alternatives for the assessment of Distinctness, Uniformity and Stability of sugar beet varieties

Three approaches for addressing criteria for Distinctness, Uniformity and Stability (DUS) assessment by means of AFLP data are presented. AFLP data were obtained for three consecutive seed deliveries of 15 sugar beet varieties that were under investigation for the official Belgian list (’93, ’94 and ’95). In total, 696 AFLP markers were scored on 1350 plants. As a first approach, a cluster analysis based on Nei’s standard genetic distances between varieties and/or seed deliveries was made. Three major groups put together varieties belonging to corresponding breeding programmes. Statistical procedures, involving bootstrapping and random sampling of subsets of markers, were applied to test the reproducibility of the ordinations and the redundancy present in the data set. In a second approach, the genetic structure inferred by varieties and seed deliveries was submitted to an Analysis of Molecular Variance (AMOVA). Major genetic variation was attributed to individual plant differences within seed deliveries. Differences among seed deliveries seemed to be as important as differences among varieties or breeding programmes. Individual plant data were used for assignment tests. The computation of the assignment was based on the ranking of individual genotypes to one other (based on Jaccard similarity coefficients). The distribution over the accessions for each variety or seed delivery was used to check what group of plants each individual is genetically most similar to. Varieties were classified according to the degree to which the distribution over the different accessions was mainly allocated to their appropriate seed deliveries (from the same variety) or cross- allocated to other varieties. Criteria for DUS-evaluation could be set by each of the approaches; it is discussed in what way the result obtained differs and agrees.

Relationship between Soybean Sudden Death Syndrome Disease Measures and Yield Components in F6‐Derived Lines

Soybean [Glycine max (L.) Merr.] sudden death syndrome (SDS), caused by Fusarium solani (Mart.) Sacc. f. sp. glycines Roy can cause substantial yield loss. To determine the relationship between SDS and yield components, 16 F6‐derived lines from the cross ‘Pyramid’ ✕ ‘Douglas’ were tested in two southern Illinois locations (Cora and Ridgway) with natural infestation by the SDS pathogen. Plots were rated for disease incidence (DI), disease severity (DS), and reproductive development stage (R). DI and DS were standardized to R6. Disease index (DX) was calculated as DI×DS/9. Individual plant data collected on three plants per plot were DS, total flowers, total pods at R5.5 (mid‐pod‐fill) and R8, total empty and partially filled pods at R8, total seeds, seeds per pod, seed weight per plant, and 100‐seed weight. Seed quality was rated only at Ridgway. The among genotypes (genetic) covariance and the within genotype (environmental) covariance detected significant reductions in yield per unit of DS and DX (whole plot data). The genetic covariance with individual plant DS detected a significant reduction per unit of DS in total seed weight per plant. The environmental covariance with DS detected significant reductions in all yield components except flowers, empty pods, and seed per pod. There was a significant correlation of G✕E effects of most yield components and DS. Generally, genetic and environmental covariances were similar in magnitude and direction, suggesting that test genotypes were similar in the components affected and the degree of yield loss associated with a given level of disease.

Sorghum Harvest Index in Relation to Plant Size, Environment, and Cultivar

AbstractThere are reports in the literature showing that harvest index (HI) does and does not increase with increase in plant size. The slope of the upper‐bound line passing through the origin in the grain yield vs. dry matter yield plots has been proposed to approximate the “genetic” HI for a given environment and inter‐cultivar comparison. The objectives of this study were to determine what changes occur in HI of sorghum [Sorghum bicolor (L.) Moench] as plant size, environment, and cultivar vary. Several environments were established in two replicated field experiments on Pullman clay loam (fine, mixed, thermic Torrertic Paleustoll), one with eight cultivars, and the other with two cultivars, three seeding dates, and three irrigation regimes. Cultivar DK‐41Y were common to both experiments. Variation in plant size was created by thinning the original stands to varying degrees. Harvest index was determined for different plant densities based on individual plant data. Lack of non‐zero intercepts of grain‐yield vs. dry‐matter‐per‐plant regressions and constancy of HI with change in plant mass, under a given environmental condition, showed that HI was independent of plant size. Harvest index values, based both on average grain and dry matter yield, and slope of the upper‐bound line, were sensitive to environments. Upperbound HI values of cultivars DK‐42, DK‐41Y, and Sprint (0.50–0.52) were significantly higher than those of Funks 499, Richardson 9112, and DK‐46 (0.46–0.48).

Prediction of F3 row performance from F2 individual plant data in oats

Parameters estimated from a Gardner-Eberhart analysis of the F2 generation of a six-parent diallel in oats (Avena sativa L.) were used to compare methods for predicting the performance of F3 row plots. The prediction methods were: (1) individual F2 plant performance (F2I), (2) parent average plus F2 plot deviations (PF2), (3) parent average plus weighted F2 plot deviations (PF2P), (4) best linear unbiased prediction (BLUP) of parent average plus F2 plot deviations (BPF2), and (5) BLUP plus weighted F2 deviations (BF2). The F2 single-plant traits used for prediction were biological yield to predict F3 biological yield, whole plant and primary tiller grain yield for prediction of F3 grain yield, and whole plant and primary tiller harvest index (HI) to predict F3 HI. Prediction methods were evaluated by correlations between predicted and observed F3 performance. Prediction methods and traits for which correlations were greater than for F2I included: BF2 for biological yield, PF2, PF2P and BF2 for whole plant grain yield, PF2, BPF2, and BF2 for primary tiller grain yield. None had a correlation significantly greater than F2I for either measure of HI, where heritability was large. PF2 is the recommended method for traits with low heritability because of its simplicity and because it had the largest or nearly the largest correlation for each of the yield traits. F2I is the recommended method for traits with larger heritability.

Development of Short and Coarse‐Fibered American Pima Cotton for Use as Parents of Interspecific Hybrids

Short, coarse‐fibered Pima (Gossypium barbadense L.) cotton strains may be desirable as parents of interspecific F1 hybrid cottons. This study was conducted to determine if selective advance toward short, coarse‐fiber types could be accomplished within a subset of Pima strains while maintaining adequate fiber strength, uniformity, and lint percentage. The fiber selection criteria of extralong length and fineness used for American Pima cotton were reversed within the subset. Two diallel tests were conducted at Maricopa and Marana, AZ in 1985. Significant (0.01 level) genotype mean squares and general combining ability (GCA) estimates indicated useful variability for shorter, coarser fiber. Comparisons of GCA and specific combining ability (SCA) sums of squares (SS) with the total sums of squares indicated that greater additive genetic variability was available for fiber micronaire (x̄GCA SS/total SS = 0.70) than for fiber length (x̄ GCA SS/total SS = 0.34). A single best parent could not be identified based upon GCA effects due to the necessity of simultaneous selection for several traits. Population variances and ranges, estimated from individual plant data from F1 and F2 generations grown in 1985 and 1986, were consistent with genetic segregation in the F2 generation for fiber length and micronaire. Broadsense heritability estimates ranged from 0.20 to 0.52 for 2.5% span length and from 0.51 to 0.69 for micronaire in the F2 populations. Transgressive segregation was observed in F2 populations for shorter and coarser fiber. Selective advance toward Pima cotton with short, coarse fiber appears possible, but progress is expected to be slow.

Generation Means Analysis of Plant Characters in Crosses Between Two Determinate Cucumber Lines and Cucumis sativus var. hardwickii

Abstract In an effort to develop a high-yielding, determinate plant type with multiple lateral branches, crosses were made between two determinate Cucumis sativus var. sativus lines (‘Spacemaster’ and NCSU M27) and a C. sativus var. hardwickii (R.) Alef. line, LJ 90430. Individual plant data from six generations (P1, P2, F1, F2, BC1P1, BC1P2) were subjected to an analysis of generation means to determine the importance of gene effects for number of nodes, number of primary lateral branches, average internode length, main stem length, and number of fruit per plant at maturity. A simple additive-dominance model adequately explained variation for number of primary laterals in both crosses, and average internode length in the ‘Spacemaster’ × LJ 90340 cross. For all other traits, it was necessary to add one or more digenic epistatic effects to the model. Heterosis of the F1 over the mid-parent was highly significant for all traits in both crosses. Heterosis above the high-parent was observed for average internode length and main stem length. Dominance for number of primary laterals was in the direction of the parent with higher lateral number.

Selection Indices for the Improvement of Opaque‐2 Maize1

The need to improve agronomic and protein and kernel quality characters in opaque‐2 maize (Zea mays L.) mandates consideration of multiple traits in selection. Data from genetic studies in an opaque‐2 maize synthetic were used to calculate selection indices for use in recurrent selection. Grain yield was considered the only trait having explicit economic value. Restricted indices were used to achieve satisfactory gains in kernel hardness, moisture content, and protein content and quality.The most important traits with respect to achieving the specified selection goals were yield, moisture content, kernel hardness, lysine content, kernel weight, and the degree of light transmission by kernels. Indices incorporating five traits were more efficient in that the addition of a sixth trait contributed very little to expected gains. The most effective combinations of traits for a recurrent selection scheme involving S1 testing were also the most effective for S2 testing and full‐sib testing.The degree to which gains predicted for an index were dependent on accurate estimates of the population parameters was considered. Indices having weights that implied reliance on indirect rather than direct selection response were not recommended.Substitution of single‐plant data for data from replicated progeny tests was considered in connection with each breeding scheme. The possibility of increasing the effective selection intensity, given that resources for replicated testing were fixed, by culling individual plants was investigated, but the use of individual plant data did not increase expected gain.A measure of subjective judgment on the part of the breeder is probably required for the successful application of selection indices to complex problems.

Inter-Fuel Substitution Possibilities: A Translog Application to Intercountry Data

One of the pressing issues in current energy policy debates in both the U. S. and other consuming countries is the feasibility of substantially reducing the use of crude oil. In some energy consuming sectors suchl as transportation, the shortrun mechanism to reduce consumption is through lower utilization of energyconsuming equipment while in the long-run, the equipment can be redesigned to achieve greater fuel efficiencies. In electricity generation and heatilng uses the primary mechanism to reduce consumption is inter-fuel substitution rather than capital/energy substitution. This paper is concernied with estimating the interfuel substitution relationships between fossil fuels (coal, gas, and residual fuel oil) in the generation of electricity. It is important to consider fossil fuel substitution possibilities in electricity generation both because considerable technical flexibility exists2 and because electric utilities are large consumers of fossil fuels. For example, electric utilities consumed 25% of primary energy inputs in the U. S. in 1970. Although not as high as the U. S., the other developed countries exhibit a similar pattern. In the 20 0. E. C. D. coun-tries, electric utilities consumed 50% of the coal, 17% of the gas, and 11% of petroleum products.3 High substitution elasticities among fuels in electricity generation indirectly increase subsequenit substitution of electricity for petroleum as a fuel source in residential, commercial, and industrial sectors. Thus the nature and extent of inter-fuel substitution possibilities in electricity generation are fundamental to the question of reducing dependency on crude oil. There have been several econometric studies of inter-fuel substitution in electricity generation using a variety of estimation procedures and samples. MacAvoy [15] used cross sectional data for U. S. power regions to examine substitution between nuclear fuel and fossil fuel. Griffin [10] estimated dynamic price and cross price elasticities among fuels utilizing annual time series data. More recently, Joskow and Mishkin [13] used individual plant data to analyze fuel choice in a logit model framework. But probably the approach capturing the most attention is the application of

AN EMPIRICAL EXAMINATION OF THE AVERCH-JOHNSON EFFECT

In this paper the Averch-Johnson thesis of a capital bias in regulated firms is examined empirically. This is accomplished by estimating a system of factor demand functions using individual plant data from the electric power industry. Results obtained do not provide support for the A-J effect and it is suggested that there are a number of reasons this could have been expected a priori. finally, the implied parameters of the production function are derived and presented; the values are in line with other estimates of the production function for the electric power industry.

Polyethylene versus Glassine Shoot Bags in Pollination of Corn (Zea mays L.)1

Individual plant data from 300 single‐cross plants consisting of 10 replications were used. Of the eight characteristics studied, the glassine bags were significantly better than the polyethylene bags with respect to the following: 1) moisture inside the bag one day before pollination; 2) moisture inside the bag on day of pollination; 3) number of kernels per ear; and 4) distribution of kernels per ear.A significantly better rating was obtained for condition of bags on the day of pollination than on one day before pollination within the polyethylene treatment. The better rating is attributed to silk growth during the interval.The simple correlation value of ‐.45** indicated that an increase in the amount of moistiire inside the shoot bag caused a decrease in number of Kernels. Ears under glassine bags produced 14.40 kernels more per ear than those under polyethylene. Moisture inside the bag and length of silks were negatively correlated (r = ‐.23**). An r value of +.27** was obtained between length of silks and number of kernels per ear. Bags standing upright on the shoot had less moisture (r = ‐‐.21*).