Plot size effects on airborne LiDAR-derived metrics and predicted model performance of subtropical planted forest stand attributes

Background Field plot measurement is an essential task for forest inventory and monitoring and ecological applications based on airborne LiDAR. To optimize the field plot size and reduce cost, it is necessary to investigate the influence of field plot size on LiDAR-derived metrics and the accuracy of forest parameter estimation models. Methods A subtropical planted forest with an area of 4,770 ha was used as the study site, and 104 square plot of 900 m2 (30 m×30 m, subdivided into nine quadrats, each with an area of 100 m2 (10 m×10 m)) was divided into field plots with six different areas (100 m2, 200 m2, 300 m2, 400 m2, 600 m2 and 900 m2) by grouping quadrats. The differences in the LiDAR-derived metrics and stand attributes of different sized plots with four forest types (Chinese fir, pine, eucalyptus and broadleaf) were investigated. Through multivariate power models with stable structures, the differences in forest parameter (BA, VOL) estimation accuracies for plots with different sizes were compared. Results (1) The mean differences in LiDAR-derived metrics related to height, density and vertical structure between the plots with different sizes and the 900 m2 plot containing all forest types were very small, and when the plot size changed, these differences changed irregularly; however, the standard deviations of the differences increased rapidly with decreasing plot size. (2) There were significant differences in the mean of the maximal height of the point cloud (Hmax), density of the 75th percentile of the point cloud (dh75) and mean leaf area density (LADmean) (except for Chinese fir and eucalyptus) between the plots with different sizes and the 900 m2 plot containing all forest types; other LiDAR-derived metrics had significant differences in only some or a certain size of plots, but there was no regularity. (3) Except for the maximal tree height of the plot (Hm), the forest stand attributes, including the mean tree height (H), diameter at breast height (DBH), basal area (BA), and stand volume (VOL), of all forest types showed either no significant differences or minimal differences between plots with different sizes and the 900 m2 plot. (4) With increasing plot size, the coefficient of determination (R2) of the estimation models for VOL and BA of all forest types increased gradually, while the relative root mean square error (rRMSE) and mean prediction error (MPE) decreased gradually, and the estimation accuracy of the models improved. Conclusion Due to the heterogeneity of the vertical and horizontal forest structures, some LiDAR-derived metrics and stand parameters for field plots with different sizes varied. As the plot size increased, the variations in the independent variables (LiDAR-derived metrics) and dependent variables (stand parameters) of the estimation models decreased gradually. These changes improved the robustness and accuracy of the models. In the application of airborne LiDAR in forest inventory and monitoring, both prediction accuracy and cost should be considered. For subtropical planted forests, we preliminarily suggest the following appropriate sizes for field plots: 900 m2 for Chinese fir and pine forests, 400 m2 for eucalyptus forests and 600 m2 for broadleaf forests. However, this protocol still needs to be tested in further studies.

Influence of spatial planting arrangement of winter rye cover crop on corn seedling disease and corn productivity

Despite numerous environmental benefits associated with cover crop (CC) use, some farmers are reluctant to include CCs in their production systems because of reported yield declines in corn. There are numerous potential reasons for this yield decline, including seedling disease. A winter rye CC can serve as a ‘green bridge’ for corn seedling pathogens. We hypothesized that proximity of corn seedling roots to decaying rye CC roots contributes to corn seeding disease. An experimental field plot and an on-farm study were conducted over two years to evaluate growth, development, and disease severity of corn seedlings planted at various distances from decaying winter rye CC plants. The experimental field plot study was conducted in a no-till corn-soybean rotation with five replications of a winter rye CC treatments seeded as (i) no CC control, (ii) broadcast, (iii) 19-cm drilled rows, and (iv) 76-cm drilled rows. The on-farm study was no-till corn-soybean rotation with four replications of a winter rye cover crop seeded as 38-cm drilled rows, 76-cm drilled rows, and no CC control. The corn was planted on 76-cm rows shortly after rye was terminated. With multiple seeding arrangements of winter rye, corn was planted at different distances from winter rye. Corn radicle root rot severity and incidence, shoot height, shoot dry weight, corn height and chlorophyll at VT, ear parameters, and yield were collected. Soil samples were taken in the corn row and the interrow at winter rye termination, corn planting, and corn growth stage V3 to estimate the abundance of Pythium clade B members present in soil samples. Our results showed that increased distance between winter rye residue and corn reduced seedling disease and Pythium clade B populations in the radicles and soil, and increased shoot dry weight, leaf chlorophyll, plant height, and yield. This suggests that physically distancing the corn crop from the winter rye CC is one way to reduce the negative effects of a winter rye CC on corn.

Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires.

Adult Plant Leaf Rust Resistance in AC Taber Wheat Maps to Chromosomes 2BS and 3BS

AC Taber is a hard red spring wheat cultivar that has had long-lasting resistance to the leaf rust fungus Puccinia triticina. The objective of this study was to determine the chromosome location of the leaf rust resistance genes in AC Taber. The leaf rust-susceptible cultivar Thatcher was crossed with AC Taber to develop an F6 recombinant inbred line (RIL) population. The RILs and parents were evaluated for segregation of leaf rust resistance in five field plot tests and in two seedling tests to race BBBDB of P. triticina. A genetic map of the RIL population was developed using 90,000 single nucleotide polymorphism markers with the Illumina Infinium iSelect 90K wheat bead array. Quantitative trait loci (QTLs) with significant effects for lower leaf rust severity in the field plot tests were found on chromosomes 2BS and 3BS. The same QTLs also had significant effects for lower infection type in seedlings to leaf rust race BBBDB. The gene on 2BS was the adult plant resistance gene Lr13, and the gene on 3BS mapped to the same region as the adult plant resistance gene Lr74 and other QTLs for leaf rust resistance. Kompetitive allele-specific PCR assay markers linked to the 2BS and 3BS regions were developed and should be useful for marker-based selection of these genes.

A high-throughput phenotyping tool to identify field-relevant anthracnose resistance in white lupin

The seed- and air-borne pathogen Colletotrichum lupini, the causal agent of lupin anthracnose, is the most important disease in white lupin (Lupinus albus) worldwide and can cause total yield loss. The aims of this study were to establish a reliable high-throughput phenotyping tool to identify anthracnose resistance in white lupin germplasm and to evaluate a genomic prediction model, accounting previously reported resistance QTLs, on a set of independent lupin genotypes. Phenotyping under controlled conditions, performing stem inoculation on seedlings, showed to be applicable for high-throughput and its disease score strongly correlated with field plot disease assessments (r = 0.95, p<0.0001) and yield (r = -0.64, p=0.035). Traditional 1-row field disease phenotyping showed no significant correlation with field plot disease assessments (r = 0.31, p=0.34) and yield (r = -0.45, p=0.17). Genomically-predicted resistance values showed no correlation with values observed under controlled or field conditions, and the parental lines of the RIL population used for constructing the prediction model exhibited a resistance pattern opposite to that displayed in the original (Australian) environment used for model construction. Differing environmental conditions, inoculation procedures or population structure may account for this result. Phenotyping a diverse set of 40 white lupin accessions under controlled conditions revealed eight accessions with improved resistance to anthracnose. The standardized area under the disease progress curves (sAUDPC) ranged from 2.1 to 2.8 compared to the susceptible reference accession with a sAUDPC of 3.85. These accessions can be incorporated into white lupin breeding programs. In conclusion, our data supports stem inoculation-based disease phenotyping under controlled conditions as a time-effective approach to identify field-relevant resistance which can now be applied to further identify sources of resistance and their underlying genetics.