Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant ...growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models' suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
•Adequate biomass is required for cover crops to provide significant ecological benefits.•Satellite imagery predicted field level cover crop biomass accurately.•Satellite imagery more accurately ...estimated biomass than handheld sensors.•At least one vegetation index for each satellite system accurately predicted biomass.•NDVI was not the best predictor of cover crop biomass for any satellite.
Cost-share programs based on measures of participation rather than performance are available to farmers who plant cover crops. However, cover crops only provide significant ecological benefits like reduced nutrient loss when adequate biomass is established. The purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy. Aboveground biomass samples of 1 m2 were collected for 86 sites within 26 agricultural fields containing unique cover crop species composition to assess biomass production. In-field sensors were used to measure normalized difference vegetation index (NDVI) and groundcover percentage. Three satellites (Landsat-8 30 m resolution, Sentinel-2 10 m resolution, and PlanetScope 3 m resolution) were used to calculate eight vegetation indices (VIs) for comparison with cover crop biomass. Multiple linear regression, correlation coefficients, and root mean square error (RMSE) were used to perform hierarchical clustering to rank VIs calculated from each satellite for biomass estimation accuracy. Satellites predicted cover crop biomass at the field level very accurately (r2 up to 0.79), demonstrating the potential of large-scale biomass estimation at relatively low cost compared to in-field sampling. All satellite-VI pairs estimated biomass more accurately than the in-field sensors. Performance of VIs varied by satellite, but each satellite had at least one VI that performed very well for both site-level and field-averaged data. When using PlanetScope or Landsat-8 imagery, the perpendicular vegetation index provided the most accurate cover crop biomass estimation on a per-site basis and ratio vegetation index performed best using Sentinel-2 imagery. PlanetScope was the only satellite to provide useable imagery for every site due to increased revisit period; however, its increased spatial resolution did not increase estimation accuracy overall compared to Landsat-8 or Sentinel-2.
Soil properties and weather conditions are known to affect soil N availability and plant N uptake; however, studies examining N response as affected by soil and weather sometimes give conflicting ...results. Meta‐analysis is a statistical method for estimating treatment effects in a series of experiments to explain the sources of heterogeneity. In this study, the technique was used to examine the influence of soil and weather parameters on N response of corn (Zea mays L.) across 51 studies involving the same N rate treatments that were performed in a diversity of North American locations between 2006 and 2009. Results showed that corn response to added N was significantly greater in fine‐textured soils than in medium‐textured soils. Abundant and well‐distributed rainfall and, to a lesser extent, accumulated corn heat units enhanced N response. Corn yields increased by a factor of 1.6 (over the unfertilized control) in medium‐textured soils and 2.7 in fine‐textured soils at high N rates. Subgroup analyses were performed on the fine‐textured soil class based on weather parameters. Rainfall patterns had an important effect on N response in this soil texture class, with yields being increased 4.5‐fold by in‐season N fertilization under conditions of “abundant and well‐distributed rainfall.” These findings could be useful for developing N fertilization algorithms that would prescribe N application at optimal rates taking into account rainfall pattern and soil texture, which would lead to improved crop profitability and reduced environmental impacts.
Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts ...associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown × VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown × VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.
SUMMARY
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.
Significance Statement
This manuscript describes a unique and powerful population of maize near‐isogenic lines that can be used for the rapid characterization of the genetic basis of a range of quantitative traits. The analysis of the genetic basis of resistance to three diseases and associated traits is provided as an example.
In 2019 and 2020, we investigated the individual and combined effects of two biofertilizers (manure tea and bioinoculant) and one humic acid (HA) product on cannabis biochemical and physiological ...parameters and soil CO
evolution under outdoor conditions. Our hypothesis was that HA would increase the microbial activity in the biofertilizers and synergy of both compounds would promote better plant performance and stimulate soil microbial activity. In 2020, the individual and combined application of biofertilizers and HA increased cannabis height, chlorophyll content, photosynthetic efficiency, aboveground biomass, and bucked biomass by 105, 52, 43, 122, and 117%, respectively. Impacts were greater under suboptimal growing conditions caused by planting delay experienced in 2020. In 2019, planting date occurred in-between the most favorable period and chlorophyll content and photosynthetic efficiency were the only parameters influenced by the application of biostimulants. The discrepancies between the two growing seasons reinforce the evidence of other studies that biostimulants efficacy is maximized under stress conditions. This study could not conclusively confirm that the combined use of biofertilizer + HA is a superior practice since affected plant parameters did not differ from application of the compounds singly. Similarly, only one biofertilizer + HA treatment increased soil microbial activity. More research is needed to define optimum rates and combinations of biofertilizer and stimulants for cannabis.
Studies have shown that the quantification of hail damage is generally inaccurate and is influenced by the experience of the field surveyors/technicians. To overcome this problem, the vegetation ...indices retrieved by remote sensing, can be used to get information about the hail damage. The aim of this work is the detection of medium-low damages (i.e., between 10 and 30% of the gross saleable production) using the much-used normalized difference vegetation index (NDVI) in comparison with alternative vegetation indices (i.e., ARVI, MCARI, SAVI, MSAVI, MSAVI2) and their change from pre-event to post-event in five hailstorms in Lombardy in 2018. Seventy-four overlapping scenes (10% cloud cover) were collected from the Sentinel-2 in the spring-summer period of 2018 in the Brescia district (Lombardy). An unsupervised classification was carried out to automatically identify the maize fields (grain and silage), testing the change detection approach by searching for damage by hail and strong wind in the Lombardy plain of Brescia. A database of 125 field surveys (average size 4 Ha) after the hailstorm collected from the insurance service allowed for the selection of the dates on which the event occurred and provided a proxy of the extent of the damage (in % of the decrease of the yield). Hail and strong wind damages ranged from 5 to 70%, and they were used for comparison with the satellite image change detection. The differences in the vegetation indices obtained by Sentinel 2 before and after the hailstorm and the insurance assessments of damage after the events were compared to assess the degree of concordance. The modified soil-adjusted vegetation index outperformed other vegetation indices in detecting hail-related damages with the highest accuracy (73.3%). On the other hand, the NDVI resulted in scarce performance ranking last of the six indices, with an accuracy of 65.3%. Future research will evaluate how much uncertainty can be found in the method’s limitations with vegetation indices derived from satellites, how much is due to errors in estimating damage to the ground, and how much is due to other causes.
Highlights - The discovery rate of damaged fields improved. - MSAVI outperformed NDVI and other vegetation indices, identifying 73.3% of occurrences. - Estimation of damage from remote sensing was more accurate for fields severely affected >50%. - In low-intensity hail events (<50 canopies affected), the MSAVI provided a detailed picture of the damage across the field. - The proposed approach is promising to develop a ‘sampling map’ for detailed on-ground assessment.
Southern Leaf Blight (SLB), Northern Leaf Blight (NLB), and Gray Leaf Spot (GLS) caused by
,
, and
respectively, are among the most important diseases of corn worldwide. Previously, moderately high ...and significantly positive genetic correlations between resistance levels to each of these diseases were identified in a panel of 253 diverse maize inbred lines. The goal of this study was to identify loci underlying disease resistance in some of the most multiple disease resistant (MDR) lines by the creation of chromosome segment substitution line (CSSL) populations in multiple disease susceptible (MDS) backgrounds. Four MDR lines (NC304, NC344, Ki3, NC262) were used as donor parents and two MDS lines (Oh7B, H100) were used as recurrent parents to produce eight BC
F
CSSL populations comprising 1,611 lines in total. Each population was genotyped and assessed for each disease in replicated trials in two environments. Moderate to high heritabilities on an entry mean basis were observed (0.32 to 0.83). Several lines in each population were significantly more resistant than the MDS parental lines for each disease. Multiple quantitative trait loci (QTL) for disease resistance were detected for each disease in most of the populations. Seventeen QTL were associated with variation in resistance to more than one disease (SLB/NLB: 2; SLB/GLS: 7; NLB/GLS: 2 and 6 to all three diseases). For most populations and most disease combinations, significant correlations were observed between disease scores and also between marker effects for each disease. The number of lines that were resistant to more than one disease was significantly higher than would be expected by chance. Using the results from individual QTL analyses, a composite statistic based on Mahalanobis distance (
) was used to identify joint marker associations with multiple diseases. Across all populations and diseases, 246 markers had significant
values. However further analysis revealed that most of these associations were due to strong QTL effects on a single disease. Together, these findings reinforce our previous conclusions that loci associated with resistance to different diseases are clustered in the genome more often than would be expected by chance. Nevertheless true MDR loci which have significant effects on more than one disease are still much rarer than loci with single disease effects.
Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m2 at ...GS 25, then an N application at that stage is not needed, only at GS 30. However, it is often difficult to obtain an accurate representation of tiller density across an entire field. Normalized difference vegetative index (NDVI) and normalized difference red edge (NDRE) have been significantly correlated with tiller density when collected from the ground. With the advent of unmanned aerial vehicles (UAVs) and their ability to collect NDVI and NDRE from the air, this study was established to examine the relationship between NDVI, NDRE, and tiller density, and to verify whether N could be applied based on these two indices. From 2018 to 2020, research trials were established across Virginia to develop a model describing the relationship between aerial NDVI, aerial NDRE, and tiller density counted on the ground, in small plots. In 2021, the model was used to apply N in small plots at two locations, where the obtained grain yield was the same whether N was applied based on tiller density, NDVI, or NDRE. From 2022 to 2023, the model was applied at six locations across the state on large scale growers’ fields to compare the amount of GS 25 N recommended by tiller density, NDVI, and NDRE. At three locations, NDVI and NDRE recommended the same N rates as the tiller density method, while at two locations, NDVI and NDRE recommended less N than tiller density. At one location, NDVI and NDRE recommended more N than tiller density. However, across all six locations, there was no difference in grain yield whether N was applied based on tiller density, NDVI, or NDRE. This study indicated that UAV-based NDVI and NDRE are excellent proxies for tiller density determination, and can be used to accurately and economically apply N at GS 25 in winter wheat production.
A sufficient nitrogen (N) supply is pivotal for high grain yield and desired grain protein content in wheat (
L.). Elucidation of physiological and molecular mechanisms underlying nitrogen use ...efficiency (NUE) will enhance our ability to develop new N-saving varieties in wheat. In this study, we analyzed two soft red winter wheat genotypes, VA08MAS-369 and VA07W-415, with contrasting NUE under limited N. Our previous study demonstrated that higher NUE in VA08MAS-369 resulted from accelerated senescence and N remobilization in flag leaves at low N. The present study revealed that VA08MAS-369 also exhibited higher nitrogen uptake efficiency (NUpE) than VA07W-415 under limited N. VA08MAS-369 consistently maintained root growth parameters such as maximum root depth, total root diameter, total root surface area, and total root volume under N limitation, relative to VA07W-415. Our time-course N content analysis indicated that VA08MAS-369 absorbed N more abundantly than VA07W-415 after the anthesis stage at low N. More efficient N uptake in VA08MAS-369 was associated with the increased expression of genes encoding a two-component high-affinity nitrate transport system, including four
s and three
s, in roots at low N. Altogether, these results demonstrate that VA08MAS-369 can absorb N efficiently even under limited N due to maintained root development and increased function of N uptake. The ability of VA08MAS-369 in N remobilization and uptake suggests that this genotype could be a valuable genetic material for the improvement of NUE in soft red winter wheat.
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