Age-dependent seasonal growth cessation in Populus.

In temperate and boreal regions, perennial plants adapt their annual growth cycle to the change of seasons. In natural forests, juvenile seedlings usually display longer growth seasons compared to adult trees to ensure their establishment and survival under canopy shade. However, how trees adjust their annual growth according to their age is not known. In this study, we show that age-dependent seasonal growth cessation is genetically controlled and found that the miR156-SPL3/5 module, a key regulon of vegetative phase change (VPC), also triggers age-dependent growth cessation in Populus trees. We show that miR156 promotes shoot elongation during vegetative growth, and its targets SPL3/5s function in the same pathway but as repressors. We find that the miR156-SPL3/5s regulon controls growth cessation in both leaves and shoot apices and through multiple pathways, but with a different mechanism compared to how the miR156-SPL regulon controls VPC in annual plants. Taken together, our results reveal an age-dependent genetic network in mediating seasonal growth cessation, a key phenological process in the climate adaptation of perennial trees.

The extent of multiallelic, co-editing of LIGULELESS1 in highly polyploid sugarcane tunes leaf inclination angle and enables selection of the ideotype for biomass yield.

Sugarcane (Saccharum spp. hybrid) is a prime feedstock for commercial production of biofuel and table sugar. Optimizing canopy architecture for improved light capture has great potential for elevating biomass yield. LIGULELESS1 (LG1) is involved in leaf ligule and auricle development in grasses. Here, we report CRISPR/Cas9-mediated co-mutagenesis of up to 40 copies/alleles of the putative LG1 in highly polyploid sugarcane (2n = 100-120, x = 10-12). Next generation sequencing revealed co-editing frequencies of 7.4%-100% of the LG1 reads in 16 of the 78 transgenic lines. LG1 mutations resulted in a tuneable leaf angle phenotype that became more upright as co-editing frequency increased. Three lines with loss of function frequencies of ~12%, ~53% and ~95% of lg1 were selected following a randomized greenhouse trial and grown in replicated, multi-row field plots. The co-edited LG1 mutations were stably maintained in vegetative progenies and the extent of co-editing remained constant in field tested lines L26 and L35. Next generation sequencing confirmed the absence of potential off targets. The leaf inclination angle corresponded to light transmission into the canopy and tiller number. Line L35 displaying loss of function in ~12% of the lg1 NGS reads exhibited an 18% increase in dry biomass yield supported by a 56% decrease in leaf inclination angle, a 31% increase in tiller number, and a 25% increase in internode number. The scalable co-editing of LG1 in highly polyploid sugarcane allows fine-tuning of leaf inclination angle, enabling the selection of the ideotype for biomass yield.

G × E × M analysis to define allometric rules between leaves and stems in wheat.

Allometric rules provide insights into the structure-function relationships across species and scales and are commonly used in ecology. The fields of agronomy, plant phenotyping and modeling also need simplifications such as allometric rules to reconcile data at different temporal and spatial levels (organs/canopy). This paper explores the variations in relationships for wheat regarding (i) the distribution of crop green area between leaves and stems, and (ii) the allocation of above-ground biomass between leaves and stems during the vegetative period, using a large dataset covering different years, countries, genotypes and management practices. Our results show that the relationship between leaf and stem area was linear, genotype-specific, and sensitive to radiation. The relationship between leaf and stem biomass depended on genotype and nitrogen fertilization. The mass per area, associating area and biomass for both leaf and stem, varied strongly by developmental stage and was significantly affected by environment and genotype. These allometric rules were evaluated with satisfactory performance, and their potential use is discussed with regard to current phenotyping techniques and plant/crop models. Our results enable the definition of models and minimum datasets required for characterizing diversity panels and making predictions in various G × E × M contexts.

Relationship between Height and Exposure in Multispectral Vegetation Index Response and Product Characteristics in a Traditional Olive Orchard.

The present research had two aims. The first was to evaluate the effect of height and exposure on the vegetative response of olive canopies' vertical axis studied through a multispectral sensor and on the qualitative and quantitative product characteristics. The second was to examine the relationship between multispectral data and productive characteristics. Six olive plants were sampled, and their canopy's vertical axis was subdivided into four sectors based on two heights (Top and Low) and two exposures (West and East). A ground-vehicle-mounted multispectral proximal sensor (OptRx from AgLeader®) was used to investigate the different behaviours of the olive canopy vegetation index (VI) responses in each sector. A selective harvest was performed, in which each plant and sector were harvested separately. Product characterisation was conducted to investigate the response of the products (both olives and oils) in each sector. The results of Tukey's test (p > 0.05) showed a significant effect of height for the VI responses, with the Low sector obtaining higher values than the Top sector. The olive product showed some height and exposure effect, particularly for the olives' dimension and resistance to detachment, which was statistically higher in the upper part of the canopies. The regression studies highlighted some relationships between the VIs and product characteristics, particularly for resistance to detachments (R2 = 0.44-0.63), which can affect harvest management. In conclusion, the results showed the complexity of the olive canopies' response to multispectral data collection, highlighting the need to study the vertical axis to assess the variability of the canopy itself. The relationship between multispectral data and product characteristics must be further investigated.

Assessment of vegetation change using NDVI, LST, and carbon analyses in Çankiri Karatekin University, Turkey.

Due to a rising population and urbanization, the green areas have been decreasing in cities, with a negative impact on air pollution, human health, and ecosystem. As part of the urban environment, university campuses contribute to urban ecosystem with their vegetation. This study aims to (1) assess the change of vegetative land cover of the Çankiri Karatekin University in Turkey and (2) evaluate its benefits to the ecosystem in terms of carbon sequestration, storage, and improvement of air quality by means of a simulation. In the study, the density and vegetation change were assessed with NDVI and LST analyses in ArcGIS; carbon emissions and air pollution benefits were estimated in i-Tree Canopy tool. The study showed that the healthy vegetation consisting of trees/shrubs and grass/herbaceous, which was 32.2% (28 ha) in 2000, increased to 85% (74 ha) in 2020 NDVI maps, and the surface temperature also increased between 2000 and 2020 in LST maps.The rise in vegetation as grass/herbaceous areas instead of trees/shrubs and the use of impervious buildings/roads on the land surface increased the land surface temperature. As a result of the analyses in the i-Tree-Canopy tool, it was estimated that the trees/shrubs and grass/herbaceous vegetation canopy covering 31.42% of the study area removed a total of 512,845.65 g of pollutant gas and particles from the air, 20.79 tonnes of carbon sequestered annually, and 522.01 tonnes of carbon stored by vegetative land cover. In the simulation, where 32.62% soil/bare ground areas were converted to trees/shrubs in order to improve vegetation cover in the area, it was determined that it contributed 5 times more to the ecosystem service value for removing pollutants from the air, carbon storage, and improving the ecosystem. It was revealed that the vegetative land cover formed by tree/shrub species should be increased in the campus in the future. The study method model serves as a tool for planning and designing eco-friendly urban environment.

Seasonal trends in leaf-level photosynthetic capacity and water use efficiency in a North American Eastern deciduous forest and their impact on canopy-scale gas exchange.

Vegetative transpiration (E) and photosynthetic carbon assimilation (A) are known to be seasonally dynamic, with changes in their ratio determining the marginal water use efficiency (WUE). Despite an understanding that stomata play a mechanistic role in regulating WUE, it is still unclear how stomatal and nonstomatal processes influence change in WUE over the course of the growing season. As a result, limited understanding of the primary physiological drivers of seasonal dynamics of canopy WUE remains one of the largest uncertainties in earth system model projections of carbon and water exchange in temperate deciduous forest ecosystems. We investigated seasonal patterns in leaf-level physiological, hydraulic, and anatomical properties, including the seasonal progress of the stomatal slope parameter (g1 ; inversely proportional to WUE) and the maximum carboxylation rate (Vcmax ). Vcmax and g1 were seasonally variable; however, their patterns were not temporally synchronized. g1 generally showed an increasing trend until late in the season, while Vcmax peaked during the midsummer months. Seasonal progression of Vcmax was primarily driven by changes in leaf structural, and anatomical characteristics, while seasonal changes in g1 were most strongly related to changes in Vcmax and leaf hydraulics. Using a seasonally variable Vcmax and g1 to parameterize a canopy-scale gas exchange model increased seasonally aggregated A and E by 3% and 16%, respectively.

Shading by giant kelp canopy can restrict the invasiveness of Undaria pinnatifida (Laminariales, Phaeophyceae).

The spread of non-indigenous and invasive seaweeds has increased worldwide, and their potential effects on native seaweeds have raised concern. Undaria pinnatifida is considered among the most prolific non-indigenous species. This species has expanded rapidly in the Northeast Pacific, overlapping with native communities such as the iconic giant kelp forests (Macrocystis pyrifera). Canopy shading by giant kelp has been argued to be a limiting factor for the presence of U. pinnatifida in the understory, thus its invasiveness capacity. However, its physiological plasticity under light limitation remains unclear. In this work, we compared the physiology and growth of juvenile U. pinnatifida and M. pyrifera sporophytes transplanted to the understory of a giant kelp forest, to juveniles growing outside of the forest. Extreme low light availability compared to that outside (~0.2 and ~4.4 mol photon ⋅ m-2 ⋅ d-1 , respectively) likely caused a "metabolic energy crisis" in U. pinnatifida, thus restricting its photoacclimation plasticity and nitrogen acquisition, ultimately reducing its growth. Despite M. pyrifera juveniles showing photoacclimatory responses (e.g., increases in photosynthetic efficiency and lower compensation irradiance, Ec ), their physiological/vegetative status deteriorated similarly to U. pinnatifida, which explains the low recruitment inside the forest. Generally, our results revealed the ecophysiological basis behind the limited growth and survival of juvenile U. pinnatifida sporophytes in the understory.

Sensing and Mapping the Effects of Cow Trampling on the Soil Compaction of the Montado Mediterranean Ecosystem.

The economic and environmental sustainability of extensive livestock production systems requires the optimisation of soil management, pasture production and animal grazing. Soil compaction is generally viewed as an indicator of soil degradation processes and a determinant factor in crop productivity. In the Montado silvopastoral ecosystem, characteristic of the Iberian Peninsula, animal trampling is mentioned as a variable to consider in soil compaction. This study aims: (i) to assess the spatial variation in the compaction profile of the 0-0.30 m deep soil layer over several years; (ii) to evaluate the effect of animal trampling on soil compaction; and (iii) to demonstrate the utility of combining various technological tools for sensing and mapping indicators of soil characteristics (Cone Index, CI; and apparent electrical conductivity, ECa), of pastures' vegetative vigour (Normalised Difference Vegetation Index, NDVI) and of cows' grazing zones (Global Positioning Systems, GPS collars). The significant correlation between CI, soil moisture content (SMC) and ECa and between ECa and soil clay content shows the potential of using these expedient tools provided by the development of Precision Agriculture. The compaction resulting from animal trampling was significant outside the tree canopy (OTC) in the four evaluated dates and in the three soil layers considered (0-0.10 m; 0.10-0.20 m; 0.20-0.30 m). However, under the tree canopy (UTC), the effect of animal trampling was significant only in the 0-0.10 m soil layer and in three of the four dates, with a tendency for a greater CI at greater depths (0.10-0.30 m), in zones with a lower animal presence. These results suggest that this could be a dynamic process, with recovery cycles in the face of grazing management, seasonal fluctuations in soil moisture or spatial variation in specific soil characteristics (namely clay contents). The NDVI shows potential for monitoring the effect of livestock trampling during the peak spring production phase, with greater vigour in areas with less animal trampling. These results provide good perspectives for future studies that allow the calibration and validation of these tools to support the decision-making process of the agricultural manager.

Seasonal climate conditions impact the effectiveness of improving photosynthesis to increase soybean yield.

Context: Photosynthetic stimulations have shown promising outcomes in improving crop photosynthesis, including soybean. However, it is still unclear to what extent these changes can impact photosynthetic assimilation and yield under long-term field climate conditions. Objective: In this paper, we present a systematic evaluation of the response of canopy photosynthesis and yield to two critical parameters in leaf photosynthesis: the maximum carboxylation rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Vcmax) and the maximum electron transport of the ribulose-1,5-bisphosphate regeneration rate (Jmax). Methods: Using the field-scale crop model Soybean-BioCro and ten years of observed climate data in Urbana, Illinois, U.S., we conducted sensitivity experiments to estimate the changes in canopy photosynthesis, leaf area index, and biomass due to the changes in Vcmax and Jmax. Results: The results show that 1) Both the canopy photosynthetic assimilation (An) and pod biomass yields were more sensitive to the changes in Jmax, particularly at high atmospheric carbon-dioxide concentrations ([CO2]); 2) Higher [CO2] undermined the effectiveness of increasing the two parameters to improve An and yield; 3) Under the same [CO2], canopy light interception and canopy respiration were key factors that undermined improvements in An and yield; 4) A canopy with smaller leaf area index tended to have a higher yield improvement, and 5) Increases in assimilations and yields were highly dependent on growing-season climatic conditions. The solar radiation, temperature, and relative humidity were the main climate drivers that impacted the yield improvement, and they had opposite correlations with improved yield during the vegetative phase compared to the reproductive phase. Conclusions: In a world with elevated [CO2], genetic engineering crop photosynthesis should focus more on improving Jmax. Further, long-term climate conditions and seasonal variations must be considered to determine the improvements in soybean canopy photosynthesis and yield at the field scale. Implications: Quantifying the effectiveness of changing Vcmax and Jmax helps understand their individual and combined contributions to potential improvements in assimilation and yield. This work provides a framework for evaluating how altering the photosynthetic rate parameters impacts soybean yield and assimilation under different seasonal climate scenarios at the field scale.

Impacts of anthropogenic disturbance on forest vegetation cover, health, and diversity within Doma forest reserve, Nigeria.

Forest encroachment is a common practice that has led to the destruction of canopy trees in the Guinea savanna part of Nigeria. This study investigated the influence of human activities on vegetation health and species composition of Doma forest reserve located in Nasarawa State, Nigeria. Landsat satellite data from 1986 to 2021 were utilized to assess forest cover change, land surface temperature (LST), and vegetation indices (VIs). The results show that dense woodland vegetation in the Doma forest reserve depreciated between 1991 and 1999 by 17.82% before increasing by 7.37% between 1999 and 2021. Similarly, vegetation greenness (measured by the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Vegetation Index (GCVI), and leaf area index (LAI)) of the forest mirrored the changes observed in the forest cover. The LST extracted for each year was correlated with all VIs, and an inverse relationship was observed in all relationships analyzed. The decline in greenness between 1999 and 2011 was attributed to increasing lumbering, bush burning, and sand dredging activities. Results also showed the current diversity state (H1 = 0.23), evenness (0.63), and the volume of tree (1.31 m3) species in the heart of the Doma forest reserve. However, a high (25%) native tree species in the Fabaceae family correlated with a dramatic increase in the VIs and an increase in dense woodland cover indicating the importance of Fabaceae in forest ecosystem regeneration.

Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes.

Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions, while trees are more limited by moisture availability during the dry season. We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopies of two lowland tropical forests with contrasting rainfall regimes in Panama. In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability. Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials that are critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.

AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry.

High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.

Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.

The recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 2021 vegetative seasons. Leaf chlorophyll content, leaf nitrogen content, leaf water content and the corresponding canopy level traits scaled through leaf area index were estimated using a hybrid retrieval scheme based on PROSAIL-PRO radiative transfer simulations coupled with a Gaussian processes regression algorithm. Active learning algorithms were used to optimise the initial set of simulated data by extracting only the most informative samples. The accuracy of the proposed retrieval scheme was evaluated against a broad ground dataset collected in 2020 in correspondence of three PRISMA overpasses. The results obtained were positive for all the investigated variables. At the leaf level, the highest accuracy was obtained for leaf nitrogen content (LNC: r2=0.87, nRMSE=7.5%), while slightly worse results were achieved for leaf chlorophyll content (LCC: r2=0.67, nRMSE=11.7%) and leaf water content (LWC: r2=0.63, nRMSE=17.1%). At the canopy level, a significantly higher accuracy was observed for nitrogen content (CNC: r2=0.92, nRMSE=5.5%) and chlorophyll content (CCC: r2=0.82, nRMSE=10.2%), whereas comparable results were obtained for water content (CWC: r2=0.61, nRMSE=16%). The developed models were additionally tested against an independent dataset collected in 2021 to evaluate their robustness and exportability. The results obtained (i. e., LCC: r2=0.62, nRMSE=27.9%; LNC: r2=0.35, nRMSE=28.4%; LWC: r2=0.74, nRMSE=20.4%; LAI: r2=0.84, nRMSE=14.5%; CCC: r2=0.79, nRMSE=18.5%; CNC: r2=0.62, nRMSE=23.7%; CWC: r2=0.92, nRMSE=16.6%) evidence the transferability of the hybrid approach optimised through active learning for most of the investigated traits. The developed models were then used to map the spatial and temporal variability of the crop traits from the PRISMA images. The high accuracy and consistency of the results demonstrates the potential of spaceborne imaging spectroscopy for crop monitoring, paving the path towards routine retrievals of multiple crop traits over large areas that could drive more effective and sustainable agricultural practices worldwide.

First report of Colletotrichum shisoi causing anthracnose of Perilla frutescens in the United States.

Perilla mint (Perilla frutescens (L.) Britt.) is an annual plant native to Asia and considered invasive in North America where it has escaped cultivation as an ornamental (Miller 1947; Swearingen et al. 2010). In August 2021, an anthracnose disease was observed on invasive perilla found along the disturbed margins of a forest in Frederick County, Maryland, United States. Symptoms included necrotic lesions with chlorotic halos, were concentrated in the lower canopy, and caused premature defoliation of lower leaves (Figure S1). Leaves from four plants were surface sterilized by rinsing for 30 s in 70% ethanol, 60 s in 0.8% NaClO, and 60 s in sterile water and then incubated on 2% water agar under ambient laboratory conditions to permit sporulation. After three days, spores that exuded from individual lesions were streaked onto acidified potato dextrose agar. Two single-conidium isolates were recovered from each plant. All eight isolates were identified to species using DNA sequences. A single isolate (21-067) was selected at random for morphological characterization and completion of Koch's postulates. Morphological features were recorded after seven days of growth on synthetic low-nutrient agar (SNA) and potato dextrose agar (PDA) incubated at 22°C under 12 hr UV-B and white fluorescent lighting. Measurements were based on a minimum of 20 observations per structure. Cultures on SNA were flat, hyaline to pale salmon, lacked sporodochia and grew at a rate of 1.3 mm day-1 (n = 3). Vegetative hyphal width was (minimum-maximum) 1.5-4.0 µm, (average ± standard deviation) 2.7 ± 0.9 µm. Conidiophores arose directly from hyphae, were hyaline, smooth, unbranched and measured 40.0-180.0 x 1.5-4.5 µm, 102.3 ± 33.9 x 2.7 ± 0.8 µm. Conidia were single celled, straight, hyaline, glabrous, rounded at both ends and measured 10.0-20.0 x 3.8-6.3 µm, 15.4 ± 2.5 x 4.9 ± 0.7 µm. Setae, observed only on PDA, were pale brown, 1-2 septate, straight, blunt tipped and measured 42.5-97.5 µm, 70.8 ± 13.4 µm. Appressoria formed on PDA were single-celled, pigmented, smooth, and obovoid with entire margins, measuring 5.2-7.7 x 8.6-13.8 µm, 6.8 ± 0.6 x 10.8 ± 1.4 µm. These characteristics were consistent with members of the Colletotrichum destructivum species complex. Partial DNA sequences from five loci were amplified following the procedures of Damm et al. 2014: internal transcribed spacer region of rDNA (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), chitin synthase 1 (CHS-1), actin (ACT), and beta-tubulin (TUB2). Pairwise sequence comparisons to references using the Blastn algorithm found 99.8% similarity between isolate 21-067 and ex-type C. shisoi isolate JCM 31818: MH660930 (ITS, 545/546 bp), MH660931 (GAPDH,192/192 bp), MH660929 (CHS-1, 278/280 bp), MH660928 (ACT, 262/262 bp), and MH660932 (TUB2, 511/511 bp) (Altschul et al. 1990; Gan et al. 2019). Subsequently, sequences from all eight isolates were aligned with Clustal Omega (Sievers and Higgins 2018), concatenated, and used in a Bayesian phylogenetic reconstruction (Huelsenbeck and Ronquist 2001) of the C. destructivum species complex (Figure S2), confirming the species identity as C. shisoi. Sequences were deposited in NCBI GenBank under accession numbers OM865277-OM865284 and OM885059-OM885090. Koch's postulates were fulfilled using perilla grown in a greenhouse until second true leaves emerged. Inoculum washed from two-week-old fungal cultures grown on potato dextrose agar was adjusted to 4 x 104 conidia mL-1 and applied to three replicate plants with an aspirator until runoff. Three plants were sprayed with sterile water as a negative control. Plants were covered with plastic bags for 72 hrs and maintained in a growth chamber at 20°C and 80% RH for 14 days. Inoculated plants displayed disease symptoms similar to those observed under field conditions, and control plants did not develop symptoms. Colletotrichum shisoi was reisolated from symptomatic tissue and reidentified based on morphology. The experiment was completed twice. To the authors' knowledge, this is the first report of C. shisoi on P. frutescens in the United States. Colletotrichum shisoi has not been reported as a pathogen on other plants in the United States and may have potential use as a biological control agent for invasive perilla.

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing.

The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive stages under flooded conditions. Unlike popular deep-learning-based approaches requiring training data and computational resources, we propose a simple image-processing pipeline following the empirical principles of synchronously emerging leaves and tillers in rice morphogenesis. Field images were taken by an unmanned aerial vehicle at a very low flying height for UAV imaging-1.5 to 3 m above the rice canopy. Subsequently, the proposed image-processing pipeline was used, which includes binarization, skeletonization, and leaf-tip detection, to count the number of long-growing leaves. The tiller number was estimated from the number of long-growing leaves. The estimated tiller number in a 1.1 m × 1.1 m area is significantly correlated with the actual number of tillers, with 60% of hills having an error of less than ±3 tillers. This study demonstrates the potential of the proposed image-sensing-based tiller-counting method to help agronomists with efficient, non-destructive field phenotyping.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

Effectiveness of vegetation indices and UAV-multispectral imageries in assessing the response of hybrid maize (Zea mays L.) to water deficit stress under field environment.

Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.

Vertical distribution of gas exchanges and their integration throughout the entire canopy in a maize field.

Fluxes of carbon and water along a vertical profile within a canopy, particularly the associations between canopy and ecosystem levels, are not well studied. In this study, gas exchange along the vertical profile in a maize canopy was examined. The relationships between leaf- and ecosystem-level carbon and water fluxes were compared. The results from research conducted over two growing seasons showed that during vegetative growth, the top and middle leaf layers in the canopy contribute most to the carbon and water fluxes of the entire canopy. During the grain-filling stage, gas exchange processes were performed mostly in the middle leaves with and near the ears. Significant relationships were observed between the net ecosystem CO2 exchange rate (NEE) plus soil respiration and the assumed canopy levels (Acanopy) and between evapotranspiration rates at the ecosystem (ET) and assumed canopy levels (Ecanopy). This highlights the close associations between these parameters by integrating the leaf gas exchange rates measured in a conventional leaf cuvette and those at the ecosystem level via the eddy covariance technique. These results improve our understanding of how carbon assimilation varies vertically within a canopy, highlighting the critical role of ear leaves.

Field-based remote sensing models predict radiation use efficiency in wheat.

Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.