Back to the future for drought tolerance

Global agriculture faces increasing pressure to produce more food with fewer resources. Drought, exacerbated by climate change, is a major agricultural constraint costing the industry an estimated US$80 billion per year in lost production. Wild relatives of domesticated crops, including wheat (Triticum spp.) and barley (Hordeum vulgare L.), are an underutilized source of drought tolerance genes. However, managing their undesirable characteristics, assessing drought responses, and selecting lines with heritable traits remains a significant challenge. Here, we propose a novel strategy of using multi-trait selection criteria based on high-throughput spectral images to facilitate the assessment and selection challenge. The importance of measuring plant capacity for sustained carbon fixation under drought stress is explored, and an image-based transpiration efficiency (iTE) index obtained via a combination of hyperspectral and thermal imaging, is proposed. Incorporating iTE along with other drought-related variables in selection criteria will allow the identification of accessions with diverse tolerance mechanisms. A comprehensive approach that merges high-throughput phenotyping and de novo domestication is proposed for developing drought-tolerant prebreeding material and providing breeders with access to gene pools containing unexplored drought tolerance mechanisms.

Divergent abiotic spectral pathways unravel pathogen stress signals across species

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain

Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.

High-resolution imagery acquired from an unmanned platform to estimate biophysical and geometrical parameters of olive trees under different irrigation regimes

The experiments were conducted in a fully-productive olive orchard (cv. Frantoio) at the experimental farm of University of Pisa at Venturina (Italy) in 2015 to assess the ability of an unmanned aerial vehicle (UAV) equipped with RGB-NIR cameras to estimate leaf area index (LAI), tree height, canopy diameter and canopy volume of olive trees that were either irrigated or rainfed. Irrigated trees received water 4-5 days a week (1348 m3 ha-1), whereas the rainfed ones received a single irrigation of 19 m3 ha-1 to relieve the extreme stress. The flight altitude was 70 m above ground level (AGL), except for one flight (50 m AGL). The Normalized Difference Vegetation Index (NDVI) was calculated by means of the map algebra technique. Canopy volume, canopy height and diameter were obtained from the digital surface model (DSM) obtained through automatic aerial triangulation, bundle block adjustment and camera calibration methods. The NDVI estimated on the day of the year (DOY) 130 was linearly correlated with both LAI and leaf chlorophyll measured on the same date (R2 = 0.78 and 0.80, respectively). The correlation between the on ground measured canopy volumes and the ones by the UAV-RGB camera techniques yielded an R2 of 0.71-0.86. The monthly canopy volume increment estimated from UAV surveys between (DOY) 130 and 244 was highly correlated with the daily water stress integral of rainfed trees (R2 = 0.99). The effect of water stress on the seasonal pattern of canopy growth was detected by these techniques in correspondence of the maximum level of stress experienced by the rainfed trees. The highest level of accuracy (RMSE = 0.16 m) in canopy height estimation was obtained when the flight altitude was 50 m AGL, yielding an R2 value of 0.87 and an almost 1:1 ratio of measured versus estimated canopy height.

Hotspots in the genomic architecture of field drought responses in wheat as breeding targets

Wheat can adapt to most agricultural conditions across temperate regions. This success is the result of phenotypic plasticity conferred by a large and complex genome composed of three homoeologous genomes (A, B, and D). Although drought is a major cause of yield and quality loss in wheat, the adaptive mechanisms and gene networks underlying drought responses in the field remain largely unknown. Here, we addressed this by utilizing an interdisciplinary approach involving field water status phenotyping, sampling, and gene expression analyses. Overall, changes at the transcriptional level were reflected in plant spectral traits amenable to field-level physiological measurements, although changes in photosynthesis-related pathways were found likely to be under more complex post-transcriptional control. Examining homoeologous genes with a 1:1:1 relationship across the A, B, and D genomes (triads), we revealed a complex genomic architecture for drought responses under field conditions, involving gene homoeolog specialization, multiple gene clusters, gene families, miRNAs, and transcription factors coordinating these responses. Our results provide a new focus for genomics-assisted breeding of drought-tolerant wheat cultivars.

Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security¹, ecosystem integrity and societies in general^2-5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment⁶. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa's geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage^7,8. X. fastidiosa can infect more than 350 plant species worldwide⁹, and early detection is critical for its eradication⁸. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content

This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R² (0.32) were found using both the spectral (NDSI—Ri, 750 to 840 nm and Rj, ±720–736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R²≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: −0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.

Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize

BACKGROUND

Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement.

RESULTS

We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield.

CONCLUSION

This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.

Soil temperature determines the reaction of olive cultivars to Verticillium dahliae pathotypes

BACKGROUND

Development of Verticillium wilt in olive, caused by the soil-borne fungus Verticillium dahliae, can be influenced by biotic and environmental factors. In this study we modeled i) the combined effects of biotic factors (i.e., pathotype virulence and cultivar susceptibility) and abiotic factors (i.e., soil temperature) on disease development and ii) the relationship between disease severity and several remote sensing parameters and plant stress indicators.

METHODOLOGY

Plants of Arbequina and Picual olive cultivars inoculated with isolates of defoliating and non-defoliating V. dahliae pathotypes were grown in soil tanks with a range of soil temperatures from 16 to 32°C. Disease progression was correlated with plant stress parameters (i.e., leaf temperature, steady-state chlorophyll fluorescence, photochemical reflectance index, chlorophyll content, and ethylene production) and plant growth-related parameters (i.e., canopy length and dry weight).

FINDINGS

Disease development in plants infected with the defoliating pathotype was faster and more severe in Picual. Models estimated that infection with the defoliating pathotype was promoted by soil temperatures in a range of 16 to 24°C in cv. Picual and of 20 to 24°C in cv. Arbequina. In the non-defoliating pathotype, soil temperatures ranging from 16 to 20°C were estimated to be most favorable for infection. The relationship between stress-related parameters and disease severity determined by multinomial logistic regression and classification trees was able to detect the effects of V. dahliae infection and colonization on water flow that eventually cause water stress.

CONCLUSIONS

Chlorophyll content, steady-state chlorophyll fluorescence, and leaf temperature were the best indicators for Verticillium wilt detection at early stages of disease development, while ethylene production and photochemical reflectance index were indicators for disease detection at advanced stages. These results provide a better understanding of the differential geographic distribution of V. dahliae pathotypes and to assess the potential effect of climate change on Verticillium wilt development.

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery.

Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery

Physical principles applied to remote sensing data are key to successfully quantifying vegetation physiological condition from the study of the light interaction with the canopy under observation. We used the fluorescence-reflectance-transmittance (FRT) and PROSPECT leaf models to simulate reflectance as a function of leaf biochemical and fluorescence variables. A series of laboratory measurements of spectral reflectance at leaf and canopy levels and a modeling study were conducted, demonstrating that effects of chlorophyll fluorescence (CF) can be detected by remote sensing. The coupled FRT and PROSPECT model enabled CF and chlorophyll a + b (Ca + b) content to be estimated by inversion. Laboratory measurements of leaf reflectance (r) and transmittance (t) from leaves with constant Ca + b allowed the study of CF effects on specific fluorescence-sensitive indices calculated in the Photosystem I (PS-I) and Photosystem II (PS-II) optical region, such as the curvature index [CUR; (R675.R690)/R2(683)]. Dark-adapted and steady-state fluorescence measurements, such as the ratio of variable to maximal fluorescence (Fv/Fm), steady state maximal fluorescence (F'm), steady state fluorescence (Ft), and the effective quantum yield (delta F/F'm) are accurately estimated by inverting the FRT-PROSPECT model. A double peak in the derivative reflectance (DR) was related to increased CF and Ca + b concentration. These results were consistent with imagery collected with a compact airborne spectrographic imager (CASI) sensor from sites of sugar maple (Acer saccharum Marshall) of high and low stress conditions, showing a double peak on canopy derivative reflectance in the red-edge spectral region. We developed a derivative chlorophyll index (DCI; calculated as D705/D722), a function of the combined effects of CF and Ca + b content, and used it to detect vegetation stress.