Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.

Water scarcity is the primary environmental constraint affecting wheat growth and production and is increasingly exacerbated due to climatic fluctuation, which jeopardizes future food security. Most breeding efforts to improve wheat yields under drought have focused on above-ground traits. Root traits are closely associated with various drought adaptability mechanisms, but the genetic variation underlying these traits remains untapped, even though it holds tremendous potential for improving crop resilience. Here, we examined this potential by re-introducing ancestral alleles from wild emmer wheat (Triticum turgidum ssp. dicoccoides) and studied their impact on root architecture diversity under terminal drought stress. We applied an active sensing electrical resistivity tomography approach to compare a wild emmer introgression line (IL20) and its drought-sensitive recurrent parent (Svevo) under field conditions. IL20 exhibited greater root elongation under drought, which resulted in higher root water uptake from deeper soil layers. This advantage initiated at the pseudo-stem stage and increased during the transition to the reproductive stage. The increased water uptake promoted higher gas exchange rates and enhanced grain yield under drought. Overall, we show that this presumably ‘lost’ drought-induced mechanism of deeper rooting profile can serve as a breeding target to improve wheat productiveness under changing climate.

Chickpea (Cicer arietinum L.) is a key legume crop grown in many semi-arid areas. Traditionally, chickpea is a rainfed spring crop, but in certain countries it has become an irrigated crop. The main objective of this study was to evaluate the ability of Unmanned Aerial Systems (UAS) imaging platform with an integrated RGB camera to provide estimations of leaf area index (LAI), biomass, and yield for chickpea during the irrigation period. Two field trials were conducted in 2019 and 2020, in which chickpea plants were subjected to five and six irrigation regimes, respectively. Eight vegetation indexes (VIs) and three morphological parameters were estimated from the RGB images. In parallel, biomass was determined, LAI was measured manually, and yield was determined at full maturity. In total, 294 plant samples were acquired and analyzed over the two years. Firstly, each of the VIs and morphological parameters were correlated separately against the two biophysical parameters and yield. Then, all the VIs and morphological parameters were analyzed together, and two statistical models, partial least squares regression (PLS-R) and support vector machine (SVM); were used to predict biomass and LAI. The yield was predicted using multi-linear regression (MLR). When each index or morphological parameter was analyzed separately, plant height and some of the VIs provided adequate predictions of the biophysical parameters in 2019 (R2 values ≥ 0.50) but failed (R2 values ≤ 0.25) in 2020. The integration of the VIs with the morphological parameters and the use of PLS-R and SVM models increased the accuracy level for both biophysical parameters (R2 ranged from 0.31 to 0.96) and mitigated the lack of consistency between the years. The SVM model was superior to the PLS-R model in both biophysical parameters. The R2 values for the combined 2019 and 2020 biomass model increased, at the model-testing stage, from 0.62 to 0.96 and the RMSE values dropped from 1778 to 490 kg ha−1. The ability of the SVM model to estimate chickpea biomass and LAI can provide convenient support for different management decisions, including timing and amount of irrigation and harvest date.

Sesame is mainly cultivated under traditional, low-input agro-systems. Recent breeding developments promoted the modernization and mechanization of sesame cultivation. However, only a few articles have been published concerning fertilization requirements for both modern and traditional agro-systems. In field trials at two locations, we determined the response of irrigated sesame to nitrogen (N). Three promising sesame lines were tested combining two irrigation levels with four N levels. At a high irrigation level, N had a significant effect on growth, branching, and consequently, seed yield exceeding two-ton ha−1. A high N doze was accompanied by a decrease in the photosynthetic rate and leaf water potential. The δ13C confirmed lower stomatal conductance under high N treatments. Under deficit irrigation, the N level had a minor effect on the monitored parameters, indicating N fertilization was not efficient. Seed oil content was negatively correlated with seed N concentration. Our results question the necessity of N application when water is limited, as N fertilization promotes vigorous development that rapidly depletes soil water. Thus, water availability should be considered when developing an N management strategy. For high-yielding agro-systems, roughly 80–120 ​kg ​ha−1 N is required for optimal yield, bearing in mind the negative association between seed-N and oil content.

Sesame is an ancient oilseed crop containing many valuable nutritional components. Recently, the demand for sesame seeds and their products has increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame are limited. In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess the accuracy of prediction for nine important agronomic traits in sesame using single- and multi-environment analyses. In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39-0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%\-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.Competing Interest StatementThe authors have declared no competing interest.

Abstract Two opposing models currently dominate Near Eastern plant domestication research. The core area-one event model depicts a knowledge-based, conscious, geographically centered, rapid single-event domestication, while the protracted-autonomous model emphasizes a non-centered, millennia-long process based on unconscious dynamics. The latter model relies, in part, on quantitative depictions of diachronic changes (in archaeological remains) in proportions of spikelet shattering to non-shattering, towards full dominance of the non-shattering (domesticated) phenotypes in cultivated cereal populations. Recent wild wheat genome assembly suggests that shattering and non-shattering spikelets may originate from the same (individual) genotype. Therefore, their proportions among archaeobotanical assemblages cannot reliably describe the presumed protracted-selection dynamics underlying wheat domestication. This calls for a reappraisal of the ?domestication syndrome? concept associated with cereal domestication.

To meet the ever-growing global population necessities, integrating climate-change-relevant plant traits into breeding programs is required. Developing new tools for fast and accurate estimation of chlorophyll parameters, chlorophyll a (Chl-a) content, chlorophyll b (Chl-b) content, and their ratio (Chl-a/b), can promote breeding programs of wheat with enhanced climate adaptability. Spectral reflectance of leaves is affected by changes in pigment concentration and can be used to estimate chlorophyll parameters. The current study identified and validated the top known spectral indices and developed new vegetation indices (VIs) for Chl-a and Chl-b content estimation and used them to non-destructively estimate Chl-a/b values and compare them to hyperspectral estimations. Three wild emmer introgression lines, with contrasting drought stress responsiveness dynamics, were selected. Well-watered and water-limited irrigation regimes were applied. The wheat leaves were spectrally measured with a handheld spectrometer to acquire their reflectance in the 330 to 790 nm range. Regression models based on calculated VIs as well as all hyperspectral curves were calibrated and validated against chlorophyll extracted values. The developed normalized difference spectral indices (NDSIs) resulted in high accuracy of Chl-a (NDSI415,614) and Chl-b (NDSI406,525) estimation, allowing for indirect non-destructive estimation of Chl-a/b with root mean square error (RMSE) values that could fit 6 to 10 times in the range of the measured values. They also performed similarly to the hyperspectral models. Altogether, we present here a new tool for a non-destructive estimation of Chl-a/b, which can serve as a basis for future breeding efforts of climate-resilient wheat as well as other crops.

Drought intensity as experienced by plants depends upon soil moisture status and atmospheric variables such as temperature, radiation, and air vapour pressure deficit. Although the role of shoot architecture with these edaphic and atmospheric factors is well characterized, the extent to which shoot and root dynamic interactions as a continuum are controlled by genotypic variation is less well known. Here, we targeted these interactions using a wild emmer wheat introgression line (IL20) with a distinct drought-induced shift in the shoot-to-root ratio and its drought-sensitive recurrent parent Svevo. Using a gravimetric platform, we show that IL20 maintained higher root water influx and gas exchange under drought stress, which supported a greater growth. Interestingly, the advantage of IL20 in root water influx and transpiration was expressed earlier during the daily diurnal cycle under lower vapour pressure deficit and therefore supported higher transpiration efficiency. Application of a structural equation model indicates that under drought, vapour pressure deficit and radiation are antagonistic to transpiration rate, whereas the root water influx operates as a feedback for the higher atmospheric responsiveness of leaves. Collectively, our results suggest that a drought-induced shift in root-to-shoot ratio can improve plant water uptake potential in a short preferable time window during early morning when vapour pressure deficit is low and the light intensity is not a limiting factor for assimilation.

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data. However, variability in spatial and spectral resolution can affect the efficiency of such dataset's fusion. In this study, to address the modality bias, the input data was projected to a shared latent space using cross-modal generative approaches or guided unsupervised transformation. The proposed adversarial networks and variational encoder-based strategies used bi-directional transformations to model the cross-domain correlation without using cross-domain correspondence. It may be noted that an interpolation-based convolution was adopted instead of the normal convolution for learning the features of the point spectral data (ground spectra). The proposed generative adversarial network-based approach employed dynamic time wrapping based layers along with a cyclic consistency constraint to use the minimal number of unlabeled samples, having cross-domain correlation, to compute a cross-modal generative latent space. The proposed variational encoder-based transformation also addressed the cross-modal resolution differences and limited availability of cross-domain samples by using a mixture of expert-based strategy, cross-domain constraints, and adversarial learning. In addition, the latent space was modelled to be composed of modality independent and modality dependent spaces, thereby further reducing the requirement of training samples and addressing the cross-modality biases. An unsupervised covariance guided transformation was also proposed to transform the labelled samples without using cross-domain correlation prior. The proposed latent space transformation approaches resolved the requirement of cross-domain samples which has been a critical issue with the fusion of multi-modal Earth observation data. This study also proposed a latent graph generation and graph convolutional approach to predict the labels resolving the domain discrepancy and cross-modality biases. Based on the experiments over different standard benchmark airborne datasets and real-world UAV datasets, the developed approaches outperformed the prominent hyperspectral panchromatic sharpening, image fusion, and domain adaptation approaches. By using specific constraints and regularizations, the network developed was less sensitive to network parameters, unlike in similar implementations. The proposed approach illustrated improved generalizability in comparison with the prominent existing approaches. In addition to the fusion-based classification of the multispectral and hyperspectral datasets, the proposed approach was extended to the classification of hyperspectral airborne datasets where the latent graph generation and convolution were employed to resolve the domain bias with a small number of training samples. Overall, the developed transformations and architectures will be useful for the semantic interpretation and analysis of multimodal data and are applicable to signal processing, manifold learning, video analysis, data mining, and time series analysis, to name a few.

SUMMARY Autonomous seed dispersal is a critical trait for wild plants in natural ecosystems; however, for domesticated crop-plants it can lead to significant yield losses. While seed shattering was a major selection target during the initial domestication of many crops, this trait is still targeted in breeding programs, especially in ?orphan crops? such as sesame, whose capsules dehisce upon ripening. Here we used a mapping population derived from a cross between wild-type (dehiscent)???indehiscent lines to test the hypothesis that the selection against indehiscent alleles in sesame is a consequence of complex genetic interactions associated with yield reduction. We identified a major pleiotropic locus, SiKANADI1, associated with abnormal hyponastic leaf and indehiscent capsule, and genetically dissected its underlying mechanism using a set of near-isogenic lines. Transcriptional, anatomical and physiological information shed light, for the first time, on the polar regulatory gene network in sesame. The pleiotropic effect of SiKANADI1 on leaf and capsule structure and its influence on photosynthetic capacity and final yield are thoroughly characterized. Overall, our results provide new insights on the genetic and morphological mechanisms regulating capsule indehiscence in sesame, and discuss their evolutionary consequences and potential for future sesame breeding.

Drought intensity as experienced by plants depends upon soil moisture status and atmospheric variables such as temperature, radiation, and air vapour pressure deficit. Although the role of shoot architecture with these edaphic and atmospheric factors is well characterized, the extent to which shoot and root dynamic interactions as a continuum are controlled by genotypic variation is less well known. Here, we targeted these interactions using a wild emmer wheat introgression line (IL20) with a distinct drought-induced shift in the shoot-to-root ratio and its drought-sensitive recurrent parent Svevo. Using a gravimetric platform, we show that IL20 maintained higher root water influx and gas exchange under drought stress, which supported a greater growth. Interestingly, the advantage of IL20 in root water influx and transpiration was expressed earlier during the daily diurnal cycle under lower vapour pressure deficit and therefore supported higher transpiration efficiency. Application of a structural equation model indicates that under drought, vapour pressure deficit and radiation are antagonistic to transpiration rate, whereas the root water influx operates as a feedback for the higher atmospheric responsiveness of leaves. Collectively, our results suggest that a drought-induced shift in root-to-shoot ratio can improve plant water uptake potential in a short preferable time window during early morning when vapour pressure deficit is low and the light intensity is not a limiting factor for assimilation.

Herbicides are the most efficient and cost-effective means of weed management [...]

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation. Wild emmer introgressions in wheat introduce divergent water stress responses associated with a shift in root-to-shoot growth dynamics for enhanced stress adaptation.

Moringa oleifera Lam. (moringa hereafter) is cultivated as a new summer super-forage field crop in Israel, yet no weed control protocol has been developed for it. The objective of the study was to develop an integrated weed management (IWM) practice for the moringa agro-system in arid and semi-arid regions like the Mediterranean basin. We tested various herbicides applied pre (PRE) and post (POST) crop emergence and cultivation methods for weed control, with an emphasis on crop safety. The PRE herbicides were the most effective and safe control mean. Their application resulted in minor (<5%) crop fresh weight reductions and weed cover area, compared with the control. The POST herbicides were also effective, yet their crop safety level was lower and non-consistent in some treatments. Generally, the finger weeder was less effective than the herbicide treatments and caused higher fresh weight reduction. However, this means was more effective when applied at earlier stages. Management and environmental conditions had a high impact on the moringa growth; hence, these aspects should be considered. Our results show the potential use of different herbicides and non-chemical tools and set the basis for a future IWM protocol for moringa. The wide range of options offered here can ensure economic and environmentally viable solutions for this new crop.

Drought intensity as experienced by plants depends upon soil moisture status and atmospheric variables such as temperature, radiation, and air vapour pressure deficit (VPD). Although the role of shoot architecture with these edaphic and atmospheric factors is well-characterized, the extent to which shoot and root dynamic interactions as a continuum are controlled by genotypic variation is less known. Here, we targeted these interactions using a wild emmer introgression line (IL20) with a distinct drought-induced shift in the shoot-to-root ratio and its drought-sensitive recurrent parent Svevo. Using a gravimetric platform, we show that IL20 maintained higher root water influx and gas exchange under drought stress, which supported a greater growth. Interestingly, the advantage of IL20 in root water influx and transpiration was expressed earlier during the daily diurnal cycle under lower VPD and therefore supported higher transpiration efficiency. Application of structural equation model indicates that under drought, VPD and radiation are antagonistic to transpiration rate, whereas the root water influx operates as feedback for the higher atmospheric responsiveness of leaves. Collectively, our results suggest that a drought-induced shift in root-to-shoot ratio can improve plant water uptake potential in a short preferable time window determined by both water and atmospheric parameters.

Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Sesamum indicum L.) is one of the oldest oil-crops in the world. Despite its economic and agricultural importance, it is an ‘orphan crop-plant’ that has undergone limited modern selection, and, as a consequence preserved wide genetic diversity. Here we established a new sesame panel (SCHUJI) that contains 184 genotypes representing wide phenotypic variation and is geographically distributed. We harnessed the natural variation of this panel to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions.

Phenotypic plasticity is one of the main mechanisms of adaptation to abiotic stresses via changes in critical developmental stages. Altering flowering phenology is a key evolutionary strategy of plant adaptation to abiotic stresses, to achieve the maximum possible reproduction. The current study is the first to apply the linear regression residuals as drought plasticity scores while considering the variation in flowering phenology and traits under non-stress conditions. We characterized the genomic architecture of 17 complex traits and their drought plasticity scores for quantitative trait loci (QTL) mapping, using a mapping population derived from a cross between durum wheat (Triticum turgidum ssp. durum) and wild emmer wheat (T. turgidum ssp. dicoccoides). We identified 79 QTLs affected observed traits and their plasticity scores, of which 33 reflected plasticity in response to water stress and exhibited epistatic interactions and/or pleiotropy between the observed and plasticity traits. Vrn-B3 (TaTF1) residing within an interval of a major drought-escape QTL was proposed as a candidate gene. The favorable alleles for most of the plasticity QTLs were contributed by wild emmer wheat, demonstrating its high potential for wheat improvement. Our study presents a new approach for the quantification of plant adaptation to various stresses and provides new insights into the genetic basis of wheat complex traits under water-deficit stress.

Chemical weed-control is the most effective practice for wheat, however, rapid evolution of herbicide-resistant weeds threat food-security and calls for integration of non-chemical practices. We hypothesis that integration of alternative GA-responsive dwarfing genes into elite wheat cultivars can promote early vigor and weed-competitiveness under Mediterranean climate. We develop near-isogenic lines of bread wheat cultivars with GAR dwarfing genes and evaluate them for early vigor and weed-competitiveness under various environmental and management conditions to identify promising NIL for weed-competitiveness and grain yield. While all seven NILs responded to external gibberellic acid application, they exhibited differences in early vigor. Greenhouse and field evaluations highlighted NIL OC1 (Rht8andRht12) as a promising line, with significant advantage in canopy early vigor over its parental. To facilitate accurate and continuous early vigor data collection, we applied non-destructive image-based phenotyping approaches which offers non-expensive and end-user friendly solution for selection. NIL OC1 was tested under different weed density level, infestation waves, and temperatures and highlight the complex genotypic × environmental × management interactions. Our findings demonstrate the potential of genetic modification of dwarfing genes as promising approach to improve weed-competitiveness, and serve as basis for future breeding efforts to support sustainable wheat production under semi-arid Mediterranean climate.