Determining the footprint of breeding in the seed microbiome of a perennial cereal.

BACKGROUND: Seed endophytes have a significant impact on plant health and fitness. They can be inherited and passed on to the next plant generation. However, the impact of breeding on their composition in seeds is less understood. Here, we studied the indigenous seed microbiome of a recently domesticated perennial grain crop (Intermediate wheatgrass, Thinopyrum intermedium L.) that promises great potential for harnessing microorganisms to enhance crop performance by a multiphasic approach, including amplicon and strain libraries, as well as molecular and physiological assays. RESULTS: Intermediate wheatgrass seeds harvested from four field sites in Europe over three consecutive years were dominated by Proteobacteria (88%), followed by Firmicutes (10%). Pantoea was the most abundant genus and Pantoea agglomerans was identified as the only core taxon present in all samples. While bacterial diversity and species richness were similar across all accessions, the relative abundance varied especially in terms of low abundant and rare taxa. Seeds from four different breeding cycles (TLI C3, C5, C704, C801) showed significant differences in bacterial community composition and abundance. We found a decrease in the relative abundance of the functional genes nirK and nifH as well as a drop in bacterial diversity and richness. This was associated with a loss of amplicon sequence variants (ASVs) in Actinobacteria, Alphaproteobacteria, and Bacilli, which could be partially compensated in offspring seeds, which have been cultivated at a new site. Interestingly, only a subset assigned to potentially beneficial bacteria, e.g. Pantoea, Kosakonia, and Pseudomonas, was transmitted to the next plant generation or shared with offspring seeds. CONCLUSION: Overall, this study advances our understanding of the assembly and transmission of endophytic seed microorganisms in perennial intermediate wheatgrass and highlights the importance of considering the plant microbiome in future breeding programs.

Low density neutrophils and neutrophil extracellular traps (NETs) are new inflammatory players in heart failure.

BACKGROUND: Heart failure with reduced (HFrEF) or preserved ejection fraction (HFpEF) is characterized by low-grade chronic inflammation. Circulating neutrophils regroup two subtypes termed high- and low-density neutrophils (HDNs and LDNs). LDNs represent less than 2% of total neutrophil under physiological conditions, but their count increase in multiple pathologies, releasing more inflammatory cytokines and neutrophil extracellular traps (NETs). The aims of this study were to assess the differential count and role of HDNs, LDNs and NETs-related activities in HF patients. METHODS: HDNs and LDNs were isolated from human blood by density gradient and purified by FACS and their counts obtained by flow cytometry. NETs formation (NETosis) was quantified by confocal microscopy. Circulating inflammatory and NETosis biomarkers were measured by ELISA. Neutrophil adhesion onto human extracellular matrix (hECM) was assessed by optical microscopy. RESULTS: A total of 140 individuals were enrolled, including 33 healthy volunteers (HV), 41 HFrEF (19 stable patients and 22 presenting acute decompensated HF; ADHF) and 66 HFpEF patients (36 stable patients and 30 presenting HF decompensation). HDNs and LDNs counts were significantly increased up to 39% and 2740% respectively in HF patients compared to HV. In HF patients, the correlations between LDNs counts and circulating inflammatory (CRP, IL-6 and -8), Troponin T, NT-proBNP and NETosis components were all significant. In vitro, LDNs expressed more H3Cit and NETs and were more pro-adhesive, with ADHFpEF patients presenting the highest pro-inflammatory profile. CONCLUSIONS: HFpEF patients present higher levels of circulating LDNs and NETs related activities, which are the highest in the context of acute HF decompensation.

Low-Density Neutrophils Contribute to Subclinical Inflammation in Patients with Type 2 Diabetes.

Type 2 diabetes (T2D) is characterized by low-grade inflammation. Low-density neutrophils (LDNs) represent normally less than 2% of total neutrophils but increase in multiple pathologies, releasing inflammatory cytokines and neutrophil extracellular traps (NETs). We assessed the count and role of high-density neutrophils (HDNs), LDNs, and NET-related activities in patients with T2D. HDNs and LDNs were purified by fluorescence-activated cell sorting (FACS) and counted by flow cytometry. Circulating inflammatory and NETs biomarkers were measured by ELISA (Enzyme Linked Immunosorbent Assay). NET formation was quantified by confocal microscopy. Neutrophil adhesion onto a human extracellular matrix (hECM) was assessed by optical microscopy. We recruited 22 healthy volunteers (HVs) and 18 patients with T2D. LDN counts in patients with diabetes were significantly higher (160%), along with circulating NETs biomarkers (citrullinated H3 histone (H3Cit), myeloperoxidase (MPO), and MPO-DNA (137%, 175%, and 69%, respectively) versus HV. Circulating interleukins (IL-6 and IL-8) and C-Reactive Protein (CRP) were significantly increased by 117%, 171%, and 79%, respectively, in patients compared to HVs. Isolated LDNs from patients expressed more H3Cit, MPO, and NETs, formed more NETs, and adhered more on hECM compared to LDNs from HVs. Patients with T2D present higher levels of circulating LDN- and NET-related biomarkers and associated pro-inflammatory activities.

Integrated crop and livestock systems increase both climate change adaptation and mitigation capacities.

Integrated crop-livestock systems (ICLS) are proposed as key solutions to the various challenges posed to present-day agriculture which must guarantee high and stable yields while minimizing its impacts on the environment. Yet the complex relationships between crops, grasslands and animals on which they rely demand careful and precise management. In this study, from a 18-year ICLS field experiment in Brazil, that consists in annual no-till soybean-pastures grazed by beef cattle, we investigated the impacts of contrasted pastures grazing intensities (defined by sward heights of 10, 20, 30 and 40 cm, plus an ungrazed treatment) on the agroecosystem productivity and soil organic carbon (SOC) under both historical and future (2040-2070, RCP8.5) climatic conditions. We used an innovative methodology to model the ICLS with the STICS soil-crop model, which was validated with field observations. Results showed that the total system production increased along with grazing intensity because of higher stocking rates and subsequent live weight gains. Moderate and light grazing intensities (30 and 40 cm sward heights) resulted in the largest increase in SOC over the 18-year period, with all ICLS treatments leading to greater SOC contents than the ungrazed treatment. When facing climate change under future conditions, all treatments increased in productivity due to the CO2 fertilization effect and the increases in organic amendments that result from the larger stocking rate allowed by the increased pasture carrying capacity. Moderate grazing resulted in the most significant enhancements in productivity and SOC levels. These improvements were accompanied by increased resistance to both moderate and extreme climatic events, benefiting herbage production and live weight gain. Globally, our results show that adding a trophic level (i.e. herbivores) into cropping systems, provided that their carrying capacities are respected, proved to increase their ability to withstand climate change and to contribute to its mitigation.

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.

Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.

To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?

The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.

PCA-based detection of phosphorous deficiency in wheat plants using prompt fluorescence and 820 nm modulated reflection signals.

Phosphorus deficiency induces biochemical and morphological changes which affect crop yield and production. Prompt fluorescence signal characterizes the PSII activity and electron transport from PSII to PSI, while the modulated light reflection at 820 (MR 820) nm investigates the redox state of photosystem I (PSI) and plastocyanin (PC). Therefore, combining information from modulated reflection at 820 nm with chlorophyll a fluorescence can potentially provide a more complete understanding of the photosynthetic process and integrating other plant physiological measurements may help to increase the accuracy of detecting the phosphorus deficiency in wheat leaves. In our study, we combined the chlorophyll a fluorescence and MR 820 signals to study the response of wheat plants to phosphorus deficiency as indirect tools for phosphorus plant status characterization. In addition, we studied the changes in chlorophyll content index, stomatal conductance (gs), root morphology, and biomass of wheat plants. The results showed an alteration in the electron transport chain as a specific response to P deficiency in the I-P phase during the reduction of the acceptor side of PSI. Furthermore, P deficiency increased parameters related to the energy fluxes per reaction centers, namely ETo/RC, REo/RC, ABS/RC, and DIo/RC. P deficiency increased the values of MRmin and MRmax and decreased νred, which implies that the reduction of PSI and PC became slower as the phosphorus decreased. The principal component analysis of the modulated reflection and chlorophyll a fluorescence parameters, with the integration of the growth parameters as supplementary variables, accounted for over 71% of the total variance in our phosphorus data using two components and provided a reliable information on PSII and PSI photochemistry under P deficiency.

Correlation between Neutrophil Extracellular Traps (NETs) Expression and Primary Graft Dysfunction Following Human Lung Transplantation.

Primary graft dysfunction (PGD) is characterized by alveolar epithelial and vascular endothelial damage and inflammation, lung edema and hypoxemia. Up to one-third of recipients develop the most severe form of PGD (Grade 3; PGD3). Animal studies suggest that neutrophils contribute to the inflammatory process through neutrophil extracellular traps (NETs) release (NETosis). NETs are composed of DNA filaments decorated with granular proteins contributing to vascular occlusion associated with PGD. The main objective was to correlate NETosis in PGD3 (n = 9) versus non-PGD3 (n = 27) recipients in an exploratory study. Clinical data and blood samples were collected from donors and recipients pre-, intra- and postoperatively (up to 72 h). Inflammatory inducers of NETs' release (IL-8, IL-6 and C-reactive protein [CRP]) and components (myeloperoxidase [MPO], MPO-DNA complexes and cell-free DNA [cfDNA]) were quantified by ELISA. When available, histology, immunohistochemistry and immunofluorescence techniques were performed on lung biopsies from donor grafts collected during the surgery to evaluate the presence of activated neutrophils and NETs. Lung biopsies from donor grafts collected during transplantation presented various degrees of vascular occlusion including neutrophils undergoing NETosis. Additionally, in recipients intra- and postoperatively, circulating inflammatory (IL-6, IL-8) and NETosis biomarkers (MPO-DNA, MPO, cfDNA) were up to 4-fold higher in PGD3 recipients compared to non-PGD3 (p = 0.041 to 0.001). In summary, perioperative elevation of NETosis biomarkers is associated with PGD3 following human lung transplantation and these biomarkers might serve to identify recipients at risk of PGD3 and initiate preventive therapies.

Neutrophils and Circulating Inflammatory Biomarkers in Diabetes Mellitus and Heart Failure With Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) is characterized by low-grade chronic inflammation, which could be exacerbated by type 2 diabetes mellitus (DM). We hypothesized that neutrophils in patients with DM and patients with HFpEF with/without DM contribute to low-grade inflammation through the release of pro-inflammatory cytokines. Venous blood was withdrawn from patients with DM (n = 22), HFpEF (n = 15), HFpEF with DM (n = 13), and healthy controls (CTL) (n = 21). Levels of circulating cytokines and in vitro cytokines released by isolated neutrophils were assessed by enzyme-linked immunosorbent assay. Compared with CTL, there was a significant decrease in circulating nitric oxide in patients with DM (p ≤0.001), HFpEF (p ≤0.05), and HFpEF with DM (p ≤0.001) up to 44%. Circulating soluble intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 levels increased (up to 2.5-fold and 1.9-fold, respectively; p ≤0.001) in patients with HFpEF and patients with HFpEF and DM, whereas soluble E-selectin only increased in patients with HFpEF and DM (1.4-fold, p ≤0.001). Circulating vascular endothelial growth factor levels were similar in CTL and patients with DM but were decreased in patients with HFpEF with/without DM (up to 94%; p ≤0.001). Circulating C-reactive protein, interleukin (IL)-8, IL-6, and IL-receptor antagonist increased in all patient groups with a maximum of 3.3-fold, 4.7-fold, 4.8-fold, and 1.6-fold, respectively, in patients with HFpEF and patients with DM. In vitro, lipopolysaccharide increased neutrophils IL-6 release from HFpEF with DM (3.7-fold; p ≤0.001), and IL-8 release from DM and HFpEF with DM versus CTL (2.8-fold and 10.1-fold, respectively; p ≤0.001). IL-1 receptor antagonist and vascular endothelial growth factor release from HFpEF neutrophils significantly decreased up to 87.0% and 92.2%, respectively, versus CTL. Neutrophils from patients with DM and HFpEF release more cytokines than CTL. This increase in pro-inflammatory status may explain the greater event rate in patients with HFpEF and DM.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

In-Field Wheat Reflectance: How to Reach the Organ Scale?

The reflectance of wheat crops provides information on their architecture or physiology. However, the methods currently used for close-range reflectance computation do not allow for the separation of the wheat canopy organs: the leaves and the ears. This study details a method to achieve high-throughput measurements of wheat reflectance at the organ scale. A nadir multispectral camera array and an incident light spectrometer were used to compute bi-directional reflectance factor (BRF) maps. Image thresholding and deep learning ear detection allowed for the segmentation of the ears and the leaves in the maps. The results showed that the BRF measured on reference targets was constant throughout the day but varied with the acquisition date. The wheat organ BRF was constant throughout the day in very cloudy conditions and with high sun altitudes but showed gradual variations in the morning under sunny or partially cloudy sky. As a consequence, measurements should be performed close to solar noon and the reference panel should be captured at the beginning and end of each field trip to correct the BRF. The method, with such precautions, was tested all throughout the wheat growing season on two varieties and various canopy architectures generated by a fertilization gradient. The method yielded consistent reflectance dynamics in all scenarios.

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification.

The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges.

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Neutrophils pro-inflammatory and anti-inflammatory cytokine release in patients with heart failure and reduced ejection fraction.

AIMS: Heart failure with reduced ejection fraction (HFrEF) is characterized by sub-clinical inflammation. Changes in selected biomarkers of inflammation concomitant with the release of pro-inflammatory and anti-inflammatory cytokines by neutrophils have not been investigated in patients with HFrEF. METHODS AND RESULTS: Fifty-two patients, aged 68.8 ± 1.7 years, with HFrEF and left ventricular ejection fraction 28.7 ± 1.0%, and 21 healthy controls (CTL) were recruited. Twenty-five HF patients had type 2 diabetes. Venous blood samples from HF and CTL were collected once. Neutrophil-derived pro-inflammatory and anti-inflammatory cytokine levels were assessed in plasma by ELISA. Plasma biomarkers assessed included: C-reactive protein (CRP), vascular endothelial growth factor (VEGF), interleukins (IL)-6, -8, -1 receptor antagonist (-1RA), nitric oxide (NO), soluble intercellular adhesion molecule-1 (sICAM-1), vascular cell adhesion molecule 1 (sVCAM-1) and E-Selectin (sE-Sel). Neutrophils were isolated and stimulated with various agonists to promote VEGF, IL-6, IL-8, and IL-1RA release. Compared with CTL, HFrEF patients showed a marked decrease in circulating VEGF [178.0 (interquartile range; IQR 99.6; 239.2) vs. 16.2 (IQR 9.3; 20.2) pg/mL, P ≤ 0.001] and NO [45.2 (IQR 42.1; 57.6) vs. 40.6 (IQR 30.4; 47.1) pg/mL, P = 0.0234]. All other circulating biomarkers were significantly elevated. Neutrophils isolated from patients with HFrEF exhibited a greater IL-8 release in response to LPS [1.2 ± 0.1 (CTL); 10.4 ± 1.6 ng/mL (HFrEF) and 12.4 ± 1.6 ng/mL (HFrEF and DM), P ≤ 0.001]. IL-6 release in response to LPS was not changed in HFrEF patients without diabetes, whereas it was significantly increased in patients with HFrEF and diabetes [46.7 ± 3.9 (CTL) vs. 165.8 ± 48.0 pg/mL (HFrEF), P = 0.1713 and vs. 397.7 ± 67.4 pg/mL (HFrEF and DM), P ≤ 0.001]. In contrast, the release of VEGF and IL-1RA was significantly reduced in HFrEF (VEGF; TNF-α: 38.6 ± 3.1 and LPS: 25.3 ± 2.6 pg/mL; IL1RA; TNF-α: 0.6 ± 0.04 and LPS: 0.3 ± 0.02 ng/mL) compared with CTL (VEGF; TNF-α: 60.0 ± 9.4 and LPS: 41.2 ± 5.9 pg/mL; IL1RA; TNF-α: 3.3 ± 0.2 and LPS: 2.3 ± 0.1 ng/mL). CONCLUSIONS: Patients with HFrEF exhibit a significant decrease in circulating VEGF. The release of VEGF and both pro-inflammatory and anti-inflammatory cytokines from the stimulated neutrophils is markedly altered in these patients. The clinical significance of these findings deserves further investigation.

Angiopoietin 1 release from human neutrophils is independent from neutrophil extracellular traps (NETs).

BACKGROUND: Neutrophils induce the synthesis and release of angiopoietin 1 (Ang1), a cytosolic growth factor involved in angiogenesis and capable of inducing several pro-inflammatory activities in neutrophils. Neutrophils also synthesize and release neutrophil extracellular traps (NETs), comprised from decondensed nuclear DNA filaments carrying proteins such as neutrophil elastase (NE), myeloperoxidase (MPO), proteinase 3 (PR3) and calprotectin (S100A8/S100A9), which together, contribute to the innate immune response against pathogens (e.g., bacteria). NETs are involved in various pathological conditions through pro-inflammatory, pro-thrombotic and endothelial dysfunction effects and have recently been found in heart failure (HF) and type 2 diabetes (T2DM) patients. The aim of the present study was to investigate the role of NETs on the synthesis and release of Ang1 by the neutrophils in patients with T2DM and HF with preserved ejection fraction (HFpEF) (stable or acute decompensated; ADHFpEF) with or without T2DM. RESULTS: Our data show that at basal level (PBS) and upon treatment with LPS, levels of NETs are slightly increased in patients suffering from T2DM, HFpEF ± T2DM and ADHF without (w/o) T2DM, whereas this increase was significant in ADHFpEF + T2DM patients compared to healthy control (HC) volunteers and ADHFpEF w/o T2DM. We also observed that treatments with PMA or A23187 increase the synthesis of Ang1 (from 150 to 250%) in HC and this effect is amplified in T2DM and in all cohorts of HF patients. Ang1 is completely released (100%) by neutrophils of all groups and does not bind to NETs as opposed to calprotectin. CONCLUSIONS: Our study suggests that severely ill patients with HFpEF and diabetes synthesize and release a greater abundance of NETs while Ang1 exocytosis is independent of NETs synthesis.

A dataset for sustainability assessment of agroecological practices in a crop-livestock farming system.

This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in "SEGAE: a serious game to learn agroecology" [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

Imaging Wheat Canopy Through Stereo Vision: Overcoming the Challenges of the Laboratory to Field Transition for Morphological Features Extraction.

Stereo vision is a 3D imaging method that allows quick measurement of plant architecture. Historically, the method has mainly been developed in controlled conditions. This study identified several challenges to adapt the method to natural field conditions and propose solutions. The plant traits studied were leaf area, mean leaf angle, leaf angle distribution, and canopy height. The experiment took place in a winter wheat, Triticum aestivum L., field dedicated to fertilization trials at Gembloux (Belgium). Images were acquired thanks to two nadir cameras. A machine learning algorithm using RGB and HSV color spaces is proposed to perform soil-plant segmentation robust to light conditions. The matching between images of the two cameras and the leaf area computation was improved if the number of pixels in the image of a scene was binned from 2560 × 2048 to 1280 × 1024 pixels, for a distance of 1 m between the cameras and the canopy. Height descriptors such as median or 95th percentile of plant heights were useful to precisely compare the development of different canopies. Mean spike top height was measured with an accuracy of 97.1 %. The measurement of leaf area was affected by overlaps between leaves so that a calibration curve was necessary. The leaf area estimation presented a root mean square error (RMSE) of 0.37. The impact of wind on the variability of leaf area measurement was inferior to 3% except at the stem elongation stage. Mean leaf angles ranging from 53° to 62° were computed for the whole growing season. For each acquisition date during the vegetative stages, the variability of mean angle measurement was inferior to 1.5% which underpins that the method is precise.

Climate change impact and adaptation for wheat protein.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.