Advances in high throughput and affordable phenotyping for adapting maize and wheat to climate change

[eng] Supplying sufficient food to an increasing population is one of the most important challenges over the next century. To meet this demand, crop productivity will need to increase while it is being threatened by climate change effects like the increase of temperatures and the intensity of drought periods. Improving crop performance is key for an efficient adaptation to these challenging growing conditions, with crop breeding being one of the pillars. In that sense selecting more productive varieties for specific environments requires a better understanding of plant acclimation to stress conditions, including efficient phenotyping approaches. Plant phenotyping research pursues the development of new methods with high-throughput capacity and affordable to characterize non-destructively plant traits of interest. The main focus of this thesis was to develop and study versatile and precise methodologies with high-throughput capacity in order to improve crop performance assessments, while saving time and costs in the phenotyping tasksof two of the most important cereal crops: maize and wheat. The use of unmanned aerial vehicles (UAV) equipped with imaging sensors (including RGB, multispectral and thermal) permits covering simultaneously hectares of experimental fields fast, precisely, and in a non-destructive way. However, ground evaluations may still be an alternative in terms of cost and spatial resolution. The performance of these methodologies to assess genotypic differences in grain yield was evaluated in maize and wheat under different agronomical and environmental growing conditions such as nutrient deficiency, conservation agriculture, drought and heat stress. On one side, maize studies were performed in trials in Zimbabwe focused on the evaluation of genotypes under either low and normal phosphorus conditions or the application of conservation agriculture together with different top-dressing nitrogen fertilization regimes, to overcome the nutrient poverty of soils. In these studies, vegetation indices, related to parameters informing on the above-ground biomass and assessed during early stages of development, performed well as grain yield indicators. Moreover, during more advanced phenological stages, indices informing on the leaf and the canopy color were the traits that reported a better association with grain yield and N content in leaves. For the case of wheat, evaluations were performed in different latitudes in Spain covering a range of environments and grown under different management conditions, and sampling was performed during the reproductive stage (heading, anthesis and grain filling). In general terms, biomass indicators, such as canopy green biomass inferred from vegetation indices, together with water status indicators, such as canopy temperature, were the most critical traits predicting GY. The delay of senescence in water-limited environments and the photosynthetic efficiency measured by multispectral indices like the photochemical reflectance index (PRI) during anthesis were also relevant traits for GY under the rainfed and late-planting trials, respectively.[cat] La producció de suficient aliment per a una població cada cop més gran és un dels reptes més importants per al pròxim segle. Per assolir la demanda, la productivitat dels cultius han d’augmentar alhora que fan front als efectes del canvi climàtic com increment de les temperatures i la intensitat dels períodes de sequera. La millora de la capacitat dels cultius és un element clau per a l’adaptació a aquestes condicions més exigents i la selecció de varietats més productives sota ambients específics requereix una millor comprensió de l’aclimatació dels cultius als estressos. La recerca en fenotipatge de cultius té com objectiu el desenvolupament de noves metodologies d’alt rendiment capaces de caracteritzar característiques d’interès de les plantes d’una manera no destructiva. Sota condicions de camp, l’aplicació de metodologies tradicionals en experiments grans laboriós i requereix molt de temps. El principal objectiu d’aquesta tesi ha estat el desenvolupament i estudi diferents metodologies de caràcter versàtil, precises i d’alta capacitat per a millorar les mesures de com es desenvolupen els cultius, alhora de que es redueixen els costos i el temps requerit per a fer els mostrejos. El treball es basa en dos dels principals cereals: el blat i el blat de moro. L’ús de vehicles aeris no tripulats (UAV, del anglès Unmanned Aerial Vehicles) equipats amb càmeres i sensors (RGB, multiespectrals i termals) permet mesurar simultàniament hectàrees de camps experimentals d’una manera ràpida, precisa i sense la destrucció de mostra. Tot i així, les mesures a nivell de terra també són una alternativa prou potent pel que fa el cost i la resolució espacial. La capacitat d’aquestes metodologies per a mesurar diferencies genotípiques en el rendiment del blat de moro i el blat ha estat analitzada sota diferents condicions de creixement com la deficiència de nutrients, pràctiques de agricultura de conservació, sequera i altes temperatures. Per una banda, els estudis de blat de moro es van desenvolupar a Zimbabwe i estaven focalitzats en l’avaluació de genotips sota condicions diferents de fòsfor o en l’aplicació de l’agricultura de conservació per combatre la pobresa mineral dels sòls. En aquests estudis, les mesures relacionades amb paràmetres de biomassa aèria durant estadis primerencs de desenvolupament va funcionar bé com a indicadors de rendiment. A més, durant estadis fenològics més avançats, mesures de color de la capçada del cultiu van estar associats tant amb el rendiment com amb el contingut de nitrogen en les fulles. En el cas del blat, les avaluacions es van dur a terme a diferents latituds d’Espanya, cobrint un ampli rang de condicions climàtiques i agronòmiques. Els mostrejos es van realitzar en diferents estadis fenològics. En termes generals, els indicadors de biomassa i d’estat hídric del cultiu han estat de les mesures més correlacionades amb el rendiment. L’endarreriment de la senescència del cultiu en els ambients on l’aigua era el factor més limitant i el potencial fotosintètic mesurat per index multiespectrals durant la floració del cultiu han estat rellevants sota condicions de sequera i sembra tardana, respectivament

Impact of rising temperatures on historical wheat yield, phenology, and grain size in Catalonia

Climate change poses significant challenges to agriculture, impacting crop yields and necessitating adaptive strategies in breeding programs. This study investigates the genetic yield progress of wheat varieties in Catalonia, Spain, from 2007 to 2021, and examines the relationship between genetic yield and climate-related factors, such as temperature. Understanding these dynamics is crucial for ensuring the resilience of wheat crops in the face of changing environmental conditions.This research was funded by the projects TED2021-131606B-C21 of the Spanish Ministry of Economy and Competitiveness and by the CROPDIVA (Climate Resilient Orphan CroPs for increased DIVersity in Agriculture) project through the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000847. The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.info:eu-repo/semantics/publishedVersio

Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe

n the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increasefood production while keeping pace with continued population growth. Conservation agriculture(CA) has been proposed to enhance soil health and productivity to respond to this situation.Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes andmanagement practices for CA conditions has been explored using remote sensing tools. They may playa fundamental role towards overcoming the traditional limitations of data collection and processing inlarge scale phenotyping studies. We present the result of a study in which Red-Green-Blue (RGB) andmultispectral indexes were evaluated for assessing maize performance under conventional ploughing(CP) and CA practices. Eight hybrids under different planting densities and tillage practices weretested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmannedaerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution thatdid not have any negative impact on the performance of the indexes. Most of the calculated indexes(Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affectedby tillage conditions increasing their values from CP to CA. Indexes derived from the RGB-imagesrelated to canopy greenness performed better at assessing yield differences, potentially due to thegreater resolution of the RGB compared with the multispectral data, although this performance wasmore precise for CP than CA.The correlations of the multispectral indexes with yield were improvedby applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels withvegetation. The results of this study highlight the applicability of remote sensing approaches basedon RGB images to the assessment of crop performance and hybrid choice

Field Plant Monitoring from Macro to Micro Scale: Feasibility and Validation of Combined Field Monitoring Approaches from Remote to in Vivo to Cope with Drought Stress in Tomato

Monitoring plant growth and development during cultivation to optimize resource use efficiency is crucial to achieve an increased sustainability of agriculture systems and ensure food security. In this study, we compared field monitoring approaches from the macro to micro scale with the aim of developing novel in vivo tools for field phenotyping and advancing the efficiency of drought stress detection at the field level. To this end, we tested different methodologies in the monitoring of tomato growth under different water regimes: (i) micro-scale (inserted in the plant stem) real-time monitoring with an organic electrochemical transistor (OECT)-based sensor, namely a bioristor, that enables continuous monitoring of the plant; (ii) medium-scale (<1 m from the canopy) monitoring through red–green–blue (RGB) low-cost imaging; (iii) macro-scale multispectral and thermal monitoring using an unmanned aerial vehicle (UAV). High correlations between aerial and proximal remote sensing were found with chlorophyll-related indices, although at specific time points (NDVI and NDRE with GGA and SPAD). The ion concentration and allocation monitored by the index R of the bioristor during the drought defense response were highly correlated with the water use indices (Crop Water Stress Index (CSWI), relative water content (RWC), vapor pressure deficit (VPD)). A high negative correlation was observed with the CWSI and, in turn, with the RWC. Although proximal remote sensing measurements correlated well with water stress indices, vegetation indices provide information about the crop’s status at a specific moment. Meanwhile, the bioristor continuously monitors the ion movements and the correlated water use during plant growth and development, making this tool a promising device for field monitoring.The research activities were supported by projects POSITIVE (Regione EmiliaRomagna ERDF project 2014–2020), and by the Project PON «R&I» 2014–2020—Azione II—“E-crops—Technologies for Digital and Sustainable Agriculture” funded by the Italian Ministry of University and Research (MUR) under the PON Agrifood Program (Contract ARS01_01136).info:eu-repo/semantics/publishedVersio

Dataset of above and below ground traits assessed in Durum wheat cultivars grown under Mediterranean environments differing in water and temperature conditions

Ideotypic characteristics of durum wheat associated with higher yield under different water and temperature regimes were studied under Mediterranean conditions. The focus of this paper is to provide raw and supplemental data from the research article entitled "Durum wheat ideotypes in Mediterranean environments differing in water and temperature conditions" [1], which aims to define specific durum wheat ideotypes according to their responses to different agronomic conditions. In this context, six modern (i.e. post green revolution) genotypes with contrasting yield performance (i.e. high vs low yield) were grown during two consecutive years under different treatments: (i) winter planting under support-irrigation conditions, (ii) winter planting under rainfed conditions, (iii) late planting under support-irrigation. Trials were conducted at the INIA station of Colmenar de Oreja (Madrid). Different traits were assessed to inform about water status (canopy temperature at anthesis and stable carbon isotope composition (delta C-13) of the flag leaf and mature grains), root performance (root traits and the oxygen isotope composition (delta O-18) in the stem base water), phenology (days from sowing to heading), nitrogen status/photosynthetic capacity (nitrogen content and stable isotope composition (delta N-15) of the flag leaf and mature grain together with the pigment contents and the nitrogen balance index (NBI) of the flag leaf), crop growth (plant height (PH) and the normalized difference vegetation index (NDVI) at anthesis), grain yield and agronomic yield components. For most of the parameters assessed, data analysis demonstrated significant differences among genotypes within each treatment. The level of significance was determined using the Tukey-b test on independent samples, and ideotypes were modelled from the results of principle component analysis. The present data shed light on traits that help to define specific ideotype characteristics that confer genotypic adaptation to a wide range of agronomic conditions produced by variations in planting date, water conditions and seasonThis study was supported by the Spanish projects PID2019-106650RB-C21 and PCIN-2017-063, from the Ministerio de Ciencia e Innovacion, Spain. FZR is a recipient of a research grant (FI-AGAUR) sponsored by the Agency for Management of University and Research Grants (AGAUR) in collaboration with the University of Barcelona (UB) . We thank the personnel from the exper-imental station of INIA at Colmenar de Oreja (Aranjuez) for their continued support of our re-search. We thank the members of the Integrative Crop Ecophysiology Group for their assistance during the data assessment of the study. We extend our thanks to The Water Research Institute (IdRA) for their financial support to cover laboratory analyses. JLA acknowledges support from ICREA Academia, Generalitat de Catalunya, Spain. We thank Dr. J.Voltas from the University of Lleida, Spain, for his support with the delta 18O water analyses

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red-green-blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn't any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield

Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: effects of growing temperature and vernalization

This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ13C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ13C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ13C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).This study was funded by the projects AGL2015-65351-R, PID2019-109089RB-C31 and TED2021-131606B-C21 of the Spanish Ministry of Economy and Competitiveness. AG-R was funded by a Margarita Salas post-doctoral contract from the Spanish Ministry of Universities affiliated to the Research Vice-Rector of the University of Barcelona. VRRY was funded by a pre-doctoral contract from the Spanish Ministry of Economy and Competitiveness (PRE2020-092369). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.The authors acknowledge the contribution of the CERCA Program (Generalitat de Catalunya). The authors acknowledge Andrea Lopez, Ezequiel Arqué, Jordi Companys, and Josep Millera for their technical contributions to the experimental setup of field trials.info:eu-repo/semantics/publishedVersio