Impact of spatio-temporal sahde on crop growth and productivity, perspectives for temperate agroforestry

Currently, silvoarable agroforestry is receiving renewed interest in Europe, as a land use system that allows for combining the production of commodities with a range of non-commodity outputs, such as environmental protection. Despite the potential of this practice, it remains rarely implemented in Northwestern Europe. One of the obstacles in the adoption of silvoarable agroforestry systems is the lack of quantitative knowledge on the long term performance of different crops when they are competing for resources with trees. In the face of a wide range of possibilities, it remains difficult to obtain a clear overview of overall system functioning. In this thesis, we simplify this complexity by focusing our research questions on the resource of light, based on the assumption that in Belgian climatic conditions light is likely to be the predominant constraint for understorey crops in a silvoarable agroforestry system. With regard to this resource, we develop our research in order to gain insights into the growth mechanisms and final yield of shaded winter wheat and sugar beet crops. We address these questions using an artificial shade system, which has been developed to reproduce the effect of the heterogeneous spatio-temporal pattern of light observed under late-flushing trees in an agroforestry system, isolated from the competition effects for water and nutrients. The shade structures recreate two shade environments: continuous and periodic. The continuous shade treatment leads to shade throughout the entire day, while the periodic shade treatment induces an intermittent shade period, which varies during the day and according to structure orientation. Winter wheat responded to the late application of both shade treatments with a significant decrease in grain yield, which was partly compensated for by an increase in grain protein content. When shaded, sugar beet compensated through morphological adaptations of the aboveground part of the plant, and by a decrease in the final root dry matter and sugar yield. Overall, for both crops, the magnitude of the final yield repercussion varied with the level and period of shade application. Additionally, an arable plot bordered by a row of poplar trees was selected to evaluate the effect of real trees on the winter wheat. The reduction in the final grain yield follows a gradient, from underneath the trees to the centre of the field. Notwithstanding that interactions other than light competition may have occurred, the maximum yield reduction observed under the trees never reaches the level of decrease which is observed under the continuous shade treatment simulated by the artificial shade arrangement. This experimental approach with winter wheat was complemented by a modelling study, in which we evaluate the ability of the STICS crop model to simulate crops growing under dynamic shade. The results highlight the limits of the STICS model when it is used to simulate crop growth under contrasted shade conditions. Finally, we propose agroecology as a conceptual framework for developing sustainable and profitable agroforestry systems in Europe, and reflect on agricultural practices, food systems, and research methodologies

Trait-Based Root Phenotyping as a Necessary Tool for Crop Selection and Improvement

Most of the effort of crop breeding has focused on the expression of aboveground traits with the goals of increasing yield and disease resistance, decreasing height in grains, and improvement of nutritional qualities. The role of roots in supporting these goals has been largely ignored. With the increasing need to produce more food, feed, fiber, and fuel on less land and with fewer inputs, the next advance in plant breeding must include greater consideration of roots. Root traits are an untapped source of phenotypic variation that will prove essential for breeders working to increase yields and the provisioning of ecosystem services. Roots are dynamic, and their structure and the composition of metabolites introduced to the rhizosphere change as the plant develops and in response to environmental, biotic, and edaphic factors. The assessment of physical qualities of root system architecture will allow breeding for desired root placement in the soil profile, such as deeper roots in no-till production systems plagued with drought or shallow roots systems for accessing nutrients. Combining the assessment of physical characteristics with chemical traits, including enzymes and organic acid production, will provide a better understanding of biogeochemical mechanisms by which roots acquire resources. Lastly, information on the structural and elemental composition of the roots will help better predict root decomposition, their contribution to soil organic carbon pools, and the subsequent benefits provided to the following crop. Breeding can no longer continue with a narrow focus on aboveground traits, and breeding for belowground traits cannot only focus on root system architecture. Incorporation of root biogeochemical traits into breeding will permit the creation of germplasm with the required traits to meet production needs in a variety of soil types and projected climate scenarios

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

INSERTIONAL POLYMORPHISMS OF MINIATURE INVERTED-REPEAT TRANSPOSABLE ELEMENTS (STOWAWAY- MITEs) IN INTRONS OF SUGAR BEET.

: Sugar beet has high sucrose content and it accounts for most of the sugar production in the world after sugar cane. European Union is the world’s largest sugar beet producer, which accounts for 50% production in the world. A panel of 12 genotypes of Beta vulgaris cultivars of sugar beet and fodder beet was used to identify polymorphisms of Stowaway miniature inverted-repeat transposable elements (MITEs). In sugar beet DNA is highly polymorphic and highly variant due to its highly repetitive DNA sequences which account for 64% of the genome. Transposable Elements (TEs) are mainly classified into Class I retrotransposons and Class II DNA transposons. MITEs belong to class II, they are non-autonomous TEs. MITEs are the most abundant group of class II elements in the plant genome. Stowaway MITEs are derived from and mobilized by elements of the Tc1/ mariner superfamily and are one of the significant sources of variation in the sugar beet. MITE copies inserted within introns can be exploited as potential intron length polymorphism (ILP) markers. Polymerase Chain Reaction (PCR) can detect ILPs with primers anchored in exon sequences flanking the target introns. Here, we designed primers for 70 BvSto (Beta vulgaris Stowaway-like) MITE insertion sites within introns along the sugar beet genome and validated them as candidate ILP markers, to develop a set of markers for genotyping the sugar beet.: Sugar beet has high sucrose content and it accounts for most of the sugar production in the world after sugar cane. European Union is the world’s largest sugar beet producer, which accounts for 50% production in the world. A panel of 12 genotypes of Beta vulgaris cultivars of sugar beet and fodder beet was used to identify polymorphisms of Stowaway miniature inverted-repeat transposable elements (MITEs). In sugar beet DNA is highly polymorphic and highly variant due to its highly repetitive DNA sequences which account for 64% of the genome. Transposable Elements (TEs) are mainly classified into Class I retrotransposons and Class II DNA transposons. MITEs belong to class II, they are non-autonomous TEs. MITEs are the most abundant group of class II elements in the plant genome. Stowaway MITEs are derived from and mobilized by elements of the Tc1/ mariner superfamily and are one of the significant sources of variation in the sugar beet. MITE copies inserted within introns can be exploited as potential intron length polymorphism (ILP) markers. Polymerase Chain Reaction (PCR) can detect ILPs with primers anchored in exon sequences flanking the target introns. Here, we designed primers for 70 BvSto (Beta vulgaris Stowaway-like) MITE insertion sites within introns along the sugar beet genome and validated them as candidate ILP markers, to develop a set of markers for genotyping the sugar beet

Development of a spatial planning support system for agricultural policy formulation related to land and water resources in Borkhar & Meymeh district, Iran

In this study, a system was developed to support agricultural planners and policy makers in land resource analysis, policy formulation, identification of possible policy measures and policy impact analysis. The research is part of a larger programme, aiming at development of a model system to support agricultural policy formulation at national level. The current study focused on methodology development and its implementation in Borkhar & Meymeh district in Esfahan province, Iran. The system comprises three main components, i.e. resource analysis, policy impact assessment and policy evaluation. The biophysical resource analysis was carried out using CGMS, the Crop Growth Monitoring System which includes WOFOST, a generic crop growth simulation model. This model simulates growth of annual crops in the potential and water-limited production situations, based on daily weather data, crop characteristics and soil physical characteristics. For this purpose, crop characteristics of winter wheat and winter barley were calibrated based on research data from the agro-meteorological research center of Kaboutar Abad, Esfahan, Iran. Crop characteristics of silage maize, sugar beet, sunflower and potato were calibrated based on yields of the best agricultural producers in the region. For the weather stations in which solar radiation was not measured, it was estimated from sunshine-hours or temperature, using empirical relations. A sensitivity analysis on method of solar radiation estimation was carried out to test model performance in terms of simulated crop yield and water requirements for winter barley and sugar beet as representatives of winter and summer crops, respectively. Results of this analysis showed that the maximum difference in simulated crop yield based on estimated and measured solar radiation is less than 10%. CGMS was used for land resource analysis at the regional (district) scale. The potentially suitable area for agriculture in the district was identified and classified into 128 homogenous units (referred to in this study as Elementary Mapping Units, EMU) in terms of soil, weather and administrative unit. For each EMU, soil physical characteristics were derived from available soil maps and soil analyses reports. Daily weather characteristics (maximum and minimum temperature, vapor pressure, wind speed, rainfall, and solar radiation) were generated for the centre of each EMU by interpolation of daily weather data of 33 weather stations, located in and around the district. CGMS was then modified to allow calculation of irrigated crop yields. Yields of major crops and water requirements per decade were simulated using CGMS for three irrigation regimes (full irrigation, 20% and 40% deficit irrigation). Fertilizer requirements for the three macro-nutrients, nitrogen, phosphorus and potassium, for each level of crop production were estimated based on soil chemical characteristics, crop yields and nutrient content in economic crop products and crop residues. An alternative methodology was developed for spatial estimation of crop yields, water and fertilizer requirements of crops (alfalfa, melon, watermelon, and colza) that could not be simulated by CGMS, either because of model limitations or lack of data for model calibration. The ratio of current and potential crop yields, referred to as production efficiency, was used as an indicator of management ability of farmers and was used in farm classification. The policy formulation process consists of three steps: i) selection of policy objectives, ii) identification of policy instruments and iii) assessment and analysis of their impacts. In this study, policy objectives and relevant policy instruments were derived from the latest agricultural development documents. A model was developed to assess the impacts of policy instruments and another model for analysis of these impacts from different perspectives. As reactions of farmers to policy instruments may be different, depending on their socioeconomic situation and the biophysical characteristics of their land, a planning (modelling) unit was defined, homogenous in terms of biophysical and socioeconomic characteristics. For this purpose, farms belonging to each of the agricultural production systems (e.g., traditional, cooperative and agroindustrial) were classified into farm types, based on land and water availability, overall production efficiency and average net income per ha. These farm types were combined with land units to form the basic units of analysis, i.e. farm type-land units (FTLU), homogenous in terms of biophysical potential, as well as in resource endowments and management ability of farmers. A distributed linear programming model was developed to assess policy impacts by simulating the response of the various farm types to specific policy instruments. This model is optimizing a utility function, composed of a combination of net income and production cost, subject to various constraints at different spatial scales (e.g., farm type-land unit, farm type, village, and subdistrict). The model was validated based on the conditions of the year 2002-03 by comparing simulated crop yields and total crop production in Borkhar subdistrict with detailed agricultural census data. Indicators, representing the effect/impact of policy instruments on economic, social, and environmental objectives of various stakeholders were selected and quantified in a post-model analysis. In a model experiment, the reactions of the different farm types to three policy instruments, aiming at increasing agricultural water productivity in Borkhar sub-district were simulated. A multi-criteria evaluation technique was used for policy analysis through overall assessment of the various economic, social and environmental indicators to evaluate the effectiveness of various policy instruments. The developed system represents a further step in the development of computeraided decision support systems for land use analysis that have received ample attention in the research community, in response to the perceived needs of policy makers. The consultations with planners in the course of the study, leads to the conclusion, however, that still a long way has to be gone to bridge the gap between the policy makers that are asking questions that land use modelers can not answer and the land use modelers that are generating answers to questions that policy makers are not (willing to) ask(ing). <br/

Unlocking the benefits of spaceborne imaging spectroscopy for sustainable agriculture

With the Environmental Mapping and Analysis Program (EnMAP) mission, launched on April 1st 2022, new opportunities unfold for precision farming and agricultural monitoring. The recurring acquisition of spectrometric imagery from space, contiguously resolving the electromagnetic spectrum in the optical domain (400—2500 nm) within close narrow bands, provides unprecedented data about the interaction of radiation with biophysical and biochemical crop constituents. These interactions manifest in spectral reflectance, carrying important information about crop status and health. This information may be incorporated in agricultural management systems to support necessary efforts to maximize yields against the backdrop of an increased food demand by a growing world population. At the same time, it enables the effective optimization of fertilization and pest control to minimize environmental impacts of agriculture. Deriving biophysical and biochemical crop traits from hyperspectral reflectance thereby always relies on a model. These models are categorized into (1) parametric, (2) nonparametric, (3) physically-based, and (4) hybrid retrieval schemes. Parametric methods define an explicit parameterized expression, relating a number of spectral bands or derivates thereof with a crop trait of interest. Nonparametric methods comprise linear techniques, such as principal component analysis (PCA) which addresses collinearity issues between adjacent bands and enables compression of full spectral information into dimensionality reduced, maximal informative principal components (PCs). Nonparametric nonlinear methods, i.e., machine learning (ML) algorithms apply nonlinear transformations to imaging spectroscopy data and are therefore capable of capturing nonlinear relationships within the contained spectral features. Physically-based methods represent an umbrella term for radiative transfer models (RTMs) and related retrieval schemes, such as look-up-table (LUT) inversion. A simple, easily invertible and specific RTM is the Beer-Lambert law which may be used to directly infer plant water content. The most widely used general and invertible RTM is the one-dimensional canopy RTM PROSAIL, which is coupling the Leaf Optical Properties Spectra model PROSPECT and the canopy reflectance model 4SAIL: Scattering by Arbitrarily Inclined Leaves. Hybrid methods make use of synthetic data sets created by RTMs to calibrate parametric methods or to train nonparametric ML algorithms. Due to the ill-posed nature of RTM inversion, potentially unrealistic and redundant samples in a LUT need to be removed by either implementing physiological constraints or by applying active learning (AL) heuristics. This cumulative thesis presents three different hybrid approaches, demonstrated within three scientific research papers, to derive agricultural relevant crop traits from spectrometric imagery. In paper I the Beer-Lambert law is applied to directly infer the thickness of the optically active water layer (i.e., EWT) from the liquid water absorption feature at 970 nm. The model is calibrated with 50,000 PROSPECT spectra and validated over in situ data. Due to separate water content measurements of leaves, stalks, and fruits during the Munich-North-Isar (MNI) campaigns, findings indicate that depending on the crop type and its structure, different parts of the canopy are observed with optical sensors. For winter wheat, correlation between measured and modelled water content was most promising for ears and leaves, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26%, and in the case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These results led to the general recommendation to collect destructive area-based plant organ specific EWT measurements instead of the common practice to upscale leaf-based EWT measurements to canopy water content (CWC) by multiplication of the leaf area index (LAI). The developed and calibrated plant water retrieval (PWR) model proved to be transferable in space and time and is ready to be applied to upcoming EnMAP data and any other hyperspectral imagery. In paper II the parametric concept of spectral integral ratios (SIR) is introduced to retrieve leaf chlorophyll a and b content (Cab), leaf carotenoid content (Ccx) and leaf water content (Cw) simultaneously from imaging spectroscopy data in the wavelength range 460—1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The approach was validated over a physiologically constrained PROSAIL simulated database, considering natural Ccx-Cab relations and green peak locations. Validation on airborne spectrometric HyMAP data achieved satisfactory results for Cab (R2 = 0.84; RMSE = 9.06 µg cm-2) and CWC (R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Mapping of the SIR results as multiband images (3-segment SIR) allows for an intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is applicable on satellite imaging spectroscopy data. In paper III a hybrid workflow is presented, combining RTM with ML for inferring crop carbon content (Carea) and aboveground dry and fresh biomass (AGBdry, AGBfresh). The concept involves the establishment of a PROSAIL training database, dimensionality reduction using PCA, optimization in the sampling domain using AL against the 4-year MNI campaign dataset, and training of Gaussian process regression (GPR) ML algorithms. Internal validation of the GPR-Carea and GPR-AGB models achieved R2 of 0.80 for Carea, and R2 of 0.80 and 0.71 for AGBdry and AGBfresh, respectively. Validation with an independent dataset, comprising airborne AVIRIS NG imagery (spectrally resampled to EnMAP) and in situ measurements, successfully demonstrated mapping capabilities for both bare and green fields and generated reliable estimates over winter wheat fields at low associated model uncertainties (< 40%). Overall, the proposed carbon and biomass models demonstrate a promising path toward the inference of these crucial variables over cultivated areas from upcoming spaceborne hyperspectral acquisitions, such as from EnMAP. As conclusions, the following important findings arise regarding parametric and nonparametric hybrid methods as well as in view of the importance of in situ data collection. (1) Uncertainties within the RTM PROSAIL should always be considered. A possible reduction of these uncertainties is thereby opposed to the invertibility of the model and its intended simplicity. (2) Both physiological constraints and AL heuristics should be applied to reduce unrealistic parameter combinations in a PROSAIL calibration or training database. (3) State-of-the-art hybrid ML approaches with the ability to provide uncertainty intervals are anticipated as most promising approach for solving inference problems from hyperspectral Earth observation data due to their synergistic use of RTMs and the high flexibility, accuracy and consistency of nonlinear nonparametric methods. (4) Parametric hybrid approaches, due to their algorithmic transparency, enable deeper insights into fundamental physical limitations of optical remote sensing as compared to ML approaches. (5) Integration-based indices that make full use of available hyperspectral information may serve as physics-aware dimensionality reduced input for ML algorithms to either improve estimations or to serve as endmember for crop type discrimination when additional time series information is available. (6) The validation of quantitative model-based estimations is crucial to evaluate and improve their performance in terms of the underlying assumptions, model parameterizations, and input data. (7) In the face of soon-to-be-available EnMAP data, collection of in situ data for validation of retrieval methods should aim at high variability of measured crop types, high temporal variability over the whole growing season, as well as include area- and biomass-based destructive measurements instead of LAI-upscaled leaf measurements. Provided the perfect functionality of the payload instruments, the success of the EnMAP mission and the here presented methods depend critically on a low-noise, accurate atmospherically corrected reflectance product. High-level outputs of the retrieval methods presented in this thesis may be incorporated into agricultural decision support systems for fertilization and irrigation planning, yield estimation, or estimation of the soil carbon sequestration potential to enable a sustainable intensive agriculture in the future.Mit der am 1. April 2022 gestarteten Satellitenmission Environmental Mapping and Analysis Program (EnMAP) eröffnen sich neue Möglichkeiten für die Präzisionslandwirtschaft und das landwirtschaftliche Monitoring. Die wiederkehrende Erfassung spektrometrischer Bilder aus dem Weltraum, welche das elektromagnetische Spektrum im optischen Bereich (400—2500 nm) innerhalb von engen, schmalen Bändern zusammenhängend auflösen, liefert nie dagewesene Daten über die Interaktionen von Strahlung und biophysikalischen und biochemischen Pflanzenbestandteilen. Diese Wechselwirkungen manifestieren sich in der spektralen Reflektanz, die wichtige Informationen über den Zustand und die Gesundheit der Pflanzen enthält. Vor dem Hintergrund einer steigenden Nachfrage nach Nahrungsmitteln durch eine wachsende Weltbevölkerung können diese Informationen in landwirtschaftliche Managementsysteme einfließen, um eine notwendige Ertragsmaximierung zu unterstützen. Gleichzeitig können sie eine effiziente Optimierung der Düngung und Schädlingsbekämpfung ermöglichen, um die Umweltauswirkungen der Landwirtschaft zu minimieren. Die Ableitung biophysikalischer und biochemischer Pflanzeneigenschaften aus hyperspektralen Reflektanzdaten ist dabei immer von einem Modell abhängig. Diese Modelle werden in (1) parametrische, (2) nichtparametrische, (3) physikalisch basierte und (4) hybride Ableitungsmethoden kategorisiert. Parametrische Methoden definieren einen expliziten parametrisierten Ausdruck, der eine Reihe von Spektralkanälen oder deren Ableitungen mit einem Pflanzenmerkmal von Interesse in Beziehung setzt. Nichtparametrische Methoden umfassen lineare Techniken wie die Hauptkomponentenanalyse (PCA). Diese adressieren Kollinearitätsprobleme zwischen benachbarten Kanälen und komprimieren die gesamte Spektralinformation in dimensionsreduzierte, maximal informative Hauptkomponenten (PCs). Nichtparametrische nichtlineare Methoden, d. h. Algorithmen des maschinellen Lernens (ML), wenden nichtlineare Transformationen auf bildgebende Spektroskopiedaten an und sind daher in der Lage, nichtlineare Beziehungen innerhalb der enthaltenen spektralen Merkmale zu erfassen. Physikalisch basierte Methoden sind ein Oberbegriff für Strahlungstransfermodelle (RTM) und damit verbundene Ableitungsschemata, d. h. Invertierungsverfahren wie z. B. die Invertierung mittels Look-up-Table (LUT). Ein einfaches, leicht invertierbares und spezifisches RTM stellt das Lambert-Beer'sche Gesetz dar, das zur direkten Ableitung des Wassergehalts von Pflanzen verwendet werden kann. Das am weitesten verbreitete, allgemeine und invertierbare RTM ist das eindimensionale Bestandsmodell PROSAIL, eine Kopplung des Blattmodells Leaf Optical Properties Spectra (PROSPECT) mit dem Bestandsreflexionsmodell 4SAIL (Scattering by Arbitrarily Inclined Leaves). Bei hybriden Methoden werden von RTMs generierte, synthetische Datenbanken entweder zur Kalibrierung parametrischer Methoden oder zum Training nichtparametrischer ML-Algorithmen verwendet. Aufgrund der Äquifinalitätsproblematik bei der RTM-Invertierung, müssen potenziell unrealistische und redundante Simulationen in einer solchen Datenbank durch die Implementierung natürlicher physiologischer Beschränkungen oder durch die Anwendung von Active Learning (AL) Heuristiken entfernt werden. In dieser kumulativen Dissertation werden drei verschiedene hybride Ansätze zur Ableitung landwirtschaftlich relevanter Pflanzenmerkmale aus spektrometrischen Bilddaten vorgestellt, die anhand von drei wissenschaftlichen Publikationen demonstriert werden. In Paper I wird das Lambert-Beer'sche Gesetz angewandt, um die Dicke der optisch aktiven Wasserschicht (bzw. EWT) direkt aus dem Absorptionsmerkmal von flüssigem Wasser bei 970 nm abzuleiten. Das Modell wird mit 50.000 PROSPECT-Spektren kalibriert und anhand von In-situ-Daten validiert. Aufgrund separater Messungen des Wassergehalts von Blättern, Stängeln und Früchten während der München-Nord-Isar (MNI)-Kampagnen, zeigen die Ergebnisse, dass je nach Kulturart und -struktur, unterschiedliche Teile des Bestandes mit optischen Sensoren beobachtet werden können. Bei Winterweizen wurde die höchste Korrelation zwischen gemessenem und modelliertem Wassergehalt für Ähren und Blätter erzielt und sie erreichte Bestimmtheitsmaße (R2) von bis zu 0,72 bei einem relativen RMSE (rRMSE) von 26%, bei Mais entsprechend nur für die Blattfraktion (R2 = 0,86, rRMSE = 23%). Diese Ergebnisse führten zu der allgemeinen Empfehlung, Kompartiment-spezifische EWT-Bestandsmessungen zu erheben, anstatt der üblichen Praxis, blattbasierte EWT-Messungen durch Multiplikation mit dem Blattflächenindex (LAI) auf den Bestandswassergehalt (CWC) hochzurechnen. Das entwickelte und kalibrierte Modell zur Ableitung des Pflanzenwassergehalts (PWR) erwies sich als räumlich und zeitlich übertragbar und kann auf bald verfügbare EnMAP-Daten und andere hyperspektrale Bilddaten angewendet werden. In Paper II wird das parametrische Konzept der spektralen Integralratios (SIR) eingeführt, um den Chlorophyll a- und b-Gehalt (Cab), den Karotinoidgehalt (Ccx) und den Wassergehalt (Cw) simultan aus bildgebenden Spektroskopiedaten im Wellenlängenbereich 460-1100 nm zu ermitteln. Das SIR-Konzept basiert auf der automatischen Separierung der jeweiligen Absorptionsmerkmale durch lokale Maxima- und Schnittpunkt-Analyse zwischen log-transformierter Reflektanz und konvexen Hüllen. Der Ansatz wurde anhand einer physiologisch eingeschränkten PROSAIL-Datenbank unter Berücksichtigung natürlicher Ccx-Cab-Beziehungen und Positionen der Maxima im grünen Wellenlängenbereich validiert. Die Validierung mit flugzeuggestützten spektrometrischen HyMAP-Daten ergab zufriedenstellende Ergebnisse für Cab (R2 = 0,84; RMSE = 9,06 µg cm-2) und CWC (R2 = 0,70; RMSE = 0,05 cm). Die ermittelten Ccx-Werte wurden anhand einer Plausibilitätsanalyse entsprechend der Cab-Ccx-Abhängigkeit als sinnvoll bewertet. Die Darstellung der SIR-Ergebnisse als mehrkanalige Bilder (3 segment SIR) ermöglicht zudem eine auf die drei betrachteten biochemischen Variablen bezogene, intuitive Visualisierung der dominanten Absorptionen. Der vorgestellte SIR-Algorithmus ermöglicht somit wenig rechenintensive und RTM-gestützte robuste Ableitungen der beiden wichtigsten Pigmente sowie des Wassergehalts und kann in auf jegliche zukünftig verfügbare Hyperspektraldaten angewendet werden. In Paper III wird ein hybrider Ansatz vorgestellt, der RTM mit ML kombiniert, um den Kohlenstoffgehalt (Carea) sowie die oberirdische trockene und frische Biomasse (AGBdry, AGBfresh) abzuschätzen. Das Konzept umfasst die Erstellung einer PROSAIL-Trainingsdatenbank, die Dimensionsreduzierung mittels PCA, die Reduzierung der Stichprobenanzahl mittels AL anhand des vier Jahre umspannenden MNI-Kampagnendatensatzes und das Training von Gaussian Process Regression (GPR) ML-Algorithmen. Die interne Validierung der GPR-Carea und GPR-AGB-Modelle ergab einen R2 von 0,80 für Carea und einen R2 von 0,80 bzw. 0,71 für AGBdry und AGBfresh. Die Validierung auf einem unabhängigen Datensatz, der flugzeuggestützte AVIRIS-NG-Bilder (spektral auf EnMAP umgerechnet) und In-situ-Messungen umfasste, zeigte erfolgreich die Kartierungsfähigkeiten sowohl für offene Böden als auch für grüne Felder und führte zu zuverlässigen Schätzungen auf Winterweizenfeldern bei geringen Modellunsicherheiten (< 40%). Insgesamt zeigen die vorgeschlagenen Kohlenstoff- und Biomassemodelle einen vielversprechenden Ansatz auf, der zur Ableitung dieser wichtigen Variablen über Anbauflächen aus künftigen weltraumgestützten Hyperspektralaufnahmen wie jenen von EnMAP genutzt werden kann. Als Schlussfolgerungen ergeben sich die folgenden wichtigen Erkenntnisse in Bezug auf parametrische und nichtparametrische Hybridmethoden sowie bezogen auf die Bedeutung der In-situ-Datenerfassung. (1) Unsicherheiten innerhalb des RTM PROSAIL sollten immer berücksichtigt werden. Eine mögliche Verringerung dieser Unsicherheiten steht dabei der Invertierbarkeit des Modells und dessen beabsichtigter Einfachheit entgegen. (2) Sowohl physiologische Einschränkungen als auch AL-Heuristiken sollten angewendet werden, um unrealistische Parameterkombinationen in einer PROSAIL-Kalibrierungs- oder Trainingsdatenbank zu reduzieren. (3) Modernste ML-Ansätze mit der Fähigkeit, Unsicherheitsintervalle bereitzustellen, werden als vielversprechendster Ansatz für die Lösung von Inferenzproblemen aus hyperspektralen Erdbeobachtungsdaten aufgrund ihrer synergetischen Nutzung von RTMs und der hohen Flexibilität, Genauigkeit und Konsistenz nichtlinearer nichtparametrischer Methoden angesehen. (4) Parametrische hybride Ansätze ermöglichen aufgrund ihrer algorithmischen Transparenz im Vergleich zu ML-Ansätzen tiefere Einblicke in die grundlegenden physikalischen Grenzen der optischen Fernerkundung. (5) Integralbasierte Indizes, die die verfügbare hyperspektrale Information voll ausschöpfen, können als physikalisch-basierte dimensionsreduzierte Inputs für ML-Algorithmen dienen, um entweder Schätzungen zu verbessern oder um als Eingangsdaten die verbesserte Unterscheidung von Kulturpflanzen zu ermöglichen, sobald zusätzliche Zeitreiheninformationen verfügbar sind. (6) Die Validierung quantitativer modellbasierter Schätzungen ist von entscheidender Bedeutung für die Bewertung und Verbesserung ihrer Leistungsfähigkeit in Bezug auf die zugrunde liegenden Annahmen, Modellparametrisierungen und Eingabedaten. (7) Angesichts der bald verfügbaren EnMAP-Daten sollte die Erhebung von In-situ-Daten zur Validierung von Ableitungsmethoden auf eine hohe Variabilität der gemessenen Pflanzentypen und eine hohe zeitliche Variabilität über die gesamte Vegetationsperiode abzielen sowie flächen- und biomassebasierte destruktive Messungen anstelle von LAI-skalierten Blattmessungen umfassen. Unter der Voraussetzung, dass die Messinstrumente perfekt funktionieren, hängt der Erfolg der EnMAP-Mission und der hier vorgestellten Methoden entscheidend von einem rauscharmen, präzise atmosphärisch korrigierten Reflektanzprodukt ab. Die Ergebnisse der in dieser Arbeit vorgestellten Methoden können in landwirtschaftliche Entscheidungsunterstützungssysteme für die Dünge- oder Bewässerungsplanung, die Ertragsabschätzung oder die Schätzung des Potenzials der Kohlenstoffbindung im Boden integriert werden, um eine nachhaltige Intensivlandwirtschaft in der Zukunft zu ermöglichen

Investigating The Impact Of Canopy Architecture On The Radiation Use Efficiency And Yield Of Sugar Beet

Current sugar beet varieties vary widely in their canopy architecture; some have a distinctively prostrate canopy angle whilst others are much more upright. Radiation use efficiency (RUE) is the amount of biomass accumulated per unit of light intercepted by the crop. In crops such as rice and wheat, where canopy architecture has been quantified, canopy angle has been shown to significantly influence light interception and RUE. This project quantifies canopy architecture and assesses its effect on RUE and yield of sugar beet. A combination of controlled environment and field experiments were conducted to classify varieties into canopy types according to petiole angle and assess the impact of canopy angle on light interception, photosynthesis and biomass accumulation in the crop. Prostrate canopy types were demonstrated as having the greatest canopy expansion rate and to intercept more light across the season than an upright or intermediate canopy type. However, despite intercepting the most light, this did not lead to the greatest sugar yield. This research shows evidence that prostrate canopy types have lower rates of photosynthesis, and that the canopy is acclimated to shaded conditions indicative of the overlapping nature of the leaves within and between rows. Upright canopy types had the greatest RUE of total biomass later in the season and could be suited to a later harvest due to potentially more efficient light interception at lower sun angles. Furthermore, the upright canopy angle was demonstrated as advantageous to the crop during hot and droughted weather conditions, when it retained more of its canopy. Intermediate canopy types had the greatest photosynthetic potential under optimal conditions. This trait can be associated with high carbon assimilation throughout the summer months in the absence of significant plant stress leading to high total biomass RUE. The Intermediate 2 variety also showed favourable biomass partitioning to the roots later in the season and this also resulted in high sugar yields. However, the Intermediate 1 variety, with similar canopy architecture yielded less and had a lower RUE. This variety was from a different breeder which may indicate that genetic traits, other than canopy architecture, are also important in determining yield and RUE. To investigate the relationship between canopy angle, RUE and yield a canopy manipulation experiment was conducted. The high yielding intermediate variety was made upright, prostrate or left as a control. Canopy manipulation had no effect on final sugar yield and the upright treatment had a higher RUE in 2022 which was a result of less proportional canopy loss and better tolerance during the drought between July and September 2022. Therefore, biomass partitioning, and high levels of photosynthesis are important traits to select for high RUE and sugar yields. However, further research is required to understand the interaction between canopy angle and RUE in winter months and water stressed conditions. Overall, the findings from this have shown that sugar beet varieties can be classified into canopy types according to their petiole angle. Canopy angle is not as important as photosynthetic rate and biomass partitioning for high RUE and sugar yields. The impact of canopy angle on drought tolerance and harvest timing should be explored by breeders in the future

The impact of two root‐symbiotic fungi on tomato plant indirect defense against Spodoptera exigua herbivory

Durch die sorgfältige Integration mehrerer Pest-Management-Maßnahmen (IPM) soll der schädliche Einfluss von Pestiziden verringert und die Biodiversität gesichert werden. Es ist wichtig, die Abwehrmechanismen der Pflanzen zu verstehen, um nachhaltige IPM zu erreichen. Darüber die durch metabolomische Veränderungen beeinflussten Interaktionen der Pflanzen mit umgebenen Organismen sind dabei von besonderer Bedeutung. Im Mittelpunkt steht dabei, ob die häufig verwendeten symbiotischen Wurzelpilze die Induzierte-Systematische-Resistenz (ISR) erhöhen können. Das bisherige Wissen beschränkt sich allerdings auf wenige spezialisierte Pflanzenfresser und deren Feinde. Daher zielte meine Studie darauf ab, die Veränderungen der flüchtigen Blattduftstoffe und Transkripte von Tomatenpflanzen zu untersuchen, die mit S. exigua befallen sind. Die Studie konzentrierte sich auf die Wirkung zweier wurzelsymbiotischer Pilze, R. irreguläris und T. harzianum, auf die Induktion der indirekten Abwehr. Um die Wirkung dieser Veränderungen auf die Anziehung von Insektenfressern zu bewerten, führte ich außerdem Olfaktometer-Bioassays mit M. pygmaeus durch, einem allesfressenden Insektenfressern zahlreicher Schadinsekten. Für ein Gewächshausexperiment züchtete ich Tomatensetzlinge, die mit kommerziellem R. irreguläris-Inoculum oder dem Laborstamm T. harzianum T-78 beimpft wurden, und nicht beimpfte Setzlinge für vier Wochen. Nachdem ich die Pflanzen 24 Stunden lang mit Larven von S. exigua im 3. Larvenstadium herausgefordert hatte, fing ich flüchtige Blattduftstoffe mit Silikonröhrchen ein und sammelte Blätter für die RNA-Extraktion. Anschließend wurden Profile von den flüchtigen Blattbestandteilen mittels GC-MS analysiert und die JA-/SAbezogene Genexpression mittels RT-qPCR gemessen. Darüber hinaus ließ ich weibliche M. pygmaeus durch ein Y-förmiges Röhrchen zwischen zwei Geruchsquellen wählen und analysierte die Bevorzugung von HIPVs von mit Mikroben inokulierten Tomatenpflanzen. Clustering-Analysen ergaben, dass mikrobielle Symbiosen zu deutlichen Veränderungen der Profile von den flüchtigen Blattbestandteilen nach S. exigua-Herbivoren führten. Im Gegensatz dazu zeigten differentielle Genexpressionsanalysen eine Suppression durch beide Symbionten in einigen JA-assoziierten Genen, während das SAMT-Gen eine leichte ISR in mit T. harzianum inokulierten Pflanzen bei Herbivorie zeigte. Die von M. pygmaeus getroffene Auswahl war bei mit R. irreguläris beimpften Pflanzen-HIPVs signifikant höher als bei nicht beimpften Pflanzen, und mit T. harzianum beimpfte Pflanzen waren für Insektenfressern attraktiver als mit R. irreguläris beimpfte Pflanzen. Diese Ergebnisse weisen gemeinsam darauf hin, dass die mikrobiellen Symbionten eine höhere indirekte Abwehr induzieren können, indem sie die Phytohormon-Signaltransduktion und die flüchtige Emission modulieren. Die Diskrepanz in den Ergebnissen und die Kontextabhängigkeit der ISR erfordern transkriptomische Ansätze und Zeitverlaufsexperimente unter sorgfältiger Berücksichtigung abiotischer Faktoren. Schließlich entdeckte diese Studie unbemerkte Interaktionen zwischen generalistischen Insekten, die durch symbiotische Mikroben durch Pflanzenabwehr moduliert werden, und schlug eine mögliche Integration aktueller Pflanzenschutzmaßnahmen vor.Careful integration of multiple pest management measures (IPM) is expected to reduce the harmful influence of pesticides and help to secure our biodiversity. It is important to understand plant defense mechanisms and interactions among surrounding organisms through plant metabolomic changes to achieve IPM. Especially whether commonly used symbiotic root fungi can raise Induced-Systematic-Resistance (ISR) is taking center stage, but our knowledge has been limited to only a few specialist herbivores and enemies. herefore, my study aimed to investigate the changes in volatile emission and transcripts of tomato plants infested with S. exigua and focused on the effect of two root-symbiotic fungi, R. irregularis and T. harzianum, on the induction of indirect defense. In addition, to evaluate the effect of these changes on predator attraction, I conducted olfactometer bioassays with M. pygmaeus, an omnivorous predator of numerous pest insects. For a greenhouse experiment, I grew tomato seedlings inoculated with commercial R. irregularis inoculum or laboratory strain T. harzianum T-78 and non-inoculated seedlings for four weeks. After challenging the plants with S. exigua 3rd instar larvae for 24 hours, I trapped leaf volatiles using silicon tubes and collected leaves for RNA extraction. Subsequently, volatile profiles were analyzed using GC-MS, and JA-/SA-related-gene expression was measured using RT-qPCR. In addition, I let female M. pygmaeus choose between two odor sources through a Y-shaped tube and analyzed the preference toward HIPVs of microbeinoculated tomato plants. Clustering analysis revealed that microbial symbioses resulted in distinct changes in volatile profiles after S. exigua herbivory. By contrast, differential gene expression analyses showed suppression by both symbionts in some JA-associated genes, while the SAMT gene showed slight ISR upon herbivory in T. harzianum-inoculated plants. The selection made by M. pygmaeus was significantly higher in R. irregularis-inoculated plant HIPVs than in noninoculated plants, and T. harzianum-inoculated plant was more attractive to predators than R. irregularis-inoculated plant. These results jointly indicate that the microbial symbionts may induce higher indirect defense by modulating phytohormone signal transduction and volatile emission. The discrepancy in the results and the context-dependency of ISR call for transcriptomic approaches and time-course experiments with careful consideration of abiotic factors. Finally, this study discovered unnoticed interaction among generalist insects modulated by symbiotic microbes through plant defense and suggested possible integration of current plant protection measures