88 research outputs found
Opportunities and limitations of crop phenotyping in southern european countries
ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts.
Mediterranean crops are frequently subjected to high evapotranspiration demands,
soil water deficits, high temperatures, and photo-oxidative stress. These conditions
will become more severe due to global warming which poses major challenges to the
sustainability of the agricultural sector in Mediterranean countries. Selection of crop
varieties adapted to future climatic conditions and more tolerant to extreme climatic events
is urgently required. Plant phenotyping is a crucial approach to address these challenges.
High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved
genotypes and is one of the most effective strategies to improve the sustainability of
agricultural production. In spite of the remarkable progress in basic knowledge and
technology of plant phenotyping, there are still several practical, financial, and political
constraints to implement HTPP approaches in field and controlled conditions across the
Mediterranean. The European panorama of phenotyping is heterogeneous and integration
of phenotyping data across different scales and translation of “phytotron research” to the
field, and from model species to crops, remain major challenges. Moreover, solutions
specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses)
are in high demand, as the region is vulnerable to climate change and to desertification
processes. The specific phenotyping requirements of Mediterranean crops have not
yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor,
though the limited availability of skilled personnel may also impair its implementation in
Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures
is hindering the development of new Mediterranean agricultural varieties and will negatively
affect future competitiveness of the agricultural sector. We provide an overview of the
heterogeneous panorama of phenotyping within Mediterranean countries, describing the
state of the art of agricultural production, breeding initiatives, and phenotyping capabilities
in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify
strategies to overcome barriers and maximize the benefits of phenotyping and modeling
approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio
High-throughput field phenotyping in cereals and implications in plant ecophysiology
[eng] Global climate change effects on agroecosystems together with increasing world population is already threatening food security and endangering ecosystem stability. Meet global food demand with crops production under climate change scenario is the core challenge in plant research nowadays. Thus, there is an urgent need to better understand the underpinning mechanisms of plant acclimation to stress conditions contributing to obtain resilient crops. Also, it is essential to develop new methods in plant research that permit to better characterize non-destructively plant traits of interest. In this sense, the advance in plant phenotyping research by high throughput systems is key to overcome these challenges, while its verification in the field may clear doubts on its feasibility. To this aim, this thesis focused on wheat and secondarily on maize as study species as they make up the major staple crops worldwide. A large panoply of phenotyping methods was employed in these works, ranging from RGB and hyperspectral sensing to metabolomic characterization, besides of other more conventional traits. All research was performed with trials grown in the field and diverse stressor conditions representative of major constrains for plant growth and production were studied: water stress, nitrogen deficiency and disease stress. Our results demonstrated the great potential of leave-to-canopy color traits captured by RGB sensors for in-field phenotyping, as they were accurate and robust indicators of grain yield in wheat and maize under disease and nitrogen deficiency conditions and of leaf nitrogen concentration in maize. On the other hand, the characterization of the metabolome of wheat tissues contributed to elucidate the metabolic mechanisms triggered by water stress and their relationship with high yielding performance, providing some potential biomarkers for higher yields and stress adaptation. Spectroscopic studies in wheat highlighted that leaf dorsoventrality may affect more than water stress on the reflected spectrum and consequently the performance of the multispectral/hyperspectral approaches to assess yield or any other relevant phenotypic trait. Anatomy, pigments and water changes were responsible of reflectance differences and the existence of leaf-side-specific responses were discussed. Finally, the use of spectroscopy for the estimation of the metabolite profiles of wheat organs showed promising for many metabolites which could pave the way for a new generation phenotyping. We concluded that future phenotyping may benefit from these findings in both the low-cost and straightforward methods and the more complex and frontier technologies.[cat] Els efectes del canvi climĂ tic sobre els agro-ecosistemes i l’increment de la poblaciĂł mundial posa en risc la seguretat alimentĂ ria i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producciĂł d’aliments sota l’escenari del canvi climĂ tic Ă©s el repte central a la Biologia Vegetal. Per això, Ă©s indispensable entendre els mecanismes subjacents de l’aclimataciĂł a l’estrès que permeten obtenir cultius resilients. TambĂ© Ă©s precĂs desenvolupar nou mètodes de recerca que permetin caracteritzar de manera no destructiva els trets d’interès. L’avenç del fenotipat vegetal amb sistemes d’alt rendiment Ă©s clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundĂ riament en el panĂs com a espècies d’estudi ja que constitueixen els cultius bĂ sics arreu del mĂłn. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a hĂper-espectrals fins a la caracteritzaciĂł metabolòmica. La recerca s’ha dut a terme en assajos de camp i s’han avaluat diversos tipus d’estrès representatius de les majors limitacions pel creixement i producciĂł vegetal: estrès hĂdric i biòtic i deficiència de nitrogen. Els resultats demostraren el gran potencial dels trets del color RGB (des de la planta a la capçada) pel fenotipat de camp, ja que foren indicadors precisos del rendiment a blat i panĂs sota condicions de malaltia i deficiència de nitrogen i de la concentraciĂł de nitrogen foliar a panĂs. La caracteritzaciĂł metabolòmica de teixits de blat contribuĂ a esbrinar els processos metabòlics endegats per l’estrès hĂdric i la seva relaciĂł amb comportament genotĂpic, proporcionant bio-marcadors potencials per rendiments mĂ©s alts i l’adaptaciĂł a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar mĂ©s que l’estrès hĂdric sobre l’espectre de reflectĂ ncia i consegĂĽentment sobre el comportament de les aproximacions multi/hĂper-espectrals per avaluar el rendiment i d’altres trets fenotĂpics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimaciĂł del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generaciĂł. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies mĂ©s sofisticades i d’avantguarda
Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview
Machine learning techniques are widely used for pixel-wise classification of hyperspectral images. These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded anci on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size anci energy consumption) anci classification accuracy
Study of land degradation and desertification dynamics in North Africa areas using remote sensing techniques
In fragile-ecosystem arid and semi-arid land, climatic variations, water scarcity and human pressure
accelerate ongoing degradation of natural resources. In order to implement sustainable
management, the ecological state of the land must be known and diachronic studies to monitor and
assess desertification processes are indispensable in this respect. The present study is developed in
the frame of WADIS-MAR (www.wadismar.eu). This is one of the five Demonstration Projects
implemented within the Regional Programme “Sustainable Water Integrated Management (SWIM)”
(www.swim-sm.eu ), funded by the European Commission and which aims to contribute to the
effective implementation and extensive dissemination of sustainable water management policies
and practices in the Southern Mediterranean Region. The WADIS-MAR Project concerns the
realization of an integrated water harvesting and artificial aquifer recharge techniques in two
watersheds in Maghreb Region: Oued Biskra in Algeria and wadi Oum Zessar in Tunisia.
The WADIS MAR Project is coordinated by the Desertification Research Center of the University
of Sassari in partnership with the University of Barcelona (Spain), Institut des RĂ©gions Arides
(Tunisia) and Agence Nationale des Ressources Hydrauliques (Algeria) and the international
organization Observatorie du Sahara et du Sahel. The project is coordinated by Prof. Giorgio
Ghiglieri. The project aims at the promotion of an integrated, sustainable water harvesting and
agriculture management in two watersheds in Tunisia and Algeria. As agriculture and animal
husbandry are the two main economic activities in these areas, demand and pressure on natural
resources increase in order to cope with increasing population’s needs. In arid and semiarid study
areas of Algeria and Tunisia, sustainable development of agriculture and resources management
require the understanding of these dynamics as it withstands monitoring of desertification
processes.
Vegetation is the first indicator of decay in the ecosystem functions as it is sensitive to any
disturbance, as well as soil characteristics and dynamics as it is edaphically related to the former.
Satellite remote sensing of land affected by sand encroachment and salinity is a useful tool for
decision support through detection and evaluation of desertification indicating features.
Land cover, land use, soil salinization and sand encroachment are examples of such indicators that
if integrated in a diachronic assessment, can provide quantitative and qualitative information on the
ecological state of the land, particularly degradation tendencies. In recent literature, detecting and
mapping features in saline and sandy environments with remotely sensed imagery has been reported
successful through the use of both multispectral and hyperspectral imagery, yet the limitations to
both image types maintain “no agreed-on best approach to this technology for monitoring and
mapping soil salinity and sand encroachment”. Problems regarding the image classification of
features in these particular areas have been reported by several researchers, either with statistical or
neural/connectionist algorithms for both fuzzy and hard classifications methods.
In this research, salt and sand features were assessed through both visual interpretation and
automated classification approaches, employing historical and present Landsat imagery (from 1984
to 2015).
The decision tree analysis was chosen because of its high flexibility of input data range and type,
the easiness of class extraction through non-parametric, multi-stage classification. It makes no a
priori assumption on class distribution, unlike traditional statistical classifiers. The visual
interpretation mapping of land cover and land use was undergone according to acknowledged
standard nomenclature and methodology, such as CORINE land cover or AFRICOVER 2000,
Global Land Cove 2000 etc. The automated one implies a decision tree (DT) classifier and an
unsupervised classification applied to the principal components (PC) extracted from Knepper ratios
composite in order to assess their validity for the change detection analysis. In the Tunisian study
area, it was possible to conduct a thorough ground truth survey resulting in a record of 400 ground
truth points containing several information layers (ground survey sheet information on various land
components, photographs, reports in various file formats) stored within the a shareable standalone
geodatabase. Spectral data were also acquired in situ using the handheld ASD FieldSpec 3 Jr. Full
Range (350 – 2500 nm) spectroradiometer and samples were taken for X-ray diffraction analysis.
The sampling sites were chosen on the basis of a geomorphological analysis, ancillary data and the
previously interpreted land cover/land use map, specifically generated for this study employing
Landsat 7 and 8 imagery. The spectral campaign has enabled the acquisition of spectral reflectance
measurements of 34 points, of which 14 points for saline surfaces (9 samples); 10 points for sand
encroachment areas (10 samples); 3 points for typical vegetation (halophyte and psammophyte) and
7 points for mixed surfaces.
Five of the eleven indices employed in the Decision Tree construction were constructed throughout
the current study, among which we propose also a salinity index (SMI) for the extraction of highly
saline areas. Their application have resulted in an accuracy of more than 80%. For the error
estimation phase, the interpreted land cover/use map (both areas) and ground truth data (Oum
Zessar area only) supported the results of the 1984 to 2014 salt – affected areas diachronic analysis
obtained through both automatic methods. Although IsoDATA classification maps applied to
Knepper ratios Principal Component Analysis has proven its good potential as an approach of fast
automated, user-independent classifier, accuracy assessment has shown that decision tree outstood
it and was proven to have a substantial advantage over the former. The employment of the Decision
Tree classifier has proven to be more flexible and adequate for the extraction of highly and
moderately saline areas and major land cover types, as it allows multi-source information and
higher user control, with an accuracy of more than 80%.
Integrating results with ancillary spatial data, we could argue driving forces, anthropic vs natural, as
well as source areas, and understand and estimate the metrics of desertification processes. In the
Biskra area (Algeria), results indicate that the expansion of irrigated farmland in the past three
decades contributes to an ongoing secondary salinization of soils, with an increase of over 75%. In
the Oum Zessar area (Tunisia), there was substantial change in several landscape components in the
last decades, related to increased anthropic pressure and settlement, agricultural policies and
national development strategies. One of the most concerning aspects is the expansion of sand
encroached areas over the last three decades of around 27%
XX Convegno nazionale dell'Associazione italiana di Agrometeorologia (AIAM). XLVI Convegno nazionale della SocietĂ italiana di Agronomia (SIA). Strategie integrate per affrontare le sfide climatiche e agronomiche nella gestione dei sistemi agroalimentari. Integrated strategies for agro-ecosystem management to address climate change challenges.
Atti del convegno nazionale di due delle principali societĂ scientifiche che si occupano di scienze agrarie (SocietĂ Italiana di Agronomia e Associazione Italiana di AgroMeteorologia), quest'anno effettuato congiuntamente. Nel convegno si Ă trattato dei problemi e delle nuove strategie integrate per affrontare le sfide climatiche e agronomiche nella gestione dei sistemi agroalimentari
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