Plant Breeding and Management Strategies to Minimize the Impact of Water Scarcity and Biotic Stress in Cereal Crops under Mediterranean Conditions

Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.info: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 climàtic sobre els agro-ecosistemes i l’increment de la població mundial posa en risc la seguretat alimentària i l’estabilitat dels ecosistemes. Actualment, satisfer les demandes de producció d’aliments sota l’escenari del canvi climàtic és el repte central a la Biologia Vegetal. Per això, és indispensable entendre els mecanismes subjacents de l’aclimatació a l’estrès que permeten obtenir cultius resilients. També és precís 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 és clau per abordar aquests reptes. La present tesi s’enfoca en el blat i secundàriament en el panís com a espècies d’estudi ja que constitueixen els cultius bàsics arreu del món. Un ampli ventall de mètodes de fenotipat s’han utilitzat, des sensors RGB a híper-espectrals fins a la caracterització 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 producció vegetal: estrès hídric 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 panís sota condicions de malaltia i deficiència de nitrogen i de la concentració de nitrogen foliar a panís. La caracterització metabolòmica de teixits de blat contribuí a esbrinar els processos metabòlics endegats per l’estrès hídric i la seva relació amb comportament genotípic, proporcionant bio-marcadors potencials per rendiments més alts i l’adaptació a l’estrès. Estudis espectroscòpics en blat van demostrar que la dorsoventralitat pot afectar més que l’estrès hídric sobre l’espectre de reflectància i consegüentment sobre el comportament de les aproximacions multi/híper-espectrals per avaluar el rendiment i d’altres trets fenotípics com anatòmics i contingut de pigments. Finalment, l’ús de l’espectroscòpia per l’estimació del contingut metabòlic als teixits de blat resulta prometedor per molts metabòlits, la qual cosa obre les portes per a un fenotipat de nova generació. El fenotipat pot beneficiar-se d’aquestes troballes, tant en els mètodes de baix cost com de les tecnologies més sofisticades i d’avantguarda

A Research Road Map for Responsible Use of Agricultural Nitrogen

Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains

Mathematical statistics vs machine learning: towards an intelligent modeling framework for soil and plant growth processes

Mestrado de dupla diplomação com a Kuban State Agrarian UniversityThe work described in this dissertation focuses on the methods for analyzing MS and ML that are used in PF. The purpose of the work is to investigate these methods on their practical application to a specific set of data. In the course of the work, the following tasks were completed: the current state of affairs in the field of PF was investigated, the theoretical foundations of the methods of MS and ML were investigated, which were subjected to practical tests on a specific set of data. Conclusions were drawn about the advantages and disadvantages of these methods. A selection of works of scientists engaged in research on the introduction of a specific set of nutrients into the soil was also investigated. The most important contributions to this work are the practical application of various methods of analysis, as well as the design of a DST designed to help farmers integrate PF into their pilot training farms.O trabalho descrito nesta dissertação versa sobre métodos e técnicas no âmbito da Estatística Matemática e de ML usados para efeitos de previsão de colheitas e tratamento de solos em agricultura de precisão. O objetivo do trabalho é investigar esses métodos em sua aplicação prática a um conjunto específico de dados. No decorrer do trabalho, foram realizadas as seguintes tarefas: investigou-se a situação atual no campo da agricultura de precisão, investigaram-se os fundamentos teóricos dos métodos e técnicas da estatística matemática e de ML. Estes métodos e técnicas foram submetidos a testes práticos em um conjunto específico de dados. Foram tiradas conclusões sobre as vantagens e desvantagens desses métodos: Uma seslção de trabalhos científicos relacionados com a investigação sobre a introdução de um conjunto específico de nutrientes no solo foram também investigados. As contribuições mais importantes para este trabalho são a aplicação prática de vários métodos de análise, bem como o projeto de uma ferramenta de apoio à decisão projetada para ajudar os agricultores a integrar a agricultura de precisão nas suas propriedades agrícolas

Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan

Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area

Spectral Mixture Analysis for Monitoring and Mapping Desertification Processes in Semi-arid Areas in North Kordofan State, Sudan

Multi-temporal remotely sensed data (MSS, TM and ETM+)were used for monitoring and mapping the desertification processes in North Kordofan State, Sudan.A liear mixture model (LMM) was adopted to analyse and the desertification proccesses by using the image endmembers. interpretation of ancillary data and field observation was adopted to verfiy the role of human impacts in the temporal changes in the study area. The findings of the study proved the powerfull of remotely sensed data in monitoring and mapping the desertification processes and come out with valuable recommendations which could contribute positively in reducing desert encroachment in the area

Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r2 = 0.65, y = 4.01 tha−1; r2 = 0.70, y = 4.28 tha−1) than of III-S (r2 = 0.012, y = 3.54 tha−1; r2 = 0.020, y = 3.7 tha−1). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period