Graduate students research

Here CIMMYT offers research opportunities for graduate students and post-docs. These research topics are already funded by CIMMYT’s portfolio of research projects and programs. Students who are enrolled at a university and benefiting from a secured scholarship can apply for one of the topics to conduct their research thesis with CIMMYT.

If you are interested in a topic click on the title and see the description and the requirements needed.

Please contact cimmyt-academy@cgiar.org for more information and the link to the application form.


Epidemiology of the pathogens involved in tar spot complex

Brief description of the topic:

Tar spot complex (TSC) is a very important foliar disease of maize in many parts of the Americas. The disease complex involves three different fungal pathogens causing significant maize yield losses in tropical and subtropical areas with relatively cool and humid climates. Because of the complexity of the disease, very little is known about the epidemiology and virulence of each of the three pathogens. In addition, although CIMMYT already has several sources of resistance to this disease, very little is known about the relationship and diversity of the available germplasm and its resistance mechanisms.

Work expectations:

The candidate is expected to study the epidemiology of each or some of the three pathogens involved in the complex. This will include investigating modes of pathogen survival (oversummering), transmission, infection, temperature and humidity requirements for pathogen development, among other things. In addition, the candidate will be involved in studies investigating the diversity of the pathogen and plant-pathogen interactions at the phenotypic level and on a molecular level (provided that funds and resources for this are available at the time). Lastly, the candidate will help develop genetic populations for studying TSC and other maize diseases (e.g., northern leaf blight or turcicum leaf blight) and will learn field, laboratory and greenhouse techniques used to improve the disease resistance of maize germplasm.

Required skills:

Experience working with maize germplasm and pathogens; willingness to work in the field; ability to work in hot and humid environments; good English skills so the candidate will be able to write and publish scientific papers upon completion of his/her stay at CIMMYT.

Supervisor:

Sandro Loladze, Global Maize Program, a.loladze@cgiar.org

Adoption, impact and productivity of yellow rust resistant winter wheat varieties in Central Asia

Brief description of the topic:

Wheat is the essential food crop in Central Asia (CA), accounting for more than 85% of all cereals produced in the region. In recent years, yellow rust has become the most important disease affecting winter wheat in CA, which has experienced seven yellow rust epidemics over the past 15 years. Given that most commercially grown cultivars in the region are susceptible to yellow rust, several improved winter wheat cultivars with high yield potential and good levels of yellow rust resistance were recently released in the region through international collaboration. However, the lack of accelerated seed multiplication has restricted the replacement of susceptible cultivars with resistant ones. A project funded by the CGIAR WHEAT research program under a partners’ grant scheme started in 2012 with the aim of accelerating seed multiplication and the adoption of improved, yellow rust resistant winter wheats in Uzbekistan and Tajikistan. One of the project’s specific objectives was not only to increase productivity but also help farmers manage yellow rust epidemics in Central Asia and neighboring West Asian countries. Wheat varieties selected for dissemination were planted on different acreages depending on seed availability in the first year of the project (2012-2013 growing season). This has continued in subsequent years and in autumn of 2014, a total of 13,528 ha in Tajikistan and 17,520 ha in Uzbekistan were planted with improved seed. In Tajikistan more than 4,000 small farmers and organizations are participating in the program, while in Uzbekistan, more than 300 farmers grow yellow rust resistant varieties either for seed production, grain production or both.

Work expectations:

The work to be performed by the candidate may take three directions that can be negotiated once proposal is approved.

a. Adoption of yellow rust resistant winter wheat varieties (soon improved varieties) in Uzbekistan/Tajikistan;

b. Impact of improved varieties on household income and welfare in Uzbekistan/Tajikistan;

c. Analysis of the productivity and efficiency of winter wheat producers in Uzbekistan/Tajikistan;

The data for this work will come from farmer surveys to be conducted in 2016-2017, both in Tajikistan and Uzbekistan.

Skills required:

Good knowledge of econometrics with STATA skills.

Proficiency in spoken and written English is required; good scientific writing skills.

Supervisor:

Dr. Aziz Karimov, Socio Economics Program, WHEAT Adoption and Impacts, Turkey; az.karimov@cgiar.org

Value chain opportunities - Wheat

Brief description of the topic:

The focus is on “Tracking wheat varieties across the seed value chain in Ethiopia.”

Over the years, the Ethiopian seed system has evolved and gone through different phases. Currently, it incorporates actors engaged in plant breeding; variety release; breeder seed maintenance; pre-basic seed production; basic seed production; certified seed production; farmer based seed production; and seed distribution and sales. The type and number of actors in the system are growing and becoming more complex with the decentralized system. Overall seed marketing activities are carried out either through a public channel that follows a top-down distribution system or through direct marketing by private seed companies that have their own varieties. In addition, there is a certain uniqueness in the distribution system of basic and certified seeds.

While many research results indicate low levels of varietal adoption, recent research using DNA fingerprinting found higher levels of adoption and also low levels of varietal identification by farmers. The mismatch between farmers’ varietal identification and estimated adoption levels requires further investigation to gain a clear understanding of its causes. This will require designing and implementing policy and development measures.

Work expectations:

There is a need to assess the causes of the mismatch between farmers’ varietal identification and estimated adoption levels. This assessment requires generating information by interviewing relevant actors on their seed production and management practices and by analyzing collected seed at different levels and comparing actors’ responses using DNA sequencing. These activities are planned for 2017. If the proposal is accepted, the candidate will use the collected information to generate knowledge of varietal identification, the actors and the causes of the mismatch.

Required skills:

Good knowledge of econometrics with STATA skills.

Proficiency in spoken and written English is required; good scientific writing skills.

Supervisor:

Dr. Aziz Karimov, Socio Economics Program, WHEAT Adoption and Impacts, Turkey; az.karimov@cgiar.org

Using multi-point simulation modeling to estimate the spatial variability of wheat grain yield and protein within farmers’ fields

Brief description of the topic:

In crop development, there is spatial and temporal variability within a site, mainly because of soil nutrient availability and the soil’s physical and chemical properties. These variables, together with climate parameters, agronomic management choices and crop genotypes, compose the whole scenario where the crop will grow and will also cause variability in yield and
quality. In terms of investigations and simulations of the main factors related to yield and quality, the use of crop models and the identification of important soil attributes during each crop cycle may help determine which nutrient-supply and variety-management practices should be used in the various cropping environments. The application of simulation models in agriculture is widespread across various industries for different uses. The global grain industry applies simulation models to answer numerous questions in areas such as biochemistry, agronomy, physiology and management.

Multi-point simulations allow point-based models to be applied multiple times within a single simulation, with data communication among all discrete points. Hence, each point in space requires its own input in the crop modules, such as a Geographic Information System (GIS) dataset, where it is possible to have several types of information for each sample point within a site. Therefore, each simulation point will have a reporting output with the studied factors. Based on this, the hypothesis of this work is that through multi-point simulations within a crop model framework, it is possible to detect the spatial variability of wheat grain yield and protein within a plot, as well as to compare measured versus simulated data.

Work expectations:

The suitable candidate will be responsible for the entire parameterization of the crop model. This includes estimating the spatial variability of wheat grain yield and protein within a plot using a multi-point simulation approach and evaluating simulated spatial variability versus measurement data. Data assimilation using remote sensing data is also a goal; the candidate will have access to remote/proximal sensing data from the sites and will be expected to explore the use of such data within the crop model.

Required skills:

A background in crop modeling (e.g., APSIM, DSSAT) and program skills (e.g., R, Python, C++) are essential. Knowledge of remote sensing as well as GIS and statistics is desired. Knowledge of maize and wheat physiology is a plus.

Supervisor:

Francelino Rodrigues/Balwinder Singh; Sustainable Inntensification Program,  F.A.Rodrigues@cgiar.org; Balwinder.SINGH@cgiar.org

A Bayesian genomic prediction model combining pedigree and incomplete genotyped individuals

Brief description of the topic:

Quantitative trait variation may display a complex genetic architecture, which is the underlying basis of a phenotype, with few genes with large effects and several genes with small effects. The genes can show additive, dominance or/and epistatic effects and different types of interaction with the environment. Advances in plant genomics –and, more recently, in DNA sequencing of many species– coupled with the availability of statistical and biometric methods for analyzing genetic and phenotypic data with friendly software, have made it feasible to map and dissect complex quantitative trait variation. Genomic selection (GS) and prediction based on genome-wide single nucleotide polymorphism genotyping, pedigree and phenotypic data are very powerful tools for capturing small genetic effects dispersed over the genome; this allows predicting an individual’s phenotype. New methods and tools are continuously being developed to integrate GS into genetics research. One of the key issues with GS is the fact that there are usually many more individuals that have been phenotyped and have pedigree data than individuals with marker data. When attempting to make predictions, only lines with marker, pedigree and phenotypic data can be used, thus leaving out lines with pedigree and phenotypic data that have missing marker data. This project aims to develop a novel approach that uses Bayesian statistics to combine genotypic data with pedigree and phenotypic data and therefore use all available data. This project will lead to developing new algorithms and software for GS based on advanced statistical methods that will cause a paradigm shift in this field.

Work expectations:

The phenotypic variation of a plant population measured across locations, seasons or years can be attributed to its genetics, the environment where it grows, and genotype × environment interaction. Biometric models are used to explain traits with continuous variation because algebraic equations facilitate the understanding of quantitative genetics, which is the study of complex traits affected by the action of multi-genes. Quantitative genetic models include genes having major or small effects and the non-genetic factors affecting a complex trait. Genomic prediction (GS) uses genome-wide single nucleotide polymorphisms, pedigree and phenotypic data, and is a very powerful tool to capture small genetic effects dispersed over the genome, which allows predicting an individual’s phenotype. New methods and tools are continuously being developed to integrate GS into genetics research. One of the key issues with GS is the missing values in the genotyped data, which makes it difficult
to develop GS models with different numbers of lines with pedigree, marker, and phenotypic data. The overall aim of this project is to develop a theoretical model that includes pedigree and missing genotyping data in a unified approach, and test it with real data available from international multi-environment wheat breeding nursery trials. The specific objectives are: (1) to mix the A matrix obtained from the pedigree with the G matrix obtained from the markers into one unified H matrix. The Bayesian approach used to compute matrix H will be compared with other models used as references in GP; and (2) the prediction accuracy of our H matrix will be compared with the prediction accuracy of other methods used to compute matrix H.

Required skills:

Bayesian analysis and computer programming in R.

Supervisor:

José Crossa; Genetic Resources Program, J.CROSSA@CGIAR.ORG

Developing an R-library for spatial analysis of experiments

Brief description of the topic:

Incomplete block designs and alpha-lattice field designs have helped increase the precision of estimating genetic values of breeding individuals. However, blocks or incomplete blocks do not capture all the plot-to-plot variability in a field experiment; therefore, appropriate modeling of the error term in field trials is indeed necessary. Analytical models that model the spatial variability considering the grid location of plots in the field have improved the precision of estimating the treatment mean, which produces greater genetic gains in plant breeding than when this spatial adjustment is not performed. A limited number of expensive commercial software (SAS, ASReml, Genstat) and specifically one of the most used spatial models, the separable autoregressive in the direction of the rows and the columns, can model this spatial variability. However, no free software is available for this analysis. Some efforts have been made in R but the results are not reliable. Free software for spatial analysis of experiments will provide breeders with more precise genetic values at minimum cost.

Work expectations:

The main objective of this proposal is to develop a free R-library for the spatial analysis of experiments. A review of existing R libraries for the analysis of experiments should be performed to identify the most suitable library, which will then be modified to include the modeling of the error by a spatial model. If no suitable library is found, a new R package including spatial analysis of field trials must be developed. Other activities included in the project are to produce technical documents for scientists and plan training activities to disseminate the new library.

Required skills:

Linear mixed models and computing programming in R.

Supervisor:

Gregorio Alvarado; Genetic Resources Program, G.Alvarado@cgiar.org

Spatial variability of high-throughput phenotypic data in field experiments

Brief description of the topic:

High-throughput precision phenotyping (HPP) is rapidly becoming popular in plant breeding for its ability to facilitate measurement of data points from a broad spectrum of light reflectance wavelengths. When the obtained data points correlate well with a phenotypic trait of interest (e.g., grain yield), this allows using this technology to evaluate several plant phenotypes of agronomical importance. For example, HPP can predict grain yield weeks before harvesting date, thus reducing harvesting costs and making it possible to make decisions for the next sowing season. However, it is not clear how spatial variability affects the relationship between HPP data and the traits of interest. It is known that soil characteristics can create a spurious relationship between different variables including HPP data. Spatial or soil variability can be handled by the experimental design and by the statistical model used to analyze the data, thereby improving the use of high-throughput phenotypic data.

Work expectations:


The overall aim of this project is to understand and develop statistical models to analyze spatial variability of HPP data and their relationship with traits of interest, i.e., grain yield, flowering date. Specific objectives: (1) to test different spatial models and their ability to model HPP data; (2) to analyze the relationship between the HPP data, the spatial model and the experimental design; and (3) to publish scientific papers and technical documents to disseminate research results. The student will be responsible for collecting and curating the data, analyzing them and writing the documents. The student will work closely with BSU colleagues and give lectures to CIMMYT staff and partners.

Required skills:

Experimental design, linear mixed models.

Supervisor:

Juan Burgueño; Genetic Resources program, J.Burgueno@cgiar.org

Fusarium head blight research

Brief description of the topic:

Fusarium head blight (FHB) is a devastating disease of wheat globally, not only reducing yield but also contaminating food and feed with mycotoxins that threaten humans and animals alike. CIMMYT has worked on this disease for four decades and incorporated FHB resistance into elite wheat germplasm. Chinese germplasm is an important source of resistance and in our previous studies several lines with very good resistance were identified; however, genotyping results indicated the absence of known major QTLs. Mapping populations have been developed in the hope of identifying new QTLs in these materials and of using the QTLs in CIMMYT and Chinese breeding programs.

Work expectations:

A RIL population derived from a cross between SW87-2323 and Gamenya was developed with 200 progenies. During field evaluation for FHB in 2016, the RIL population showed good segregation and will therefore be evaluated for two more years to generate good scientific findings that can be published in refereed journals. The candidate is expected to work with this population as the main research objective, but he/she is also strongly encouraged to participate in other research activities of the wheat pathology group and possibly publish more articles, depending on the research output and findings.

Required skills:

Good knowledge of genetics and statistics is required; knowledge of breeding, phytopathology and biotechnology is desirable.

The candidate will work collaboratively with colleagues and provide necessary assistance when requested. He/she is expected to work not only in the office, but also in the field, laboratory and greenhouse, and to travel to experiment stations when necessary. For security reasons, the candidate is expected to live on campus.

Supervisors:

Drs. Xinyao He and Pawan Singh; Global Wheat Program, x.he@cgiar.org; Pk.Singh@cgiar.org

Cataloguing botanical, genetic and agronomic features of the Turkish bread wheat landrace core collection

Brief description of the topic:

The proposed project will build on the IWWIP Turkish LRACE-CORE collection and CIMMYT-Mexico genotyping to create a comprehensive description of the material. This will include cataloguing and curating all of the existing information about the core set of bread wheat landraces including collection data, botanical descriptions and agronomic evaluation. Following this, higher density genetic information will be overlaid for use in association mapping and trait dissection. A preliminary agronomic evaluation identified a number of genotypes resistant to abiotic and biotic stresses, and this will be extended through additional testing (in Turkey) in order to further investigate their genetic basis. Pre-breeding strategies will also be employed to transfer favorable landrace alleles to elite IWWIP and British winter and spring breeding lines to facilitate their use in breeding. This will require the application of quantitative genetic approaches, including genomic selection to devise optimal strategies for exploiting landrace diversity for breeding.

Work expectations:

a. Agronomic evaluation of the core set of bread wheat landraces in representative Turkish environments over two years. The evaluation will be conducted using conventional and new physiological tools in Konya, a major rainfed region of winter wheat production under moisture stress. Disease resistance will be evaluated in the country’s hot spots.

b. Genotyping the core set using high density, high-throughput genotyping approaches for analyzing genetic diversity. We will use the 35K Affymetrix Axiom SNP chip for high-throughput genotyping of the core set. We expect the student to also evaluate the core set with gene-diagnostic KASP markers related to traits evaluated under task 1.

c. Association mapping for important agronomic traits using phenotypic and genetic data to identify marker-trait associations.

Supervisor:

Alexey Ivanovich MORGOUNOV; Global Wheat Program, a.morgounov@CGIAR.ORG

Evaluating the effect of conservation agriculture-based practices on soil organic matter dynamics in rice-based cropping systems of the Indo-Gangetic Plains

Brief description of the topic:

Since the Green Revolution of the 1960s, rice-wheat cropping has been the major cropping system in the Indo-Gangetic Plains (IGP), where annual cycles of wet (puddling) and dry tillage conditions exert specific pressure on soil organic matter (SOM) dynamics. Puddling under flooded conditions destroys soil aggregates and disperses clay and SOM particles. Drainage following the rice harvest causes the clay particles to settle, creating a hardpan that makes it necessary to till before sowing the wheat. Tillage destroys soil aggregates, increases aeration and exposes SOM, making it more accessible to decomposing micro-organisms. Conservation agriculture (CA) based management practices (zero-tillage, residue retention and appropriate rotations, etc.) have been widely promoted as an alternative to intensive tillage based management practices for rice and wheat, that can result in more efficient use of water, fertilizer and fuel, maintain or increase land productivity, reduce greenhouse gases (GHGs) and also to help to cope with some weather extremes. Conservation agriculture management practices involve retaining residues on the soil surface, which has impact on the long-term buildup of SOM at different landscape levels. However, how this translates into improvements in soil health is still unclear. A holistic systems approach that combines experimentation tools and mathematical modeling is needed to evaluate the long-term effect of CA practices on multiple scales of SOM dynamics.

Work expectations:

To explore the long-term effects of CA practices on SOM dynamics, resource use efficiency and crop yields and at a landscape scale using the Roth-Landscape model (Coleman et al., in prep). The Roth-Landscape model is a two-dimensional model that simulates the vertical and horizontal interactions of C, N, P and water between the atmosphere, plants and soil. The model has been tested for soil organic carbon (SOC), nitrogen (SON), soil moisture and crop yield in several long-term experiments (LTEs) under different crop rotations available from Rothamsted Research in the UK but has yet to be calibrated and tested for tropical conditions.

Required skills:

Previous experience in modeling (desirable but not essential).

Supervisors:

C.M. Stirling, M.L. Jat and Tek Sapkota; Sustainable Intensification Program, C.Stirling@cgiar.org; M.Jat@cgiar.org; T.Sapkota@cgiar.org

Mechanistic basis of heat tolerance in wheat: Biochemical and genetic analyses

Brief description of the topic:

Rising global temperature is perhaps the single most critical factor in wheat production, as every degree Celsius rise in temperature reduces grain yield by more than 5%. CIMMYT possesses an extraordinary germplasm collection as well as sequence information on a majority of the lines. In addition, a collection of lines identified after years of field testing for variable response to heat and water stresses is available from breeders and physiologists. A set of 300-400 lines will constitute a diversity panel for genetic association mapping of stress tolerance. The panel will be grown in growth chambers and subjected to various day and night temperature treatments. Seedling biomass at the 4-6 week stage will be one of the components to map, the other being leaf photosynthate (fructose, glucose, starch, and sucrose). As photosynthate production capacity is the primary determinant of biomass accumulation, of which grain yield is a component, its continued production under stressful conditions will be the focus of our work. Aside from identifying lines for immediate use in setting up crosses to breed for stress tolerance, this project will provide QTL for various photosynthate components under normal and stressful conditions. In some cases, the QTL might be significant enough and on short enough of a chromosomal interval that candidate genes could be identified for validation either by gene editing or a transgenic approach.

Work expectations:

Compile a diversity panel of bread wheat lines based on historical field data available and sequence information. Determine growth chamber conditions that result in a 20-30% reduction in seedling biomass at elevated temperature (day as well as night) as compared to normal growing conditions. Grow the diversity panel in the growth chamber (Biosafety Laboratory), collect leaf samples before and after temperature treatment in the morning (to measure photosynthate remobilization and utilization efficiency at night) and evening (photosynthate production and storage capacity). Dry leaf samples, grind in a GenoGrinder, extract metabolites, and measure photosynthetic components using 96-well assays. Harvest seedlings after 4-6 weeks, dry, weigh, and carry out metabolite assays on the leaves and stems (for reserves). Identify QTL for photosynthate production under normal and heat stress conditions through genetic association mapping. Several publications (photosynthate profile of a growing plant, QTL for photosynthetic components, QTL for stem reserves, QTL for heat tolerance) could be prepared by this stage. The follow-up project would aim to: (1) employ these QTL directly to identify additional lines in the CIMMYT germplasm collection to provide additional options to breeders; (2) validate QTL by generating near-isogenic lines in a series of genetic backgrounds; and (3) identify potential gene candidates to be validated by gene editing or transgenic expression.

Required skills:

Ph.D. in genetics, physiology, biochemistry, or a related field.

Supervisor:

Kanwarpal S. Dhugga; Genetic Resources Program, K.DHUGGA@cgiar.org

Gene editing in wheat to improve herbicide and stress tolerance

Brief description of the topic:

Certain genes are known to affect nutrient uptake and tolerance to abiotic stresses and herbicides. For example, down-regulation of ethylene biosynthesis (via ACC synthase) is known to improve plant performance under drought stress. Similarly, molecular modification of nitrate reductase in a single amino acid increases its half-life considerably by keeping it from being targeted for degradation. Mutant forms of acetolactate synthase (ALS) have long been known to confer upon the plant the ability to survive imidazolinone herbicides. The ALS protein possesses a dozen or so amino acids which, upon alteration to other amino acids, give plants resistance to various classes of sulfonylureas, which offers the possibility of developing wheat that is tolerant to a broad spectrum of chemical herbicides. The CRISPR-Cas system is being set up in the Biosafety Laboratory at CIMMYT to edit genes. Under this project, we will either inactivate the genes for the formation of ethylene or change specific amino acids to alter the properties of the resultant protein for nutrient use efficiency or herbicide tolerance.

Work expectations:

Become familiar with wheat transformation, which has already been streamlined in the Biosafety Laboratory. Isolate protoplasts from wheat seedling leaves and use them to test various guide-RNA sequences for the gene of interest for efficacy in transient assays. Select the most efficacious gRNA for transformation in combination with Cas9 into immature embryos. Regenerate plants, analyze for gene alteration by PCR and by next generation sequencing (after adding unique adaptors and combining DNA from 384 plants into a single sample). Test the plants with edited genes for the appropriate traits. Each edited gene would potentially lead to publishing a paper in a high-profile journal.

Required skills:

Ph.D. in genetics, physiology, biochemistry, or a related field.

Supervisor:

Kanwarpal S. Dhugga; Genetic Resources Program, K.DHUGGA@cgiar.org

High-throughput phenotyping for maize and wheat using remote sensing technology

Brief description of the topic:

Plant phenotyping is one of the most costly and labor-intensive activities in a breeding program. High-throughput phenotyping methods have been broadly used to estimate crop traits under different scenarios using different tools, and remote sensing is a promising technology that can provide rapid access to a large number of plots in short periods of time. Canopy temperature taken by thermal cameras and biomass dynamics through normalized vegetation index (NDVI) acquired with multispectral cameras, both on board unmanned aerial vehicles (UAV) or airborne, are already a reality at CIMMYT. Besides these applications, this technology is currently being tested in maize and wheat research programs for further applications, such as plant height, plot volume and disease detection. Research in this field of expertise is required and real advances will have an impact within CIMMYT and in the global phenotyping research community.

Work expectations:

The candidate will be responsible for the entire remote sensing component of the experiments. This includes the UAV flight campaign and using UAV for making a flight plan and executing flights with different sensors in different locations. Image processing, data extraction and analysis will be essential tasks.

Required skills:

Background in remote sensing and excellent skills in GIS and statistics are essential. Knowledge of maize and wheat physiology is a plus.

Supervisor:

Francelino Rodrigues; Sustainable Intensification Program, F.A.Rodrigues@cgiar.org

Sustainable intensification and soil physical properties: A synthesis of data from contrasting cropping systems and ecologies in the Indo-Gangetic Plains

Brief description of the topic:

Conservation agriculture (CA) influences crop growth and yield through changes in soil physical properties, which are also thought to be beneficial for longer-term soil quality and sustainability. Frequently observed changes include increased aggregate stability, water infiltration rate, water retention, decreased penetration resistance (an indicator of ease of root penetration), and reduced sub-soil compaction as well as reduced risk of soil erosion. These soil physical changes are generally associated with increased organic carbon content that can result from: (1) increased total organic matter input from crop residues compared to the conventional system; (2) altered composition or location of inputs resulting from cropping system diversification; (3) concentration of organic matter near the soil surface caused by elimination of tillage; or (4) a combination of these factors. However, the mechanisms underlying these soil physical changes are not well understood. In alarge number of experimental sites across the Indo-Gangetic Plains (IGP) of South Asia, combinations of CA-based practices have been applied over several years and on different soil types and cropping systems. The main aim of the proposed project is to synthesize existing data on soil chemical and physical properties to determine whether thresholds exist in terms of amount and quality of organic matter inputs needed to bring about beneficial changes in soil physical properties that will ultimately improve adaptation to climatic variability and extremes. A second aim is to test the suitability of a range of novel physical measurement techniques for detecting and quantifying changes in soil organic matter and physical properties.

Work expectations:

The research will involve the use of a range of soil science techniques including thermo-gravimetric analysis (TGA), currently being investigated in collaboration with Rothamsted Research and China Agricultural University; TGA is a means of distinguishing between organic C in different chemical forms, thought to be significant in altering physical properties. Other techniques will include measuring soil shear strength and compression through triaxial testing. A third aim is to develop simple measurements of potential value for field use. The water release characteristics and water repellence of soils will also be measured to evaluate differences in pore sizes due to the effects of CA. These data will be used to link hydraulic and mechanical properties with simple but mechanistic models.

Required skills:

Previous research experience in soil science, agronomy or soil physics.

Supervisor:

C.M. Stirling and M.L. Jat; Sustainable Intensification Program, C.Stirling@cgiar.org;M.Jat@cgiar.org

Seed treatment for eliminating/reducing seed contamination due to MLN infection

Brief description of the topic:

Maize hybrid seeds from various genotypic backgrounds that are resistant, tolerant or susceptible to maize lethal necrosis (MLN) were grown under uniform MLN disease pressure using artificial inoculation at an MLN screening facility in Naivasha, Kenya. Many methods and approaches are available to eliminate virus-contaminated seeds, which are washed and made available with no surface contamination after the treatment. The objectives of the future studies are: (1) to treat MLN-contaminated seeds with chemicals and understand the viability of the treated seeds from various genotypic backgrounds; (2) to study the longevity of the seed treatment with respect to the virus-free period and seed viability; (3) to study MLN contamination with reference to post treatment and see the impact of yield; (4) to study the possibility of eliminating MLN from seeds even from internal infection; and (5) to draw conclusions and study the efficacy of the seed treatment to overcome the MLN seed contamination.

Work expectations and required skills:

This project needs a student who is interested in data collection to assist in the experiment and analyses, interpret the results, and publish the results in the form of a full manuscript. The student should have theoretical knowledge of plant pathology and seed technology or agronomy. Knowledge of software is an additional advantage. We expect the student to check outliers from seed treatment data, interpret the results and their future implications.

Supervisor:

Suresh, L.M.; Global Maize Program, l.m.suresh@cgiar.org

Optimal flowering time to maximize wheat yields in low rainfall areas of Turkey and Iran

Brief description of the topic:

Flowering time in wheat is one of the most important mechanisms of abiotic stress escape, which, if fine-tuned to escape environmental stresses like drought, cold and heat, will maximize grain fertility and yield. Fine-tuning flowering time and including genes related to wheat’s adaptation to the most likely environmental scenarios will increase the crop’s resilience to climate change in low rainfall areas of Turkey and Iran. Moreover, drought tolerance must be evaluated in a wide range of germplasm showing very similar flowering time. To evaluate drought tolerance, stress indices will be developed at key developmental stages to help identify easy-to-measure resilience traits and sources of tolerant germplasm.

Work expectations:

• Analysis of large sets of climatic, phenotypic and genotypic data to determine optimal flowering time in the region to maximize yield in Turkey and Iran. Several models will be developed using at least 50 different sets of wheat genotypes showing contrasting flowering time under different rainfall patterns.

• Development of indices to characterize drought events throughout crop development and their probability across years in Turkey and Iran through the analysis of climatic data.

• Analysis of large sets of germplasm (modern and landraces) with similar flowering time previously characterized in field trials to determine drought tolerance mechanisms. This analysis will start by identifying sets of germplasm with different grain yield not determined by a drought escape strategy and by identifying other crop physiological mechanisms underlying that response.

• Summarize results in tables and diagrams and draft a publication.

Required skills:

Basic statistics skills (ANOVA, GxE analysis); basic knowledge of wheat crop development.

Supervisor:

Marta da Silva Sabino Lopes; Global Wheat Program, m.DaSilva@cgiar.org

A decision support tool for targeting nutrient management recommendations for maize in Eastern Kenya

Brief description of the topic:

Low and inappropriate use of nutrient inputs by smallholder farmers is a major factor underlying low crop productivity in sub-Saharan Africa. Current fertilizer recommendations were mostly developed to maximize production and ignore the financial limitations faced by various categories of farmers, and fail to address the high uncertainty of the returns on investment in fertilizer associated with variable soil fertility conditions and rainfall variability. A feasible option for ensuring sustainable crop production intensification is the use of simple but robust decision support tools that provide guidelines for fertilizer use and expected yield levels at various spatial and temporal scales. In SSA, a number of QUEFTS (Quantitative Evaluation of the Fertility of Tropical Soils)-based decision support tools have been successfully developed for exploring responses to fertilizers across heterogeneous farms. The same concepts underpinning these models were also used to develop the Nutrient Expert, a simple decision tool for use by extension services to develop and disseminate site-specific nutrient management recommendations. The proposed research aims to use existing long-term trials in Eastern Kenya to evaluate the added agronomic and economic value of using simple decision support tools over current blanket recommendations and recommendations based on soil testing.

Work expectations:

The candidate is expected to: (1) to analyze data sets on soil fertility and maize yields; (2) use these data to calibrate and test the decision support tool Nutrient Expert; and (3) write down the findings of the study in a paper to be published in a peer-reviewed journal.

Required skills:

Computer skills.

Supervisor:

Marc Corbeels; Sustainable Intensification Program, marc.corbeels@cirad.fr

Design and implementation of a processing scheme for phenotypic data within the context of physiological breeding

Brief description of the topic:

Field phenotyping is probably one of the most important bottlenecks to be eliminated in order to optimize the exploration, evaluation and selection of the available genetic resources in wheat. Technological advances are helping to overcome this problem by providing tools that enable high-throughput measurements of plant traits. However, at the same time, huge amounts of data are being generated, and most breeding programs lack suitable schemes and tools to make adequate use of this information. Every year CIMMYT’s wheat physiology group evaluates large numbers of genotypes for several physiological traits in different environments. Fast and efficient analysis of such data is essential for proper data management and storage. Implementing a data management scheme is not a trivial task, for it requires a methodological approach to find the most operational and reliable design. The research topic suggested in this proposal consists of developing a data processing scheme that integrates all the steps from data collection to final compilation into a database. The main focus of this project will be the development of a tool for statistical analysis where the candidate will work with a number of current data sets. The final goal is to have a platform that allows quick access and analysis of phenotypic data, to improve the outcomes of other research projects and help decision making in our pre-breeding program.

Work expectations:

• Become familiar with the data collected by the wheat physiology group.

• Make a list of the statistical analyses required by the group.

• Develop a computer program using R or Python for data curation and statistical analyses (main objective).

• Design a processing scheme for defining each step, from data collection up to the final database (based on the requirements of the wheat physiology group).

• Propose and implement a workflow for effective integration of the steps identified in the previous point.

Required skills:

• Advanced knowledge of R, Python or a similar open-source programming language.

• Advanced knowledge of statistics.

• Functional English.

Supervisor:

Carolina Rivera and Francisco Pinto; Global Wheat program, A.RIVERA@cgiar.org ; FR.PINTO@cgiar.org

Analysis of genetic variability for wheat grain fructans

Brief description of the topic:

Fructans are functional food ingredients that deserve attention for their potential health benefits. They selectively promote the growth of beneficial bifidobacteria in the human gut, making the digestive system work more effectively, thereby increasing the absorption of more beneficial nutrients, particularly calcium and iron. Iron availability and absorption are particularly important, given that billions of people are iron deficient. Significant genotypic variation has been described for these bioactive components, with grain fructans content ranging from 0.7 to 2.9% of grain dry weight. There is no evidence of strong genotype-by-environment interaction, and therefore breeding approaches could be carried out to increase fructans content. For this purpose, the first step is to analyze the genetic variability in current CIMMYT lines to have an idea the available genetic variability. This step also involves developing/validating a high-throughput methodology to quickly quantify grain fructans at a low cost, which is necessary when analyzing large numbers of lines developed by the breeding program.

Work expectations:

The candidate will be in charge of developing, testing and validating different high-throughput protocols to analyze wheat grain fructans and to screen for fructans concentration in different sets of wheat samples, in order to have an idea of the genetic variability for this trait in CIMMYT germplasm. Those sets will include different wheat lines grown in different environments to analyze environmental and GxE effects on fructans concentration. The student will be responsible for analyzing the results and publishing them in an SCI journal.

Required skills:

Ability to work with chemical/biochemical methodologies in the laboratory.

Supervisor:

Carlos Guzman and Velu Govindan; Global Wheat Program, C.Guzman@cgiar.org, velu@cgiar.org

Genotype x environment x management (GxExM) interactions in wheat mega-environments in India

Brief description of the topic:

Challenges related to natural resource degradation, climate change and their projected impact on food security are the major concerns for humanity in the densely populated Indian subcontinent. Sustainable intensification practices based on conservation agriculture (CA) have demonstrated potential to address many of these challenges and are being advocated to ensure sustainable food security in the future. In general, high yielding crop varieties are developed and evaluated under conventional agronomic management practices. However, varieties developed and tested under conventional agronomic practices may perform differently under conservation agriculture practices in a specific mega-environment. Therefore, development of high yielding, resource efficient, climate-smart varieties when combined with improved agronomic management, especially CA-based cropping system optimization, may minimize yield gaps while addressing natural resource challenges. Therefore, it is imperative to capture G x E x M interactions in major mega-environments to define recommendation domains.

Work expectations:

• Prepare a minimum dataset template for analyzing G x E x M interactions.

• Collect and analyze weather and soil data of major wheat mega-environments in India.

Collect and manage G x E x M data of CIMMYT wheat trials conducted during the past 3-4 years.

Collect additional/missing data from ongoing trials.

• Analyze G x E x M datasets and develop at least one research article for a high impact journal.

Required skills:

Good knowledge of spoken and written English. Ability to work in the field on data collection, compilation and management. Strong skills in basic applications, statistical tools and techniques, and data analysis. Candidates with knowledge of GIS are encouraged to apply.

Supervisor:

M.L. Jat; Sustainbale Intensification Program, M.Jat@cgiar.org

Efficiency of physiological tools in tropical maize breeding for yield potential and stress tolerance

Brief description of the topic:

This research topic would investigate the selection efficiency of data derived from proximal and remote sensing (indirect selection) versus direct selection for grain yield in a range of environments (optimal, drought and low nitrogen stress). The student would assist in the collection of field data over one season and have access to previous data to allow her/him to determine if any data from proximal or remote sensing tools combined with grain yield could increase selection efficiency in tropical maize breeding programs compared to selecting for grain yield alone.

Work expectations:

The candidate is expected to be strong in data analysis and able to use selection indices to compare probable gains obtained through indirect and direct selection for grain yield. A solid understanding of breeding and quantitative genetics is required, along with an understanding of stress physiology. The data available to the student are sufficient for publication in a peer-reviewed journal; therefore, he/she would be expected to develop a publication based on the results.

Required skills:

Knowledge of statistical analysis and plant breeding.

Supervisor:

Mainassara Zaman-Allah and Jill Cairns; Global Maize Program, Z.MainassaraAbdou@cgiar.org ; J.Cairns@cgiar.org

Genetic characterization of varieties in farmers’ fields across wheat growing regions in Afghanistan

Brief description of the topic:

As the third largest center of origin of domesticated crops worldwide, Afghanistan has played an important role in the domestication of wheat. However, most germplasm collections maintained in the country have been lost due to frequent armed conflicts and other factors. The Afghanistan government, in collaboration with CIMMYT, is now making significant efforts to house and inventory wheat varieties that are grown across the country. Recently, samples of 700 cultivars growing in farmers’ fields across Afghanistan’s wheat producing regions have been collected. In addition, a reference set of 1200 cultivars including cultivars being grown and genebank accessions have been selected. Detailed genetic characterization of this large collection will be performed using genotyping-by-sequencing (GBS) technology. GBS has the advantage of providing a robust diversity estimate with a much reduced ascertainment bias in comparison to other whole-genome genotyping technologies. The varieties will also be genotyped with gene-based markers linked to specific genes (wheat gluten subunits, plant height genes, kernel color etc.) to characterize their functional diversity. Genomic data will be used to evaluate the genetic diversity, identity and distribution of the 700 cultivars currently grown in farmers’ fields in comparison to the obtained reference set.

Work expectations:

The candidate is expected to analyze genotyping data for genetic diversity and variety identification under the supervision of a CIMMYT scientist. The candidate is also expected to develop a draft manuscript to report some of the results in an international journal of repute with CIMMYT co-authorship.

Required skills:

Basic knowledge of diversity analysis software; basic knowledge of R.

Supervisor:

Susanne Dreisigacker and Deepmala Sehgal; Global Wheat Program, S.Dreisigacker@CGIAR.ORG; D.Sehgal@cgiar.org

Genome diversity and signature of selection in global bread wheat program using genotyping-by-sequencing data

Brief description of the topic:

The CIMMYT Global Wheat Program (GWP) develops large numbers of elite wheat lines that are distributed to national partners. The best lines are directly released or used as parents in the development of cultivars worldwide. As germplasm undergoes years of intense selection, some regions of the genome become fixed and show limited diversity, while other regions maintain diversity. The major objective of this study is to understand the genomic diversity of GWP germplasm characterized using high density genotyping-by-sequencing (GBS) data. More detailed objectives of this study are to: (1) study the genetic diversity of the breeding germplasm using all the genomic data (“global” diversity) versus genome-specific data (markers specific to the “A”, “B” or “D” genomes); (2) evaluate region-specific variation (“local” diversity in terms of haplotypes) in surrounding regions of the genome that contributed to the Green Revolution (disease resistance, plant height, photoperiod) or other important genes; and (3) understand the signatures of selection. CIMMYT’s wheat molecular breeding unit has already genotyped more than 20,000 individuals using GBS and single nucleotide polymorphism (SNP) markers. In addition, these individuals have comprehensive pedigree and phenotypic information. By looking at these patterns across the genome, it may be possible to predict candidates or parents for new cross combinations that could lead to high yielding cultivars. The comprehensive genomic profile could also help to increase allelic diversity in target genomic regions. Thus, this project will help ongoing conventional and hybrid breeding programs develop new strategies.

Work expectations:

The candidate is expected to collaborate with the GWP and other relevant programs. CIMMYT already has a comprehensive collection of pedigree, phenotypic and marker data. The candidate’s major activities will be to analyze the data, write reports and publish manuscripts.

Required skills:

a. A basic understanding of plant breeding and molecular markers.

b. The candidate should have basic skills in any of the programming languages, but R, Python or Perl are desirable. By working with the supervisor(s), the candidate will have the opportunity of advancing his/her programming skills.

c. Good English writing and speaking skills.

Supervisor:

Umesh ROSYARA and Susanne DREISIGACKER; Global Wheat Program, u.rosyara@cgiar.org; S.Dreisigacker@CGIAR.ORG

Evaluation of the yield impact of 50% male-sterile hybrids in low yield environments and their potential value for smallholders in Africa

Brief description of the topic:

We are evaluating the yield impact of 50% male-sterile hybrids in low yield environments to assess the potential value of male sterility gene 44 or Ms44 for smallholders in Africa. We hypothesize that the gene will provide a 3 to 5% yield advantage under drought and/or low nitrogen conditions commonly faced by smallholders. The gene is being provided on a royalty free basis by DuPont-Pioneer to benefit resource poor smallholders in Africa. The male sterility allele is dominant and will be delivered using Seed Production Technology (SPT), also being provided by DuPont-Pioneer. Research will be conducted both on station and on farm. The student will work with researchers at CIMMYT, KALRO, ARC, and Pioneer in Kenya, Zimbabwe and South Africa. The student will also work closely with CIMMYT social scientists to obtain gender-disaggregated farmer preference data.

Work expectations:

The student will be involved in preparing and planting yield trials, collecting data, analyzing trial data, writing up and publishing results. We anticipate that the student would be first author of a publication describing the impact of Ms44 50% hybrids under smallholder farmer conditions in eastern and southern Africa. The student would also contribute to a separate manuscript describing farmer preferences regarding the 50% male-sterile hybrid concept.

Required skills:

An understanding of genetics and plant biology.

Supervisor:

Mike Olsen and Jill Cairns; Global Maize Program, M.Olsen@cgiar.org; J.Cairns@cgiar.org

Efficacy and equivalency evaluation of MLN conversions of elite CIMMYT maize lines

Brief description of the topic:

Maize lethal necrosis (MLN) is an important disease in Eastern Africa, significantly impacting maize production and food security of smallholders in the region. CIMMYT has introgressed multiple QTL alleles into various elite inbred lines in an effort to improve performance of these lines when infected with MLN. The research proposed would evaluate the efficacy of the converted lines in hybrid combinations for yield under MLN pressure, as well as assess the equivalency of the conversions with the original recurrent parents for various economically important traits including yield under drought, low N, and optimal conditions. The work will be conducted primarily in Kenya, with some evaluation in Uganda and/or Tanzania. The project will also involve assessing the effect size of individual QTLs as part of a broad validation and prioritization strategy.

Work expectations:

The student will be involved in preparing and planting yield trials, collecting data, analyzing trial data, and writing up and publishing results. We anticipate that the student would be first author of a publication focusing on the efficacy and equivalency evaluation of the best inbred conversions and would also contribute significantly to a second manuscript on validation of QTLs for MLN.

Required skills:

An understanding of genetics and plant biology; introduction to plant pathology preferable.

Supervisor:

Mike Olsen and Manje Gowda; Global Maize Program, M.Olsen@cgiar.org; M.Gowda@cgiar.org

Heat shock tolerance mechanisms in wheat

Brief description of the topic:

The negative effects of heat shocks (short periods of 1–4 consecutive days during which maximum temperature increases 2-4°C) on wheat yields during the grain-filling period are expected to become more common as a consequence of climate change. Heat shock can reduce grain weight and, sometimes, grain number, if it occurs before or soon after flowering, causing serious damage to wheat quality. Modeling studies by Asseng et al. (2011) suggested a yield reduction of ~0.2 t/ha for each such day, because of an assumed dramatic acceleration of leaf senescence in proportion to the number of such shocks. These effects should be influenced by the level of soil water and the previous acclimation of the wheat plants. The present study aims to identify physiological mechanisms associated with heat shock tolerance in a range of environments (drought, irrigation and heat) and the ability of different wheat genotypes to recover from brief ‘heat shock’ events.

Work expectations:

The candidate will conduct research at CIMMYT’s experiment station in the Yaqui Valley (Northwest Mexico, Ciudad Obregon) during a period of 5 months (from February to June). The candidate will spend an additional month (July) at CIMMYT headquarters (in Texcoco) working with the data and statistical analysis for the publication.

During the period in Cd. Obregon, the candidate will work with the same genotypes under drought, irrigated and heat (late sowing) conditions. Two different heat shocks will be induced at heading stage and ten days after anthesis using specially designed tents in all three environments. Physiological characterization before, during and after heat shock will be conducted in the control and treated plots.

Required skills:

The candidate should be able to work in the field under high temperature conditions (up to 40°C) and have basic knowledge of statistical analysis.

Supervisor:

Gemma Molero; Global Wheat Program g.molero@cgiar.org

The role of stomatal conductance and leaf porosity for maximizing radiation use efficiency of wheat in irrigated and heat environments

Brief description of the topic:

It is predicted that future increases in yield potential will rely largely on improved biomass production boosted by higher radiation use efficiency (RUE).

Major improvement in photosynthetic capacity and/or efficiency will be required to maximize RUE. One strategy for increasing photosynthetic capacity is to increase canopy photosynthesis, including photosynthesis in leaves situated at different strata of the canopy. However, relatively little is known about genetic diversity for the photosynthetic rate of leaves below the flag leaf and their contribution to final biomass, probably due to the amount of time needed to make these measurements.

A useful proxy for measuring photosynthesis is to measure stomatal conductance using a porometer. In previous studies, a positive correlation between yield and stomatal conductance in the flag leaf was observed. To date, information regarding stomatal conductance and stomatal density at the different canopy strata and their regulation in response to environment in wheat is scarce. Given their importance, a clear opportunity exists to genetically improve crops for stomatal behavior through increased understanding of their distribution along the canopy, their response to environment, and through exploitation of genetic diversity.

Stomatal conductance and leaf porosity will be studied along the canopy strata with the goal of identifying genetic variation associated with radiation use efficiency in different environments (irrigated and heat) where increasing RUE is a target for increasing yield in wheat.

Work expectations:

The candidate will conduct research at CIMMYT’s experiment station in the Yaqui Valley (Northwest Mexico, Ciudad Obregon) during a period of 5 months (from February to June). The candidate will spend an additional month (July) at CIMMYT headquarters (in Texcoco) working with the data and performing statistical analysis for publication.

During the period in Cd. Obregon, the candidate will work with genotypes that have contrasting canopy architecture under irrigated and heat (late sowing) conditions. Stomatal conductance and leaf porosity will be measured at booting initiation and during grain-filling at different canopy strata (flag leaf, leaf 2, leaf 3 and leaf 4). These measurements will be included in growth and RUE analysis.

Required skills:

The candidate must be able to work in the field under high temperature conditions (up to 40°C) and have a basic knowledge of statistical analysis.

Supervisor:

Gemma Molero; Global Wheat Program g.molero@cgiar.org

Development of hybrid wheat technology

Brief description of the topic:

CIMMYT is working on hybrid wheat breeding and genetics in order to develop efficient and economically viable hybrid wheat technology for the future. We are exploring potential heterosis in CIMMYT wheat germplasm and trying to define/develop possible heterotic patterns. Our key research area includes, but is not limited to, understanding desirable male and female floral structures and their genetic control; understanding the genetic basis of male sterility/fertility (cytoplasmic-genetic interaction) in wheat; optimizing male and female breeding strategies; applying speed breeding/rapid cycling to develop male and female lines; and optimizing hybrid seed production agronomy.

Work expectations:

Depending on the candidate’s research interest, he/she can get involved in any of the research areas outlined above. Based on the available timeframe, a specific work plan can be developed to accomplish academically sound and publishable studies. The candidate is expected to become involved in designing and conducting experiments, collecting and analyzing data, and preparing manuscripts. Research facilities and other logistics will be provided. Apart from his/her specific thesis research, the candidate will get substantial exposure to ongoing research activities in the hybrid wheat program. Moreover, the candidate is encouraged to have cross-disciplinary interaction with other scientists and become familiar with their programs/activities within CIMMYT.

Required skills:

Plant biology/genetics/genomics, statistical analyses.

Supervisor:

Dr. Bhoja Raj Basnet; Global Wheat Program, B.R.Basnet@cgiar.org

Modelling farming systems adaptation to Climate Change

Brief description of the topic:

In recent years, several technological and policy options have been developed to build adaptable and resilient farming systems (Jat et al. 2016). In South Asia, conservation agriculture based practices for improved productivity and resilience, and more efficient use of water and nutrients, have been developed and tested at field level in different agro-ecologies (e.g. Jat et al. 2014).

The objective of this PhD is to assess the suitability and contribution of such practices, through quantitative farming systems analysis (QFSA), to the sustainability, adaptability and resilience of diverse cereal-based farming systems in South Asia.

CIMMYT, in collaboration with several national and international partners (ICAR-India, WUR, ILRI, CIRAD-France) is currently developing and applying several QFSA tools (Groot et al. 2012, Berre et al. 2016, Frelat et al. 2016, Kalawantawanit, 2016, Lopez-Ridaura et al. 2016) and the PhD research will contribute to their further development and scale application in the context of cereal-based systems in South Asia.

Quantitative farming systems analysis (QFSA) can provide useful tools to better fit options to diverse farming systems and assess (ex-post and ex-ante) the contribution of such options to their sustainability. QFSA also allows the exploration of different scenarios and pathways for adaptation of rural households as well as the quantitative analysis of main trade-offs and synergies emerging in promotion of specific alternatives.

Work expectations:

The student will i) identify the most suitable models and tools to assess the contribution of conservation agriculture practices to the sustainability of farming systems in South Asia, ii) gather data through field measurements and farm surveys to parametrize the selected model(s) for specific sites and iii) develop additional modules/models to better represent processes related conservation agriculture and the resilience and adaptation of farming systems to climate change.

The student will, through the application of the QFSA tools, identify main recommendation domains for different technological options and understand the underpinning processes limiting the suitability of specific options to diverse farming systems.

The candidate will be based in the CIMMYT offices of New Delhi with short duration stays in i) field stations in India and ii) CIMMYT HQ in Mexico and Wageningen University in the Netherlands.

Discipline:

  • Agronomy – Farming Systems Analysis.
  • Theme: Conservation agriculture and nutrient management
  • Desirable level: PhD, PostDoc, V.Sc.
  • Post-doc in collaboration with Wageningen University.
  • Specific or special skills required:
  • Quantitative systems analysis, modeling.
  • Duration: 24 months

Required skills:

Plant biology/genetics/genomics, statistical analyses.

Supervisor:

M.L. Jat (CIMMYT-India), Sustainable Intensification Program, m.jat@cgair.org

Santiago Lopez-Ridaura (CIMMYT-Mexico), Sustainable Intensification Program, s.l.ridaura@cgiar.org

Jeroen Groot (Farming Systems Ecology –WUR)

Youth Engagement with the Rural Economy in Africa

Brief description of the topic:

We are using LSMS-ISA panel data for 6 countries in sub-Saharan Africa to examine patterns of economic engagement in rural economies by young people. Questions of particular interest are: How do young people engage with land and labor markets? What role does migration play in land and labor market participation?

Work expectations:

The student will work closely with other project researchers. In addition to primary data analysis, the student may contribute to secondary data analysis (e.g. literature review) and writing up of results.

Required skills:

Household survey data analysis, econometrics.

Supervisor:

Jordan Chamberlin, Socio Economics Program, Ethiopia j.chamberlin@cgiar.org

Spatial Analysis of Fertilizer Profitability

Brief description of the topic:

We are using LSMS-ISA panel data for 6 countries in sub-Saharan Africa to examine patterns of economic engagement in rural economies by young people. Questions of particular interest are: How do young people engage with land and labor markets? What role does migration play in land and labor market participation?

Work expectations:

The student will work closely with other project researchers. In addition to primary data analysis, the student may contribute to secondary data analysis (e.g. literature review) and writing up of results.

Required skills:

Agronomy background, knowledge of the QUEFTS model, programming skills in the R language

Supervisor:

Jordan Chamberlin, Socio Economics Program, Ethiopia j.chamberlin@cgiar.org

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