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The history of CGIAR and the development and implementation of the International Treaty on Plant Genetic Resources for Food and Agriculture (“Plant Treaty”) are closely intertwined. In accordance with the agreements that 11 CGIAR centers signed with the Plant Treaty’s Governing Body under Article 15 of the treaty, >730,000 accessions of crop, tree, and forage germplasm conserved in CGIAR genebanks are made available under the terms and conditions of the multilateral system of access and benefit sharing, and the CGIAR centers have transferred almost 4 million samples of plant genetic resources under the system. Many activities of CGIAR centers and their genebanks (e.g., crop enhancement, improved agronomic methods, seed system strengthening, and capacity building) are influenced by, and promote, the Plant Treaty’s objectives. The continued existence and optimal functioning of the Plant Treaty’s multilateral system of access and benefit sharing is critically important to CGIAR in the pursuit of its mission. However, the multilateral system has encountered some challenges since the Plant Treaty came into force. The successful conclusion of the ongoing process for enhancing the functioning of the multilateral system could increase monetary benefit sharing and incentives for exchanging more germplasm. In the meantime, increased efforts are necessary to promote nonmonetary benefit sharing through partnerships, technology transfer, information exchange, and capacity building. These efforts should be integrated into countries’ and organizations’ work to implement the Plant Treaty’s provisions on conservation and sustainable use of plant genetic resources, and farmers’ rights.

There is strong evidence of a key role for mitochondrial dysfunction in both sporadic and all forms of familial Parkinson's disease (PD). However, none of the clinical trials carried out with putative mitochondrial rescue agents has been successful. Firm establishment of a wet biomarker or a reliable readout from imaging studies detecting mitochondrial dysfunction and reflecting disease progression is also awaited. We will provide an overview of our current knowledge about mitochondrial dysfunction in PD and related drug screens. We will also summarize previously undertaken mitochondrial wet biomarker studies and relevant imaging studies with particular focus on 31P-MRI Spectroscopy. We will conclude with an overview of clinical trials which tested putative mitochondrial rescue agents in PD patients. Parkinson's disease is a common, relentlessly progressive neurodegenerative disorder. The pathological hallmark is loss of dopaminergic neurons in the substantia nigra. The resulting motor presentation includes rest tremor, bradykinesia and rigidity but the importance of non-motor symptoms such as cognitive impairment and depression is increasingly recognized, too. Currently available dopaminergic treatment often only addresses the motor impairment partially. This review will summarize our current knowledge about mitochondrial dysfunction as a key target for disease-modifying treatment for PD. We will also provide an update on mitochondrial readouts in PD patients, namely imaging and putative mitochondrial biomarkers, which may become highly relevant in the context of future drug trials. This article is protected by copyright. All rights reserved.

Genotypes produced from plant breeding programs for annual crops are evaluated in multiyear, multilocation field trials where a large number of genotypes grown in a few locations in the first year are progressively reduced by selection and tested in a greater number of locations in successive years. Provided fixed resources are specified, the efficiencies of testing strategies differing in the number of locations and replications used and in the number of genotypes tested in successive years can be evaluated by three statistics (genetic repeatability, acceptance probability, and potential gain) calculated from five variance components applicable to the field trials. The five variance components required are deployed genetic variance, genotype-by-year, genotype-by-location, and genotype-by-year-by-location interaction variances and the residual (error) variance. Strictly, known variance components are required, but the statistics and their standard errors can be calculated using robust estimates of the variance components obtained from a combined analysis over years of a large set of historical data from field trials that are connected by shared genotypes across years, as genotype-by-year and genotype-by-year-by-location variance components are estimable. Single-year analyses are unsuitable, as no estimate of the interactions with year are available, and such analyses produce biased estimates of genetic variance and genotype-by-location interaction variance. These procedures are illustrated using an example from the CIMMYT international wheat breeding program to evaluate 2-yr testing strategies for CIMMYT’s Elite Spring Wheat Yield Trials that has been distributed and evaluated internationally since 1979.

The high rainfall mega-environment [mega environment 2 (ME2)], characterized by more than 500 mm rainfall during wheat season, is one of the 12 mega-environments on which the global wheat breeding program of the International Maize and Wheat Improvement Center (CIMMYT) has been assembling and distributing high rainfall wheat yield trials (HRWYT) since 1992. In this study, our objectives were to evaluate the adaptation and performance of entries included in the 1st through the 12th HRWYT (1992-2004), identify best lines as parents for crossing and recommend for direct release by the National Agricultural Research Systems (NARS), and test the association among sites and determine yield progress in HRWYTs. Combined ANOVA across locations for grain yield showed highly significant difference among genotypes and genotype x environment interactions (p < 0.001) for each of the HRWYTs. At regional level, wide range of yield variability was observed in Africa (0.69-9.614 t ha ―1 ) followed by Asia (0.389-6.93 t ha ―1 ) with the highest mean yields in Europe (4.392 t ha ―1 ). Two widely grown mega-cultivars have been developed from Attila cross: entry number 4 (CM85836-4Y-0M-0Y-8M-0Y) from 1stHRWYT, which is released as PBW343 in India, and entry number 48 from 4thHRWYT (CM85836-50Y-0M-0Y-3M-0Y), which is released in Ethiopia, Tunisia, Iran, Pakistan, and Sudan with local names of Kubsa, Utique96, Chamran, MH97, and Imam, respectively. There is a significant positive association between plant height and grain yield in rain-fed environments whereas the association between heading dates and yield is location specific indicating the importance of selecting broad range of materials for heading date at the main station in Mexico. Correlation analysis shows that the sites giving the best prediction of global performance are Marcos Juarez (Argentina), Pergamino (Argentina), Izmir (Turkey), Bethlehem (South Africa), Sakha (Egypt), and Tassaout (Morocco). In each year, it was possible to find a line out yielding the local check in at least 52% of the sites. However, at the trial level, lines were beating the local checks more frequently in the first trials than in the last trials. Regular assessment of the current and future HRWYTs in terms of adaptation, resistance, diversity, and quality will be of paramount importance in improving the breeding process.

Cereal production is strongly infl uenced by fl owering date. Wheat (Triticum aestivum L.) models simulate days to fl ower by assuming that development is modifi ed by vernalization and photoperiodism. Cultivar differences are parameterized by vernalization requirement, photoperiod sensitivity, and earliness per se. The parameters are usually estimated by comparing simulations with fi eld observations but appear estimable from genetic information. For wheat, the Vrn and Ppd loci, which affect vernalization and photoperiodism, were logical candidates for estimating parameters in the model CSM-Cropsim-CERES. Two parameters were estimated conventionally and then re-estimated with linear effects of Vrn and Ppd. Flowering data were obtained for 29 cultivars from international nurseries and divided into calibration (14 locations) and evaluation (34 locations) sets. Simulations with a generic cultivar explained 95% of variation in fl owering for calibration data (10 d RMSE) and 89% for evaluation data (10 d RMSE), indicating the large effect of environment. Nonetheless, for the calibration data, the gene-based model explained 29% of remaining variation, and the conventional model, 54%. For the evaluation data, the gene-based model explained 17% of remaining variation, and the conventional model, 27%. Gene-based prediction of wheat phenology appears feasible, but more extensive genetic characterization of cultivars is needed.