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Although yield and total biomass produced by annual legumes remain major objectives for breeders, other issues such as environment-friendly, resource use efficiency including symbiotic performance, resilient production in the context of climate change, adaptation to sustainable cropping systems (reducing leaching, greenhouse gas emissions and pesticide residues), adaptation to diverse uses (seeds for feed, food, non-food, forage or green manure) and finally new ecological services such as pollinator protection, imply the need for definition of new ideotypes and development of innovative genotypes to enhance their commercialization. Taken as a whole, this means more complex and integrated objectives for breeders. Several illustrations will be given of breeding such complex traits for different annual legume species. Genetic diversity for root development and for the ability to establish efficient symbioses with rhizobia and mycorrhiza can contribute to better resource management (N, P, water). Shoot architectures and phenologies can contribute to yield and biotic constraint protection (parasitic weeds, diseases or insects) reducing pesticide use. Variable maturity periods and tolerance to biotic and abiotic stresses are key features for the introduction of annual legumes to low input cropping systems and for enlarging cultivated area. Adaptation to intercropping requires adapted genotypes. Improved health and nutritional value for humans are key objectives for developing new markets. Modifying product composition often requires the development of specific cultivars and sometimes the need to break negative genetic correlations with yield. A holistic approach in legume breeding is important for defining objectives with farmers, processors and consumers. The cultivar structures are likely to be more complex, combining genotypes, plant species and associated symbionts. New tools to build and evaluate them are important if legumes are to deliver their exciting potential in terms of agricultural productivity and sustainability as well as for feed and food.

Cytoplasmic-nuclear male-sterility is an important biological tool, which has been used by plant breeders to increase yields in cross-pollinated cereals and vegetables by commercial exploitation of the phenomenon of hybrid vigor. In legumes, no such example exists due to the absence of an economic way of mass pollen transfer from male to female parent. Pigeonpea [Cajanus cajan (L.) Millsp.], however, is a different legume where a moderate level of insectaided natural out-crossing (25-70%) exists and it can be used to produce commercial hybrid cultivars, if an efficient and stable cytoplasmic-nuclear male-sterility (CMS) system is available. This paper reports the development of a stable CMS system (ICP 2039A), derived from an inter-specific hybrid of Cajanus cajanifolius, a wild relative of pigeonpea, with a cultivar ICP 11501. Using this genetic material, designated as the A 4 cytoplasm, a number of fertility restorers and maintainers have been developed. The best short-duration experimental pigeonpea hybrid ICPH 2470 produced 3205 kg ha −1 grain yield in 125 days, exhibiting 77.5% advantage over the control cultivar UPAS 120. At present, all the important biological systems necessary for a successful commercial hybrid breeding program are available in pigeonpea and the package of this technology has been adopted by private seed sector in India for the production and marketing of hybrid varieties.

BackgroundFaba bean (Vicia faba L.) is an important food legume crop, grown for human consumption globally including in China, Turkey, Egypt and Ethiopia. Although genetic gain has been made through conventional selection and breeding efforts, this could be substantially improved through the application of molecular methods. For this, a set of reliable molecular markers representative of the entire genome is required.ResultsA library with 125,559 putative SSR sequences was constructed and characterized for repeat type and length from a mixed genome of 247 spring and winter sown faba bean genotypes using 454 sequencing. A suit of 28,503 primer pair sequences were designed and 150 were randomly selected for validation. Of these, 94 produced reproducible amplicons that were polymorphic among 32 faba bean genotypes selected from diverse geographical locations. The number of alleles per locus ranged from 2 to 8, the expected heterozygocities ranged from 0.0000 to 1.0000, and the observed heterozygosities ranged from 0.0908 to 0.8410. The validation by UPGMA cluster analysis of 32 genotypes based on Nei's genetic distance, showed high quality and effectiveness of those novel SSR markers developed via next generation sequencing technology.ConclusionsLarge scale SSR marker development was successfully achieved using next generation sequencing of the V. faba genome. These novel markers are valuable for constructing genetic linkage maps, future QTL mapping, and marker-assisted trait selection in faba bean breeding efforts.

Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates OPEN ACCESSRemote Sens. 2015, 7 3468 that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition.