Breeding to Optimize Chinese Agriculture (OPTICHINA) was a three-year EU-China project launched in June of 2011. As designed, the project acted as a new strategic model to reinforce systematic cooperation on agricultural research between Europe and China. The OPTICHINA International Conference “Breeding to Optimize Agriculture in a Changing World” was held in Beijing, May 26-29, 2014. The conference included six thematic areas: (1) defining and protecting the yield potential of traits and genes; (2) high-throughput precision phenotyping in the field; (3) molecular technologies in modern breeding; (4) plant ideotype; (5) data analysis, data management, and bioinformatics; and (6) national challenges and opportunities for China. The 10 articles collected in this special issue represent key contributions and topics of this conference. This editorial provides a brief introduction to the OPTICHINA project, followed by the main scientific points of articles published in this special issue. Finally, outcomes from a brainstorming discussion at the end of the conference are summarized, representing the authors' opinions on trends in breeding for a changing world.

Intensification in rice crop production is generally understood as requiring increased use of material inputs: water, inorganic fertilizers, and agrochemicals. However, this is not the only kind of intensification available. More productive crop phenotypes, with traits such as more resistance to biotic and abiotic stresses and shorter crop cycles, are possible through modifications in the management of rice plants, soil, water, and nutrients, reducing rather than increasing material inputs. Greater factor productivity can be achieved through the application of new knowledge and more skill, and (initially) more labor, as seen from the System of Rice Intensification (SRI), whose practices are used in various combinations by as many as 10 million farmers on about 4 million hectares in over 50 countries. The highest yields achieved with these management methods have come from hybrids and improved rice varieties, confirming the importance of making genetic improvements. However, unimproved varieties are also responsive to these changes, which induce better growth and functioning of rice root systems and more abundance, diversity, and activity of beneficial soil organisms. Some of these organisms as symbiotic endophytes can affect and enhance the expression of rice plants' genetic potential as well as their phenotypic resilience to multiple stresses, including those of climate change. SRI experience and data suggest that decades of plant breeding have been selecting for the best crop genetic endowments under suboptimal growing conditions, with crowding of plants that impedes their photosynthesis and growth, flooding of rice paddies that causes roots to degenerate and forgoes benefits derived from aerobic soil organisms, and overuse of agrochemicals that adversely affect these organisms as well as soil and human health. This review paper reports evidence from research in India and Indonesia that changes in crop and water management can improve the expression of rice plants' genetic potential, thereby creating more productive and robust phenotypes from given rice genotypes. Data indicate that increased plant density does not necessarily enhance crop yield potential, as classical breeding methods suggest. Developing cultivars that can achieve their higher productivity under a wide range of plant densities—breeding for density-neutral cultivars using alternative selection strategies—will enable more effective exploitation of available crop growth resources. Density-neutral cultivars that achieve high productivity under ample environmental growth resources can also achieve optimal productivity under limited resources, where lower densities can avert crop failure due to overcrowding. This will become more important to the extent that climatic and other factors become more adverse to crop production. Focusing more on which management practices can evoke the most productive and robust phenotypes from given genotypes is important for rice breeding and improvement programs since it is phenotypes that feed our human populations.

The phenotypic diversity of 274 Ethiopian durum wheat accessions was analyzed, taking their geographic origins into account. The aim was to assess the extent and patterns of agronomically important phenotypic variation across districts of origin and altitude classes for major qualitative traits using diversity index and multivariate methods. Eight qualitative and three quantitative traits were scored for 2740 plants and analyzed for diversity. The Shannon-Weaver diversity (H′) index was used to estimate phenotypic diversity. The estimated H′ ranged from monomorphic for glume hairiness to highly polymorphic for other traits. The highest (0.86) H′ was obtained for seed degree of shriveling, possibly indicating the differential responses of the genotypes to water deficit during later growth stages. With respect to district of origin, the highest (0.72) and lowest (0.44) H′ values were obtained for the Bale and SNNP districts, respectively. With respect to altitude, the highest (0.76) and lowest (0.62) H′ values were recorded for altitudes 1600-2000 and > 3000 m above sea levels, respectively. Principal components analysis explained substantial variation contributed by district of origin and altitude range. Genotypes were clustered into three groups by districts of origin and altitude class, with relatively strong bootstrap values of 57 and 62 for the former and latter, respectively. It could be concluded that Ethiopian durum wheat landraces are very diverse both within and among districts of origin and altitude classes. This wealth of genetic diversity should be exploited for wheat improvement of yield and for resistance to biotic and abiotic stresses, particularly terminal drought.

The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010-2013 a new strain of this pathogen (Warrior/Ambition), against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity. RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R² = 0.581 and R² = 0.536, respectively), far surpassing the predictive ability of NDVI (R² = 0.118 and R² = 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.

Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.

Plant water use efficiency (WUE) is becoming a key issue in semiarid areas, where crop production relies on the use of large volumes of water. Improving WUE is necessary for securing environmental sustainability of food production in these areas. Given that climate change predictions include increases in temperature and drought in semiarid regions, improving crop WUE is mandatory for global food production. WUE is commonly measured at the leaf level, because portable equipment for measuring leaf gas exchange rates facilitates the simultaneous measurement of photosynthesis and transpiration. However, when those measurements are compared with daily integrals or whole-plant estimates of WUE, the two sometimes do not agree. Scaling up from single-leaf to whole-plant WUE was tested in grapevines in different experiments by comparison of daily integrals of instantaneous water use efficiency [ratio between CO₂ assimilation (A_N) and transpiration (E); A_N/E] with midday A_N/E measurements, showing a low correlation, being worse with increasing water stress. We sought to evaluate the importance of spatial and temporal variation in carbon and water balances at the leaf and plant levels. The leaf position (governing average light interception) in the canopy showed a marked effect on instantaneous and daily integrals of leaf WUE. Night transpiration and respiration rates were also evaluated, as well as respiration contributions to total carbon balance. Two main components were identified as filling the gap between leaf and whole plant WUE: the large effect of leaf position on daily carbon gain and water loss and the large flux of carbon losses by dark respiration. These results show that WUE evaluation among genotypes or treatments needs to be revised.

The colonization of maize (Zea mays L.) and peanut (Arachis hypogaea L.) by the fungal pathogen Aspergillus flavus results in the contamination of kernels with carcinogenic mycotoxins known as aflatoxins leading to economic losses and potential health threats to humans. The regulation of aflatoxin biosynthesis in various Aspergillus spp. has been extensively studied, and has been shown to be related to oxidative stress responses. Given that environmental stresses such as drought and heat stress result in the accumulation of reactive oxygen species (ROS) within host plant tissues, host-derived ROS may play an important role in cross-kingdom communication between host plants and A. flavus. Recent technological advances in plant breeding have provided the tools necessary to study and apply knowledge derived from metabolomic, proteomic, and transcriptomic studies in the context of productive breeding populations. Here, we review the current understanding of the potential roles of environmental stress, ROS, and aflatoxin in the interaction between A. flavus and its host plants, and the current status in molecular breeding and marker discovery for resistance to A. flavus colonization and aflatoxin contamination in maize and peanut. We will also propose future directions and a working model for continuing research efforts linking environmental stress tolerance and aflatoxin contamination resistance in maize and peanut.

Field pea (Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value to the crop. However, knowledge about genetic diversity and lipid content in field pea is limited. An understanding of genetic diversity and population structure in diverse germplasm is important and a prerequisite for genetic dissection of complex characteristics and marker-trait associations. Fifty polymorphic microsatellite markers detecting a total of 207 alleles were used to obtain information on genetic diversity, population structure and marker-trait associations. Cluster analysis was performed using UPGMA to construct a dendrogram from a pairwise similarity matrix. Pea genotypes were divided into five major clusters. A model-based population structure analysis divided the pea accessions into four groups. Percentage lipid content in 35 diverse pea accessions was used to find potential associations with the SSR markers. Markers AD73, D21, and AA5 were significantly associated with lipid content using a mixed linear model (MLM) taking population structure (Q) and relative kinship (K) into account. The results of this preliminary study suggested that the population could be used for marker-trait association mapping studies.

Superoxide dismutase (SOD, EC 1.15.1.1) plays a key role in response to drought stress, and differences in SOD activity changes among cultivars are important under drought conditions. We obtained the full-length DNA of the chloroplast Cu/Zn-SOD gene (AhCSD2) from 11 allotetraploid cultivars and 5 diploid wild species in peanut. BLAST search against the peanut genome showed that the AhCSD2 genes gCSD2-1 and gCSD2-2 are located at the tops of chromosome A03 (A genome) and B03 (B genome), respectively, and both contain 8 exons and 7 introns. Nucleotide sequence analyses indicated that gCSD2-2 sequences were identical among all the tested cultivars, while gCSD2-1 sequences showed allelic variations. The amino acid sequences deduced from gCSD2-1 and gCSD2-2 both contain a chloroplast transit peptide and are distinguished by 6 amino acid (aa) residue differences. The other 2 aa residue variations in the mature peptide regions give rise to three-dimensional structure changes of the protein deduced from the genes gCSD2-1 and gCSD2-2. Sequences analyses of cultivars and wild species showed that gCSD2-2 of Arachis hypogaea and gAipCSD2 (Arachis ipaensis) are identical, and despite the abundant polymorphic loci between gCSD2-1 of A. hypogaea and sequences from A genome wild species, the deduced amino acid sequence of AhCSD2-1 (A. hypogaea) is identical to that of AduCSD2 (Arachis duranensis), whereas AcoCSD2 (Arachis correntina) and AcaCSD2 (Arachis cardenasii) both have 2 aa differences in the transit peptide region compared with AhCSD2-1 (A. hypogaea). Based on the Peanut Genome Project, promoter prediction revealed many stress-related cis-acting elements within the potential promoter regions (pp-A and pp-B). pp-A contains more binding sites for drought-associated transcriptional factors than pp-B. We hypothesize that the marked changes in SOD activity in different cultivars under drought stress are tightly regulated by transcription factors through transcription and expression of AhCSD2 genes.

Sixteen faba bean genotypes were evaluated in 13 environments in Ethiopia during the main cropping season for three years (2009-2011). The objectives of the study were to evaluate the yield stability of the genotypes and the relative importance of different stability parameters for improving selection in faba bean. The study was conducted using a randomized complete block design with four replications. G × E interaction and yield stability were estimated using 17 different stability parameters. Pooled analysis of variance for grain yield showed that the main effects of both genotypes and environments, and the interaction effect, were highly significant (P ≤ 0.001) and (P ≤ 0.01), respectively. The environment main effect accounted for 89.27% of the total yield variation, whereas genotype and G × E interaction effects accounted for 2.12% and 3.31%, respectively. Genotypic superiority index (P_i) and FT3 were found to be very informative for selecting both high-yielding and stable faba bean genotypes. Twelve of the 17 stability parameters, including CV_i, RS, α, λ, S²d_i, b_i, S_i⁽²⁾, W_i, σ_i²,²², EV, P⁵⁹,⁵⁹⁵⁹, and ASV, were influenced simultaneously by both yield and stability. They should accordingly be used as complementary criteria to select genotypes with high yield and stability. Although none of the varieties showed consistently superior performance across all environments, the genotype EK 01024-1-2 ranked in the top third of the test entries in 61.5% of the test environments and was identified as the most stable genotype, with type I stability. EK 01024-1-2 also showed a 17.0% seed size advantage over the standard varieties and was released as a new variety in 2013 for wide production and named “Gora”. Different stability parameters explained genotypic performance differently, irrespective of yield performance. It was accordingly concluded that assessment of G × E interaction and yield stability should not be based on a single or a few stability parameters but rather on a combination of stability parameters.

QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations. Eight functionalities are integrated in this software package: (1) BIN: binning of redundant markers; (2) MAP: construction of linkage maps in biparental populations; (3) CMP: consensus map construction from multiple linkage maps sharing common markers; (4) SDL: mapping of segregation distortion loci; (5) BIP: mapping of additive, dominant, and digenic epistasis genes; (6) MET: QTL-by-environment interaction analysis; (7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and (8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL, and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci, and to perform analysis of variance for multi-environmental trials.