S orghum [Sorghum bicolor (L.) Moench] is a major cereal crop grown on nearly 42 million ha worldwide for food, fodder, fiber, and fuel (FAOSTAT, 2013). It serves as a staple food crop for millions of people, predominantly in developing countries of Africa and Asia. Among the cereals, sorghum displays exceptional tolerance to heat and drought, and its complex biochemical and morphological characteristics offers an advantage of enhanced C4 carbon assimilation even at high temperatures (Paterson, 2008;Shoemaker et al., 2010). These characteristics make sorghum an attractive crop in both subsistence-and commercial-farming operations. However, a number of biotic and abiotic stresses are known An additional major QTL on chromosome 5 of the SC414-12E genome explained from 20 to 39% of the phenotypic variance and was observed in four of the six environments tested.Resequencing of the genomes of resistant cultivars SC155-14E and SC414-12E facilitated a preliminary survey of the coding regions of genes annotated as playing a role in host defense. The resequenced genomes of the resistant genotypes and the linkage mapping resources represent information relevant for more detailed molecular characterization of genes conditioning anthracnose resistance in this tropical cereal. The identification of QTL conferring anthracnose resistance and the identification of single-nucleotide polymorphisms linked to these loci will provide the necessary molecular tools for markerassisted introgression of durable anthracnose resistance into elite sorghum inbreds.

Anthracnose, caused by the fungal pathogen Colletotrichum sublineolum Henn. ex. Sacc. and Trotter 1913, is an economically damaging disease of sorghum [Sorghum bicolor (L.) Moench] in hot and humid production regions of the world. Control of anthracnose is almost exclusively through the use of genetic resistance. To further elucidate genetic resistance to anthracnose, a recombinant inbred line population derived from the cross of BTx623 (susceptible) and SC748‐5 (resistant) was created. A linkage map was constructed using 117 F5 individuals that were genotyped using Digital Genotyping, a genotyping‐by‐sequencing method developed specifically for C4 grasses, on an Illumina GAIIx. The linkage map consists of 619 single nucleotide polymorphism markers and three microsatellites with a total map length of 1269.9 cM. The population was phenotyped for anthracnose in four different environments. Using both composite interval mapping and inclusive composite interval mapping (ICIM), one major quantitative trait locus (QTL) on chromosome 5 was consistently identified as the source of anthracnose resistance in all environments. Sequencing genomic DNA from SC748‐5 and comparison to BTx623 genomic sequence revealed numerous amino acid changes in annotated disease‐resistance genes located in the area under the anthracnose QTL. This suggests that the genetic architecture for anthracnose resistance in SC748‐5 is not under the control of one gene but, more likely, a linkage block containing several resistance genes.

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High-throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high-throughput traits could lead to significant optimization of HTP-assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS-based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field-based breeding program and suggest additional optimization options. Abbreviations: AUDPC, area under the disease progress curve; CIg, chlorophyll index; CIre, red-edge chlorophyll index; DAS, days after sowing; GY, grain yield; HTP, high-throughput phenotyping; ML, machine learning; MSR, modified simple ratio; MSRre, red-edge modified simple ratio; NDVI, normalized difference vegetation index; NDVIre, red-edge normalized difference vegetation index; PLSR, partial least squares regression; UAS, unmanned aerial system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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