Mutations in the dwarf3 gene confer height stability in sorghum.

Dwarfism is a useful trait in many crop plants because it contributes to improved lodging resistance and harvest index. The mutant allele dw3-ref (dwarf3-reference) of sorghum [Sorghum bicolor (L.) Moench] is characterized by an 882 bp tandem duplication in the fifth exon of the gene that is unstable and reverts to wild-type at a frequency greater than 0.001 in many genetic backgrounds. The goal of this research was to identify stable alleles of dw3 (dwarf3) that could be backcrossed into elite parent lines to improve height stability of the crop. To discover new alleles of dw3, a panel consisting mostly of sorghum conversion lines (SC-lines) was screened by polymerase chain reaction for the 882 bp tandem duplication in the fifth exon of dw3-ref. Sanger sequencing was used to characterize the DNA sequence of this fragment in genotypes that did not contain the 882 bp tandem duplication. Sequence analysis identified three indel mutations, including an 82 bp deletion, a 6 bp duplication, and a 15 bp deletion in this region of the gene. Field trials of the donor genotypes with these new alleles indicated no wild-type revertants of dw3-sd3 (dwarf3-stable dwarf), dw3-sd4, and dw3-sd5. These alleles were backcrossed into Tx430. Field trials of backcross progeny (BC2F4) with the dw3-sd3, dw3-sd4, and dw3-sd5 alleles indicated no revertants. The plant height and flowering time characteristics of the backcross progeny were similar or slightly shorter and earlier than the recurrent parent. These findings demonstrate that dw3-sd3, dw3-sd4, and dw3-sd5 alleles will be useful in breeding for the stable dwarf trait.

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Rapid determination of protein digestibility in sorghum before and after cooking.

Quantifying the digestibility of proteins in cereal grain is important for assessing and improving the nutritional quality of the grain after ingestion. This trait is particularly important for sorghum since the grain protein is known to be less digestible after wet cooking compared to other cereals. The reduced digestibility contributes to malnutrition in regions where sorghum is consumed as a staple food. We describe here a modified, high-throughput protocol to quantify pepsin-digestible proteins in sorghum grain before and after cooking. The protocol includes three basic steps: •grinding and cooking the sorghum into a small porridge for 20 min,•digesting the porridge with pepsin for at least 2 h,•extracting and assaying the protein extract. This method closely resembles the reality of sorghum usage as food and feed, can be scaled to process large numbers of samples and can be adapted for use with other cereal crops. This protocol requires only basic lab equipment and expertise, and one person can easily process 280 samples (140 accessions) in 7-8 h.

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

Crop improvement influences on water quantity and quality processes in an agricultural watershed.

Field crop traits have and are experiencing significant changes due to genetic and agronomic improvements. How these changes affect regional water quantity and quality processes has not been clarified. The St. Joseph River Watershed (SJRW) located in the U.S. Corn Belt was selected as a case study area. Crop (corn and soybean) trait improvements in the past decades were reviewed and summarized and include changes of growing degree days (GDD), leaf area index (LAI), light utilization (LU), drought tolerance (DT), nutrient content (NC), and harvest index (HI). Based on a calibrated 9-year (from 2011 to 2019) SWAT (Soil and Water Assessment Tool) simulation in SJRW, sensitivities of the above crop traits to yield, ETa, stream flow, tile flow, surface runoff, and nutrient loads (NO3N, TN, soluble-P, and TP) were analyzed. Crop traits and their corresponding SWAT parameters for the 2010s were obtained from model calibration and used as the baseline/current scenario; for the 1980s, they were summarized from literature review and used as an historical scenario, while those for the 2040s were determined by assuming crop traits are changing linearly with time and projected as the future scenario. Water quantity and quality changes under the historical and future crop scenarios were compared with the baseline/current simulation. Results showed LU and DT were the most sensitive crop traits to water quantity (i.e., ETa, stream flow, tile flow, and surface runoff), while HI was the most sensitive to nutrient loads. The impacts of crop improvements on nutrient loads were more significant than on water budgets. Compared with the baseline, the historical and future scenarios resulted in 1.5 - 2.0% changes of stream flow, 6.8 - 18.6% changes of nitrogen loads (NO3N and TN) and 2.6 - 3.9% changes of phosphorus loads (soluble-P, and TP) in the stream flow, annually. Moreover, in certain months, these changes can reach about 12% for stream flow, 42% for nitrogen loads, and 12% for phosphorus loads. Nitrogen losses by tile drainage and percolation, and phosphorus losses by surface runoff and tile drainage were most significantly affected by the crop improvements. Future work should consider expected crop improvements when studying long-term hydrology and nutrient cycles in agricultural watersheds.

Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling.

Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.

Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum.

Cyanogenic glucosides (CGs) play a key role in host-plant defense to insect feeding; however, the metabolic tradeoffs between synthesis of CGs and plant growth are not well understood. In this study, genetic mutants coupled with nondestructive phenotyping techniques were used to study the impact of the CG dhurrin on fall armyworm [Spodoptera frugiperda (J.E. Smith)] (FAW) feeding and plant growth in sorghum [Sorghum bicolor (L.) Moench]. A genetic mutation in CYP79A1 gene that disrupts dhurrin biosynthesis was used to develop sets of near-isogenic lines (NILs) with contrasting dhurrin contents in the Tx623 bmr6 genetic background. The NILs were evaluated for differences in plant growth and FAW feeding damage in replicated greenhouse and field trials. Greenhouse studies showed that dhurrin-free Tx623 bmr6 cyp79a1 plants grew more quickly than wild-type plants but were more susceptible to insect feeding based on changes in green plant area (GPA), total leaf area, and total dry weight over time. The NILs exhibited similar patterns of growth in field trials with significant differences in leaf area and dry weight of dhurrin-free plants between the infested and non-infested treatments. Taken together, these studies reveal a significant metabolic tradeoff between CG biosynthesis and plant growth in sorghum seedlings. Disruption of dhurrin biosynthesis produces plants with higher growth rates than wild-type plants but these plants have greater susceptibility to FAW feeding.

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data.

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R 2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography.

BACKGROUND: Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). RESULTS: Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. CONCLUSIONS: The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Heterotic Patterns of Temperate and Tropical Maize by Ear Photometry.

As the plant variety protection (PVP) of commercial inbred lines expire, public breeding programs gain a wealth of genetic materials that have undergone many years of intense selection; however, the value of these inbred lines is only fully realized when they have been well characterized and are used in hybrid combinations. Additionally, while yield is the primary trait by which hybrids are evaluated, new phenotyping technologies, such as ear photometry (EP), may provide an assessment of yield components that can be scaled to breeding programs. The objective of this experiment was to use EP to describe the testcross performance of inbred lines from temperate and tropical origins. We evaluated the performance of 298 public and ex-PVP inbred lines and 274 Drought Tolerant Maize for Africa (DTMA) inbred lines when crossed to Iodent (PHP02) and/or Stiff Stalk (2FACC) testers for 25 yield-related traits. Kernel weight, kernels per ear, and grain yield predicted by EP were correlated with their reference traits with r = 0.49, r = 0.88, and r = 0.75, respectively. The testcross performance of each maize inbred line was tester dependent. When lines were crossed to a tester within the heterotic group, many yield components related to ear size and kernels per ear were significantly reduced, but kernel size was rarely impacted. Thus, the effect of heterosis was more noticeable on traits that increased kernels per ear rather than kernel size. Hybrids of DTMA inbred lines crossed to PHP02 exhibited phenotypes similar to testcrosses of Stiff Stalk and Non-Stiff Stalk heterotic groups for yield due to significant increases in kernel size to compensate for a reduction in kernels per ear. Kernels per ear and ear length were correlated (r = 0.89 and r = 0.84, respectively) with and more heritable than yield, suggesting these traits could be useful for inbred selection.

High arabinoxylan fine structure specificity to gut bacteria driven by corn genotypes but not environment.

While gut bacteria have different abilities to utilize dietary fibers, the degree of fiber structural alignment to bacteria species is not well understood. Corn bran arabinoxylan (CAX) was used to investigate how minor polymer fine structural differences at the genotype × environment level influences the human gut microbiota. CAXs were extracted from 4 corn genotypes × 3 growing years and used in in vitro fecal fermentations. CAXs from different genotypes had varied contents of arabinose/xylose ratio (0.46-0.54), galactose (58-101 mg/g), glucuronic acid (18-32 mg/g). There was genotype- but not environment-specific differences in fine structures. After 24 h fermentation, CAX showed different acetate (71-86 mM), propionate (35-44 mM), butyrate (7-10 mM), and total short chain fatty acid (SCFA) (117-137 mM) production. SCFA profiles and gut microbiota both shifted in a genotype-specific way. In conclusion, the study reveals a very high specificity of fiber structure to gut bacteria use and SCFA production.

Genetic diversity for starch quality and alkali spreading value in sorghum.

Sorghum is an important food crop in many parts of Africa and Asia. Landraces of sorghum are known to exhibit variation in food quality traits including starch and protein content and composition. In this study, a panel of diverse sorghum breeding lines and 788 sorghum conversion (SC) lines representing the global germplasm diversity of the crop were evaluated for variation in starch quality based on alkali spreading value (ASV). A small number of genotypes with stable expression of the ASV+ phenotype across seasons were identified; mostly representing Nandyal types from India. Genetic studies showed the ASV+ phenotype was inherited as a recessive trait. Whole genome resequencing of ASV+ donor lines revealed SNPs in genes involved in starch biosynthesis. A genome wide association study (GWAS) identified a significant SNP associated with ASV near Sobic.010G273800, a starch branching enzyme I precursor, and Sobic.010G274800 and Sobic.010G275001, both annotated as glucosyltransferases. Physiochemical analyses of accessions with contrasting ASV phenotypes demonstrated an environment dependent lower starch gelatinization temperature (GT), amylose content of approximately 22%, and good gel consistency. The starch quality attributes of these lines could be valuable in food products that require good gel consistency and viscosity.

Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status.

Advancements in phenotyping techniques capable of rapidly and nondestructively detecting impacts of drought on crops are necessary to meet the 21st-century challenge of food security. Here, we describe the use of hyperspectral reflectance to predict variation in physiological and anatomical leaf traits related with water status under varying water availability in six maize (Zea mays) hybrids that differ in yield stability under drought. We also assessed relationships among traits and collections of traits with yield stability. Measurements were collected in both greenhouse and field environments, with plants exposed to different levels of water stress or to natural water availability, respectively. Leaf spectral measurements were paired with a number of physiological and anatomical reference measurements, and predictive spectral models were constructed using a partial least-squares regression approach. All traits were relatively well predicted by spectroscopic models, with external validation (i.e. by applying partial least-squares regression coefficients on a dataset distinct from the one used for calibration) goodness-of-fit (R 2 ) ranging from 0.37 to 0.89 and normalized error ranging from 12% to 21%. Correlations between reference and predicted data were statistically similar for both greenhouse and field data. Our findings highlight the capability of vegetation spectroscopy to rapidly and nondestructively identify a number of foliar functional traits affected by drought that can be used as indicators of plant water status. Although we did not detect trait coordination with yield stability in the hybrids used in this study, expanding the range of functional traits estimated by hyperspectral data can help improve trait-based breeding approaches.

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize.

Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

OBJECTIVES: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Allelochemicals targeted to balance competing selections in African agroecosystems.

Among major cereals domesticated as staple food, only sorghum has a high proportion of cultivars with condensed tannins in grain, which can trigger bitter taste perception in animals by binding to type 2 taste receptors (TAS2Rs). Here, we report the completion of uncovering of a pair of duplicate recessive genes (Tannin1 and Tannin2) underlying tannin presence. Three loss-of-function alleles from each gene were identified in non-tannin sorghum desired as palatable food. Condensed tannins effectively prevented sparrows from consuming sorghum grain. Parallel geographic distributions between tannin sorghum and Quelea quelea supported the role of tannins in fighting against this major herbivore threat. Association between geographic distributions of human TAS2R variants and tannin sorghum across Africa suggested that different causes had probably driven this bidirectional selection according to varied local herbivore threats and human taste sensitivity. Our investigation uncovered coevolution among humans, plants and environments linked by allelochemicals.