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Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties. [ABSTRACT FROM AUTHOR]

The cultivation of pearl millet in India is experiencing important transformations. Here, we propose a new characterization of the pearl millet production environment using the latest available district level data (1998–2017), principal component analysis, and large-scale crop model simulations. Pearl millet cultivation environment can be divided in up to five environments (TPEs). The eastern part of the country (Rajasthan, Haryana, Uttar Pradesh) emerges as the only region where pearl millet cultivation has grown (+0.4 Kha/year), with important yield increase (+51 kg/ha/year), and potential surplus that are likely exported. Important reductions of pearl millet cultivated area in Gujarat (−4.5 Kha/year), Maharashtra and Karnataka (−4 Kha/year) are potentially due to economy-driven transition to other more profitable crops, such as cotton or maize. The potential rain increase could also accelerate this transition. With R 2 ∈ [0.15–0.61], the tested crop models reflected reasonably well the pearl millet production system in the A1 (North Radjasthan) and AE1 (South Rajastan and Haryana) TPEs covering the largest area (66%) and production share (59%), especially after the use of a new strategy for environment and management parameters calibration. Those results set the base for in silico system design and optimization in future climatic scenarios. [ABSTRACT FROM AUTHOR]

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP. [ABSTRACT FROM AUTHOR]

The article presents a research focusing on the effect of social instability on social reaction to deviance using the results of the 2004 vote on the lifelong detention initiative in Switzerland. It shows that at the macro-social level, social instability reinforces the positive relationship between xenophobic attitudes and support for lifelong detention. It states that the social instability would lead to a situation in which norms and values are contested.

• Genotype by Environment by Management (GxExM) interactions hampers crop improvement. • Crop simulation revealed large/variable yield gaps (∼70 %). • Filling 80 % of yield gap would increase production by 8 M tons. • Modelling output and biophysical traits were clustered (HPU). • Grouping by HPU reduced GxExM interactions and will help refine breeding targets. The high degree of Genotype by Environment by Management (GxExM) interactions is a serious challenge for production and crop improvement efforts. This challenge is especially true for a crop like groundnut that is often grown as a rainfed crop in diverse environments and management, leading to considerable production fluctuations among regions and seasons. Developing a means to characterize the drivers of variable yield and to identify region specific breeding objectives were the main motivations for this research, using groundnut production in India, as a case study for rainfed crops. Historically, five groundnut production areas have been considered by Indian crop improvement programs. Our objectives were to assess the relevance of this zonation system and possibly to re-define production areas with a higher degree of similarities into homogeneous production units (HPUs). Towards this, we used yield gap analysis and the geo-biophysical characters of the production region to understand and deal with GxExM interactions. Weather and soil data, crop parameters, and management information data were collected and groundnut production was simulated at the district scale over 30 consecutive years. Consequently, the geographic distribution of the potential yields and the yield gaps were first estimated to understand the main production limitations in a given region. Large and variable yield gaps (with a mean of ∼70 %) were observed and results revealed a readily exploitable production gap (∼ 8 M tons), which might be bridged by following recommended agronomic practices. Water deficit limited the yield potential by an average of 40 %, although with large variability among districts. However, large and variable yield gaps remained. To resolve the unexplained variation, principal component and cluster analysis of agronomic model output together with geo-biophysical indicators for each district were carried out. This resulted in seven HPUs, having well-defined production-limiting constraints. Grouping by HPU greatly reduced variance in actual and simulated yields, as compared to grouping across all groundnut production zones in India. The HPU based approach delimited precise geographic regions within which HPU-specific GxM products could be designed by crop improvement programs to boost productivity. [ABSTRACT FROM AUTHOR]