Pearl millet is highly resilient to some of the driest areas of the world, like the Sahel area or fringes of the Thar desert in India. Despite this, there is a wealth of variation in pearl millet genotypes for their adaptation to drought and the object of this paper was to review some related work in the past 25 years to harness these capacities toward the breeding of better adapted cultivars. Work on short duration cultivars has been a major effort. Pearl millet has also some development plasticity thanks to a high tillering ability, which allows compensating for possible drought-related failure of the main culm under intermittent drought. The development of molecular tools for breeding has made great progress in the last 10–15 years and markers, maps, EST libraries, BACs are now available and a number of quantitative trait loci (QTLs) for different traits, including drought, have been identified. Most of the work on drought has focused on the drought tolerance index (DTI), an index that reflect the genetic differences in drought adaptation that are independent of flowering time and yield potential. The DTI is closely associated to the panicle harvest index (PNHI), a trait that relates to a better grain setting and grain filling capacity. Initial work on the DTI involved empirical breeding and selection based on PNHI. A QTL for PNHI has then been identified and introgressed by marker-assisted backcrossing. More recently, a thorough dissection of that QTL has been carried out and shows that high PNHI is related to the constitutive ability of tolerant lines to save water (lower leaf conductance and sensitivity of transpiration to high vapor pressure deficit) at a vegetative stage and use it for the grain filling period. However, there is no contribution of root traits in this QTL. Current work is taking place to map these water saving traits, understand their genetic interactions, and design ideotypes having specific genetic make-up toward adaptation to specific rainfall environments.

Post-rainy sorghum (Sorghum bicolor (L.) Moench) production underpins the livelihood of millions in the semiarid tropics, where the crop is affected by drought. Drought scenarios have been classified and quantified using crop simulation. In this report, variation in traits that hypothetically contribute to drought adaptation (plant growth dynamics, canopy and root water conducting capacity, drought stress responses) were virtually introgressed into the most common post-rainy sorghum genotype, and the influence of these traits on plant growth, development, and grain and stover yield were simulated across different scenarios. Limited transpiration rates under high vapour pressure deficit had the highest positive effect on production, especially combined with enhanced water extraction capacity at the root level. Variability in leaf development (smaller canopy size, later plant vigour or increased leaf appearance rate) also increased grain yield under severe drought, although it caused a stover yield trade-off under milder stress. Although the leaf development response to soil drying varied, this trait had only a modest benefit on crop production across all stress scenarios. Closer dissection of the model outputs showed that under water limitation, grain yield was largely determined by the amount of water availability after anthesis, and this relationship became closer with stress severity. All traits investigated increased water availability after anthesis and caused a delay in leaf senescence and led to a ‘stay-green’ phenotype. In conclusion, we showed that breeding success remained highly probabilistic; maximum resilience and economic benefits depended on drought frequency. Maximum potential could be explored by specific combinations of traits.

Terminal drought stress leads to substantial annual yield losses in chickpea (Cicer arietinum L.). Adaptation to water limitation is a matter of matching water supply to water demand by the crop. Therefore, harnessing the genetics of traits contributing to plant water use, i.e. transpiration rate and canopy development dynamics, is important to design crop ideotypes suited to a varying range of water limited environments. With an aim of identifying genomic regions for plant vigour (growth and canopy size) and canopy conductance traits, 232 recombinant inbred lines derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under well-watered conditions using a high throughput phenotyping platform (LeasyScan). Twenty one major quantitative trait loci (M-QTLs) were identified for plant vigour and canopy conductance traits using an ultra-high density bin map. Plant vigour traits had 13 M-QTLs on CaLG04, with favourable alleles from high vigour parent ICC 4958. Most of them co-mapped with a previously fine mapped major drought tolerance “QTL-hotspot” region on CaLG04. One M-QTL was found for canopy conductance on CaLG03 with the ultra-high density bin map. Comparative analysis of the QTLs found across different density genetic maps revealed that QTL size reduced considerably and % of phenotypic variation increased as marker density increased. Earlier reported drought tolerance hotspot is a vigour locus. The fact that canopy conductance traits, i.e. the other important determinant of plant water use, mapped on CaLG03 provides an opportunity to manipulate these loci to tailor recombinants having low/high transpiration rate and plant vigour, fitted to specific drought stress scenarios in chickpea.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R² ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R² = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Water availability is a major constraint for crop production in semiarid environments. The impact of tillage practices on water potential gradient, water transfer resistance, yield, and water use efficiency (WUE_g) of spring wheat was determined on the western Loess Plateau. Six tillage practices implemented in 2001 and their effects were determined in 2016 and 2017 including conventional tillage with no straw (T), no-till with straw cover (NTS), no-till with no straw (NT), conventional tillage with straw incorporated (TS), conventional tillage with plastic mulch (TP), and no-till with plastic mulch (NTP). No-till with straw cover, TP, and NTP significantly improved soil water potential at the seedling stage by 42, 47, and 57%, respectively; root water potential at the seedling stage by 34, 35, and 51%, respectively; leaf water potential at the seedling stage by 37, 48, and 42%, respectively; tillering stage by 21, 24, and 30%, respectively; jointing stage by 28, 32, and 36%, respectively; and flowering stage by 10, 26, and 16%, respectively, compared to T. These treatments also significantly reduced the soil–leaf water potential gradient at the 0–10 cm soil depth at the seedling stage by 35, 48, and 35%, respectively, and at the 30–50 cm soil depth at flowering by 62, 46, and 65%, respectively, compared to T. Thus, NTS, TP, and NTP reduced soil–leaf water transfer resistance and enhanced transpiration. Compared to T, the NTS, TP, and NTP practices increased biomass yield by 18, 36, and 40%; grain yield by 28, 22, and 24%; and WUE_g by 24, 26, and 24%, respectively. These results demonstrate that no-till with straw mulch and plastic mulching with either no-till or conventional tillage decrease the soil–leaf water potential gradient and soil–leaf water transfer resistance and enhance sustainable intensification of wheat production in semi-arid areas.

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.