Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features.
Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit.
Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
Sebastien Lacube, Christian Fournier, Carine Palaffre, Emilie J Millet, Francois Tardieu, Boris Parent
Plant Cell & Environment, 2020.
Leaf expansion depends on both carbon and water availabilities. In cereals, most of experimental effort has focused on leaf elongation, with essentially hydraulic effects. We have tested if evaporative demand and light could have distinct effects on leaf elongation and widening, and if short‐term effects could translate into final leaf dimensions. For that, we have monitored leaf widening and elongation in a field experiment with temporary shading, and in a platform experiment with 15 min temporal resolution and contrasting evaporative demands. Leaf widening showed a strong (positive) sensitivity to whole‐plant intercepted light and no response to evaporative demand. Leaf elongation was (negatively) sensitive to evaporative demand, without effect of intercepted light per se. We have successfully tested resulting equations to predict leaf length and width in an external dataset of 15 field and six platform experiments. These effects also applied to a panel of 251 maize hybrids. Leaf length and width presented quantitative trait loci (QTLs) whose allelic effects largely differed between both dimensions but were consistent in the field and the platform, with high QTL × Environment interaction. It is therefore worthwhile to identify the genetic and environmental controls of leaf width and leaf length for prediction of plant leaf area.
Vincent Oury, François Tardieu, Olivier Turc
Plant Physiology, 2016.
Grain abortion allows the production of at least a few viable seeds under water deficit but causes major yield loss. It is maximum for water deficits occurring during flowering in maize (Zea mays). We have tested the hypothesis that abortion is linked to the differential development of ovary cohorts along the ear and to the timing of silk emergence. Ovary volume and silk growth were followed over 25 to 30 d under four levels of water deficit and in four hybrids in two experiments. A position-time model allowed characterizing the development of ovary cohorts and their silk emergence. Silk growth rate decreased in water deficit and stopped 2 to 3 d after first silk emergence, simultaneously for all ovary cohorts, versus 7 to 8 d in well-watered plants. Abortion rate in different treatments and positions on the ear was not associated with ovary growth rate. It was accounted for by the superposition of (1) the sequential emergence of silks originating from ovaries of different cohorts along the ear with (2) one event occurring on a single day, the simultaneous silk growth arrest. Abortion occurred in the youngest ovaries whose silks did not emerge 2 d before silk arrest. This mechanism accounted for more than 90% of drought-related abortion in our experiments. It resembles the control of abortion in a large range of species and inflorescence architectures. This finding has large consequences for breeding drought-tolerant maize and for modeling grain yields in water deficit.
Vincent Oury, Cecilio F. Caldeira, Duyên Prodhomme, Jean-Philippe Pichon, Yves Gibon, François Tardieu, Olivier Turc
Plant Physiology, 2016.
Flower or grain abortion causes large yield losses under water deficit. In maize (Zea mays), it is often attributed to a carbon limitation via the disruption of sucrose cleavage by cell wall invertases in developing ovaries. We have tested this hypothesis versus another linked to the expansive growth of ovaries and silks. We have measured, in silks and ovaries of well-watered or moderately droughted plants, the transcript abundances of genes involved in either tissue expansion or sugar metabolism, together with the concentrations and amounts of sugars, and with the activities of major enzymes of carbon metabolism. Photosynthesis and indicators of sugar export, measured during water deprivation, suggested sugar export maintained by the leaf. The first molecular changes occurred in silks rather than in ovaries and involved genes affecting expansive growth rather than sugar metabolism. Changes in the concentrations and amounts of sugars and in the activities of enzymes of sugar metabolism occurred in apical ovaries that eventually aborted, but probably after the switch to abortion of these ovaries. Hence, we propose that, under moderate water deficits corresponding to most European drought scenarios, changes in carbon metabolism during flowering time are a consequence rather than a cause of the beginning of ovary abortion. A carbon-driven ovary abortion may occur later in the cycle in the case of carbon shortage or under very severe water deficits. These findings support the view that, until the end of silking, expansive growth of reproductive organs is the primary event leading to abortion, rather than a disruption of carbon metabolism.
Sebastien Lacube, Loïc Manceau, Claude Welcker, Emilie J Millet, Brigitte Gouesnard, Carine Palaffre, Jean-Marcel Ribaut, Graeme Hammer, Boris Parent, François Tardieu.
Journal of Experimental Botany, 2020.
The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.
Emilie J Millet, Willem Kruijer, Aude Coupel-Ledru, Santiago Alvarez Prado, Llorenç Cabrera-Bosquet, Sébastien Lacube, Alain Charcosset, Claude Welcker, Fred van Eeuwijk, François Tardieu
Nature Genetics, 2019.
The development of germplasm adapted to changing climate is required to ensure food security. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios (genotype × environment interaction), in spite of promising results for flowering time8. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
Boris Parent, Margot Leclere, Sébastien Lacube, Mikhail A Semenov, Claude Welcker, Pierre Martre, François Tardieu
PNAS (Proceedings of the National Academy of Sciences), 2018.
Projections based on invariant genotypes and agronomic practices indicate that climate change will largely decrease crop yields. The comparatively few studies considering farmers’ adaptation result in a diversity of impacts depending on their assumptions. We combined experiments and process-based modeling for analyzing the consequences of climate change on European maize yields if farmers made the best use of the current genetic variability of cycle duration, based on practices they currently use. We first showed that the genetic variability of maize flowering time is sufficient for identifying a cycle duration that maximizes yield in a range of European climatic conditions. This was observed in six field experiments with a panel of 121 accessions and extended to 59 European sites over 36 years with a crop model. The assumption that farmers use optimal cycle duration and sowing date was supported by comparison with historical data. Simulations were then carried out for 2050 with 3 million combinations of crop cycle durations, climate scenarios, management practices, and modeling hypotheses. Simulated grain production over Europe in 2050 was stable (−1 to +1%) compared with the 1975–2010 baseline period under the hypotheses of unchanged cycle duration, whereas it was increased (+4–7%) when crop cycle duration and sowing dates were optimized in each local environment. The combined effects of climate change and farmer adaptation reduced the yield gradient between south and north of Europe and increased European maize production if farmers continued to make the best use of the genetic variability of crop cycle duration.