Breeding drought-tolerant crop varieties with higher water use efficiency could help maintain food supply to a growing population and save valuable water resources. Fast and accurate phenotyping is currently a bottleneck in the process towards attaining this goal, as available plant phenotyping platforms have an excessive cost for many research institutes or breeding companies. Here we describe a simple and low-cost, automatic platform for high-throughput measurement of plant water use and growth and present its utilisation to assess the drought tolerance of two soybean genotypes. The platform allows the evaluation of up to 120 plants growing in individual pots. A cart moving in only one direction carries the measuring and watering devices. Watering and measurement routines allow the simulation of multiple water regimes for each plant individually and indicate the timing of measurement of soil water content and image capture for growth estimation. Water use, growth and water use efficiency were measured in two experiments with different water scenarios. Differences in water use efficiency between genotypes were detected only in some treatments, emphasising the importance of phenotyping platforms to evaluate a genotype’s phenotype under a broad range of conditions in order to capture valuable differences, minimising the chance of artefacts and increasing precision of measurements.

Plants under water deficit reduce leaf growth, thereby reducing transpiration rate at the expense of reduced photosynthesis. The objective of this work was to analyse the response of leaf growth to water deficit in several sunflower genotypes in order to identify and quantitatively describe sources of genetic variability for this trait that could be used to develop crop varieties adapted to specific scenarios. The genetic variability of the response of leaf growth to water deficit was assessed among 18 sunflower (Helianthus annuus L.) inbred lines representing a broad range of genetic diversity. Plants were subjected to long-term, constant-level, water-deficit treatments, and the response to water deficit quantified by means of growth models at cell-, leaf-, and plant-scale. Significant variation among lines was found for the response of leaf expansion rate and of leaf growth duration, with an equal contribution of these responses to the variability in the reduction of leaf area. Increased leaf growth duration under water deficit is usually suggested to be caused by changes in the activity of cell-wall enzymes, but the present results suggest that the duration of epidermal cell division plays a key role in this response. Intrinsic genotypic responses of rate and duration at a cellular scale were linked to genotypic differences in whole-plant leaf area profile to water deficit. The results suggest that rate and duration responses are the result of different physiological mechanisms, and therefore capable of being combined to increase the variability in leaf area response to water deficit.

Sunflower (Helianthus annuus L.) grain and oil quality are defined by grain weight and oil percentage, oil fatty acid composition and the amount of antioxidants. The aim of this work was to establish and validate a simple model, based on published relationships, which can estimate not only yield and its components, but also grain and oil quality aspects which are of relevance for industrial processes or human health. The model we developed provided good estimations of grain yield (similar to those of a more complex model) and oil quality from independent experiments. It explained known differences in potential yield and grain and oil quality between locations, in terms of differences in incident radiation, mean or minimum temperature. Simulations showed that recent climatic changes could have caused a decrease in sunflower yield and changes in oil quality. Our results suggest that at locations at lower latitudes, sunflower oil with high nutritious value and oxidative stability could compensate for relatively low yields, while at higher latitudes, highlinoleic acid oil production should be compatible with high yield potentials. Our model could facilitate the selection of the best location, sowing date or density for the production of sunflower oil with specific quality characteristics.

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Drought is a major limitation to crop yields worldwide. Screening for soybean yield under water deficit is often a bottleneck in breeding programmes. We assessed the validity of a standardized drought tolerance screening method to predict water‐limited field performance of soybean in NW Argentina. First, to determine the phenological period when yield of glasshouse‐grown plants was more sensitive to water deficit, we applied treatments during 21 days in V7, R3 or R5 stages, being the period from R5 to R6 the most critical for yield. Afterwards, two glasshouse experiments were carried out to quantify the tolerance of either eight or four genotypes, respectively, by applying a controlled water deficit of constant intensity during the critical period. Finally, yield data obtained in field trials in Argentina across several locations and seasons classified according to rainfall were analysed. Drought Susceptibility Index was calculated for each experiment and for field data, and rankings of tolerance were similar in all cases. This standardized method, which can be automated for high‐throughput phenotyping, could represent a useful tool in breeding programmes for identifying soybean cultivars with improved performance under drought conditions.

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