Mainassara Zaman-Allah

Water: the most important 'molecular' component of water stress tolerance research

Water deficit is the main yield-limiting factor across the Asian and African semiarid tropics and a basic consideration when developing crop cultivars for water-limited conditions is to ensure that crop water demand matches season water supply. Conventional breeding has contributed to the development of varieties that are better adapted to water stress, such as early maturing cultivars that match water supply and demand and then escape terminal water stress. However, an optimisation of this match is possible. Also, further progress in breeding varieties that cope with water stress is hampered by the typically large genotype×environment interactions in most field studies. Therefore, a more comprehensive approach is required to revitalise the development of materials that are adapted to water stress. In the past two decades, transgenic and candidate gene approaches have been proposed for improving crop productivity under water stress, but have had limited real success. The major drawback of these approaches has been their failure to consider realistic water limitations and their link to yield when designing biotechnological experiments. Although the genes are many, the plant traits contributing to crop adaptation to water limitation are few and revolve around the critical need to match water supply and demand. We focus here on the genetic aspects of this, although we acknowledge that crop management options also have a role to play. These traits are related in part to increased, better or more conservative uses of soil water. However, the traits themselves are highly dynamic during crop development: they interact with each other and with the environment. Hence, success in breeding cultivars that are more resilient under water stress requires an understanding of plant traits affecting yield under water deficit as well as an understanding of their mutual and environmental interactions. Given that the phenotypic evaluation of germplasm/breeding material is limited by the number of locations and years of testing, crop simulation modelling then becomes a powerful tool for navigating the complexity of biological systems, for predicting the effects on yield and for determining the probability of success of specific traits or trait combinations across water stress scenarios.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization

Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.

Higher flower and seed number leads to higher yield under water stress conditions imposed during reproduction in chickpea

The reproductive phase of chickpea (Cicer arietinum L.) is more sensitive to water deficits than the vegetative phase. The characteristics that confer drought tolerance to genotypes at the reproductive stage are not well understood; especially which characteristics are responsible for differences in seed yield under water stress. In two consecutive years, 10 genotypes with contrasting yields under terminal drought stress in the field were exposed to a gradual, but similar, water stress in the glasshouse. Flower number, flower+pod+seed abortion percentage, pod number, pod weight, seed number, seed yield, 100-seed weight (seed size), stem+leaf weight and harvest index (HI) were recorded in well watered plants (WW) and in water-stressed plants (WS) when the level of deficit was mild (phase I), and when the stress was severe (phase II). The WS treatment reduced seed yield, seed and pod number, but not flower+pod+seed abortion percentage or 100-seed weight. Although there were significant differences in total seed yield among the genotypes, the ranking of the seed yield in the glasshouse differed from the ranking in the field, indicating large genotype×environment interaction. Genetic variation for seed yield and seed yield components was observed in the WW treatment, which also showed differences across years, as well as in the WS treatment in both the years, so that the relative seed yield and relative yield components (ratio of values under WS to those under WW) were used as measures of drought tolerance. Relative total seed yield was positively associated with relative total flower number (R2=0.23 in year 2) and relative total seed number (R2=0.83, R2=0.79 in years 1 and 2 respectively). In phase I (mild stress), relative yield of seed produced in that phase was found to be associated with the flower number in both the years (R2=0.69, R2=0.76 respectively). Therefore, the controlled drought imposition that was used, where daily water loss from the soil was made equal for all plants, revealed genotypic differences in the sensitivity of the reproductive process to drought. Under these conditions, the seed yield differences in chickpea were largely related to the capacity to produce a large number of flowers and to set seeds, especially in the early phase of drought stress when the degree of water deficit was mild.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.

New Technologies for Phenotyping

Improvements in agronomical practices and crop breeding are paramount responses to the present and future challenges imposed by water stress and heat (Lobell et al. 2011a, b; Cairns et al. 2013; Hawkins et al. 2013). On what concerns breeding, constraints in field phenotyping capability currently limit our ability to dissect the genetics of quantitative traits, especially those related to yield and water stress tolerance. Progress in sensors, aeronautics and high-performance computing is paving the way. Field high throughput platforms will combine non-invasive remote-sensing methods, together with automated environmental data collection. In addition, laboratory analyses of key plant parts may complement direct phenotyping under field conditions (Araus and Cairns 2014). Moreover, these phenotyping techniques may also help to cope with spatial variability inherent to phenotyping in the field.

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging

In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.