International Maize and Wheat Improvement Center

About: International Maize and Wheat Improvement Center is a nonprofit organization based out in Texcoco, Mexico. It is known for research contribution in the topics: Population & Agriculture. The organization has 1976 authors who have published 4799 publications receiving 218390 citations.

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

Abstract: 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,anoptimisationofthismatchispossible.Also,furtherprogressinbreedingvarietiesthatcopewithwaterstressis hamperedbythetypicallylargegenotypeenvironmentinteractionsinmost fieldstudies.Therefore,amorecomprehensive 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.

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits

Abstract: Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-Lopez et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson’s correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.

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

Abstract: 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.