Analysis of fruit ripening in Theobroma cacao pod husk based on untargeted metabolomics.

The pod husk of Theobroma cacao (CPH) plays an important agronomical role, as its appearance is used as indicator of ripening, guiding the farmers in the harvest process. Cacao harvesting is not a standardized practice because farmers harvest between six up to eight months from flowering, guided by pod's color and shape. The mixture of cacao beans from different ripening stages (RS), negatively affecting the quality and price of grain. A way to help the farmers in the harvest standardization could be through the use of chemical markers and visual indicators of CPH ripening. This study analyses CPH's metabolic distribution of two cacao clones, ICS95 and CCN51 at six, seven, and eight months of ripening. Untargeted metabolomics was done using HPLC-MS/MS for biomarker discovery and association to cacao ripening. The results indicated a strong metabolic differentiation of the sixth month with the rest of the months independent of the variety. Also, metabolic differences were found between cacao clones for the seventh and eighth month. We annotated five potential biochemical markers including 3-caffeoylpelargodinin 5-glucoside, indoleacetaldehyde, procyanidin A dimer, procyanidin C1, and kaempferol. We further looked for correlation between patterns of progression of our markers against quantitative indicators of CPH appearance and texture, at the same ripening stages. We also performed a functional analysis and three possible metabolic pathways: flavone and flavonol biosynthesis, flavonoid biosynthesis, and tryptophan metabolism were identified associated with stress sensing, plant development and defense respectively. We found significant and positive correlations between green color density and all metabolites. For texture, the correlations were significantly negative with all metabolites. Our results suggest that about the sixth month is appropriate for harvesting cacao in the region of Caldas, Colombia in order to avoid all the metabolic variations occurring at later stages of ripening which impact the cacao bean quality. Therefore, studying the cacao ripening process can help in the estimation of the best harvest time and contribute to the standardization of harvest practices.

Genetic architecture of variation in Arabidopsis thaliana rosettes.

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

A Crop Modelling Strategy to Improve Cacao Quality and Productivity.

Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/-2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51.

Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat.

Senescence is the final stage of leaf development and is critical for plants' fitness as nutrient relocation from leaves to reproductive organs takes place. Although senescence is key in nutrient relocation and yield determination in cereal grain production, there is limited understanding of the genetic and molecular mechanisms that control it in major staple crops such as wheat. Senescence is a highly orchestrated continuum of interacting pathways throughout the lifecycle of a plant. Levels of gene expression, morphogenesis, and phenotypic development all play key roles. Yet, most studies focus on a short window immediately after anthesis. This approach clearly leaves out key components controlling the activation, development, and modulation of the senescence pathway before anthesis, as well as during the later developmental stages, during which grain development continues. Here, a computational multiscale modelling approach integrates multi-omics developmental data to attempt to simulate senescence at the molecular and plant level. To recreate the senescence process in wheat, core principles were borrowed from Arabidopsis Thaliana, a more widely researched plant model. The resulted model describes temporal gene regulatory networks and their effect on plant morphology leading to senescence. Digital phenotypes generated from images using a phenomics platform were used to capture the dynamics of plant development. This work provides the basis for the application of computational modelling to advance understanding of the complex biological trait senescence. This supports the development of a predictive framework enabling its prediction in changing or extreme environmental conditions, with a view to targeted selection for optimal lifecycle duration for improving resilience to climate change.

Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa.

Agriculture remains critical to Africa's socioeconomic development, employing 65% of the work force and contributing 32% of GDP (Gross Domestic Product). Low productivity, which characterises food production in many Africa countries, remains a major concern. Compounded by the effects of climate change and lack of technical expertise, recent reports suggest that the impacts of climate change on agriculture and food systems in African countries may have further-reaching consequences than previously anticipated. Thus, it has become imperative that African scientists and farmers adopt new technologies which facilitate their research and provide smart agricultural solutions to mitigating current and future climate change-related challenges. Advanced technologies have been developed across the globe to facilitate adaptation to climate change in the agriculture sector. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9), synthetic biology, and genomic selection, among others, constitute examples of some of these technologies. In this work, emerging advanced technologies with the potential to effectively mitigate climate change in Africa are reviewed. The authors show how these technologies can be utilised to enhance knowledge discovery for increased production in a climate change-impacted environment. We conclude that the application of these technologies could empower African scientists to explore agricultural strategies more resilient to the effects of climate change. Additionally, we conclude that support for African scientists from the international community in various forms is necessary to help Africans avoid the full undesirable effects of climate change.

Genotypic variations in leaf and whole-plant water use efficiencies are closely related in bread wheat genotypes under well-watered and water-limited conditions during grain filling.

Wheat plants growing under Mediterranean rain-fed conditions are exposed to water deficit, particularly during the grain filling period, and this can lead to a strong reduction in grain yield (GY). This study examines the effects of water deficit after during the grain filling period on photosynthetic and water-use efficiencies at the leaf and whole-plant level for 14 bread wheat genotypes grown in pots under glasshouse conditions. Two glasshouse experiments were conducted, one in a conventional glasshouse at the Universidad de Talca, Chile (Experiment 1), and another at the National Plant Phenomics Centre (NPPC), Aberystwyth, UK (Experiment 2), in 2015. Plants were grown under well-watered (WW) and water-limited (WL) conditions during grain filling. The reductions in leaf water potential (Ψ), net CO2 assimilation (An) and stomatal conductance (gs) due to water deficit were 79, 35 and 55%, respectively, during grain filling but no significant differences were found among genotypes. However, chlorophyll fluorescence parameters (as determined on dark-adapted and illuminated leaves) and chlorophyll content (Chl) were significantly different among genotypes, but not between water conditions. Under both water conditions, An presented a positive and linear relationship with the effective photochemical quantum yield of Photosystem II (Y(II)) and the maximum rate of electron transport (ETRmax), and negative with the quantum yield of non-photochemical energy conversion in Photosystem II (Y(NPQ)). The relationship between An and Chl was positive and linear for both water conditions, but under WL conditions An tended to be lower at any Chl value. Both, instantaneous (An/E) and intrinsic (An/gs) water-use efficiencies at the leaf level exhibited a positive and linear relationship with plant water-use efficiency (WUEp = plant dry weight/water use). Carbon discrimination (Δ13C) in kernels presented a negative relationship with WUEp, at both WW and WL conditions, and a positive relationship with GY. Our results indicate that during grain filling wheat plants face limitations to the assimilation process due to natural senesce and water stress. The reduction in An and gs after anthesis in both water conditions was mainly due a decline in the chlorophyll content (non-stomatal limitation), whereas the observed differences between water conditions were mainly due to a stomatal limitation.

Predicting future biomass yield in Miscanthus using the carbohydrate metabolic profile as a biomarker.

In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant science. We aimed to identify biomarkers of productivity in the bioenergy crop, Miscanthus, that could be used prognostically to predict future yields. This study identified a metabolic profile reflecting productivity in Miscanthus by correlating the summer carbohydrate composition of multiple genotypes with final yield 6 months later. Consistent and strong, significant correlations were observed between carbohydrate metrics and biomass traits at two separate field sites over 2 years. Machine-learning feature selection was used to optimize carbohydrate metrics for support vector regression models, which were able to predict interyear biomass traits with a correlation (R) of >0.67 between predicted and actual values. To identify a causal basis for the relationships between the glycome profile and biomass, a 13C-labelling experiment compared carbohydrate partitioning between high- and low-yielding genotypes. A lower yielding and slower growing genotype partitioned a greater percentage of the 13C pulse into starch compared to a faster growing genotype where a greater percentage was located in the structural biomass. These results supported a link between plant performance and carbon flow through two rival pathways (starch vs. sucrose), with higher yielding plants exhibiting greater partitioning into structural biomass, via sucrose metabolism, rather than starch. Our results demonstrate that the plant metabolome can be used prognostically to anticipate future yields and this is a method that could be used to accelerate selection in perennial energy crop breeding programmes.