Editorial: Plant Phenotyping and Phenomics for Plant Breeding

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.

Agriculture generates 11.4 billion tons of biomass worldwide, including residues from crop production and industrial processing. Improper disposal of agricultural residues results in environmental pollution and the waste of valuable biomass resources. Management of agricultural waste is particularly suboptimal in developing countries where low added-value traditional practices to manage the residues are commonplace. A bioeconomy approach to better manage agricultural waste is to use renewable biological resources from land and sea to produce new materials and energy, allowing the development of bio-based value chains (BBVC). This study explored the potential of BBVC in the management of cacao pod husk (CPH), the primary residual biomass generated from the production of cacao in Colombia. Thus, a literature review on cacao pod husk (CPH) management strategies and a survey for farmers from Caldas State in Colombia between August and November of 2020 were performed to identify the potentials and limitations of BBVC in the management of CPH. Assessment of CPH management strategies suggested variable uses for CPH categorized in energy, food, and miscellaneous. Analysis of surveys indicated farmers are keen to implement strategies to better manage their agricultural waste, but that information is not available to them. Finally, an approach to develop a BBVC from cacao was proposed, which we plan to implement as a future research direction. We expect to impact the economic growth positively in the region with bio-based products in the market.

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.

The Food and Agriculture Organization (FAO) AquaCrop model, run either via a standalone graphical user interface (GUI) or via a matlab application programming interface (API) (AquaCrop-OS), has been successfully tested on many crop species and under multiple scenarios. However, with these current versions, it is difficult for users to adapt formulae, add functionality or incorporate the model into other applications such as decision support tools. Here, we report on the release of a version of AquaCrop written in R. Performance of the model was tested using published datasets of wheat (Triticum aestivum L.) and maize (Zea mays L.), comparing output from AquaCropR with these other versions of AquaCrop. Our goal in developing this version was to widen the use and improvement of AquaCrop through open access.

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.