Foodomics: A Data-Driven Approach to Revolutionize Nutrition and Sustainable Diets.

Globally, we are failing to meet numerous nutritional, health, and environmental targets linked to food. Defining food composition in its full chemical and quantitative diversity is central to data-driven decision making for supporting nutrition and sustainable diets. "Foodomics"-the application of omics-technology to characterize and quantify biomolecules to improve wellbeing-has the potential to comprehensively elucidate what is in food, how this composition varies across the food system, and how diet composition as an ensemble of foods guides outcomes for nutrition, health, and sustainability. Here, we outline: (i) challenges of evaluating food composition; (ii) state-of-the-art omics technology and innovations for the analysis of food; and (iii) application of foodomics as a complementary data-driven approach to revolutionize nutrition and sustainable diets. Featuring efforts of the Periodic Table of Food Initiative, a participatory effort to create a globally shared foodomics platform, we conclude with recommendations to accelerate foodomics in ways that strengthen the capacity of scientists and benefit all people.

Articulating the effect of food systems innovation on the Sustainable Development Goals.

Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.

Perspective article: Actions to reconfigure food systems.

There is broad agreement that current food systems are not on a sustainable trajectory that will enable us to reach the Sustainable Development Goals by 2030, particularly in the face of anthropogenic climate change. Guided by a consideration of some food system reconfigurations in the past, we outline an agenda of work around four action areas: rerouting old systems into new trajectories; reducing risks; minimising the environmental footprint of food systems; and realigning the enablers of change needed to make new food systems function. Here we highlight food systems levers that, along with activities within these four action areas, may shift food systems towards more sustainable, inclusive, healthy and climate-resilient futures. These actions, summarised here, are presented in extended form in a report of an international initiative involving hundreds of stakeholders for reconfiguring food systems.

High-resolution and bias-corrected CMIP5 projections for climate change impact assessments.

Projections of climate change are available at coarse scales (70-400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method -a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a 'perfect sibling' framework and show that it reduces climate model bias by 50-70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

GeoFarmer: A monitoring and feedback system for agricultural development projects.

Farmers can manage their crops and farms better if they can communicate their experiences, both positive and negative, with each other and with experts. Digital agriculture using internet communication technology (ICT) may facilitate the sharing of experiences between farmers themselves and with experts and others interested in agriculture. ICT approaches in agriculture are, however, still out of the reach of many farmers. The reasons are lack of connectivity, missing capacity building and poor usability of ICT applications. We decided to tackle this problem through cost-effective, easy to use ICT approaches, based on infrastructure and services currently available to small-scale producers in developing areas. Working through a participatory design approach, we developed and tested a novel technology. GeoFarmer provides near real-time, two-way data flows that support processes of co-innovation in agricultural development projects. It can be used as a cost-effective ICT-based platform to monitor agricultural production systems with interactive feedback between the users, within pre-defined geographical domains. We tested GeoFarmer in four geographic domains associated with ongoing agricultural development projects in East and West Africa and Latin America. We demonstrate that GeoFarmer is a cost-effective means of providing and sharing opportune indicators of on-farm performance. It is a potentially useful tool that farmers and agricultural practitioners can use to manage their crops and farms better, reduce risk, increase productivity and improve their livelihoods.

Reduction in nutritional quality and growing area suitability of common bean under climate change induced drought stress in Africa.

Climate change impacts on food security will involve negative impacts on crop yields, and potentially on the nutritional quality of staple crops. Common bean is the most important grain legume staple crop for human diets and nutrition worldwide. We demonstrate by crop modeling that the majority of current common bean growing areas in southeastern Africa will become unsuitable for bean cultivation by the year 2050. We further demonstrate reductions in yields of available common bean varieties in a field trial that is a climate analogue site for future predicted drought conditions. Little is known regarding the impact of climate change induced abiotic stresses on the nutritional quality of common beans. Our analysis of nutritional and antinutritional compounds reveals that iron levels in common bean grains are reduced under future climate-scenario relevant drought stress conditions. In contrast, the levels of protein, zinc, lead and phytic acid increase in the beans under such drought stress conditions. This indicates that under climate-change induced drought scenarios, future bean servings by 2050 will likely have lower nutritional quality, posing challenges for ongoing climate-proofing of bean production for yields, nutritional quality, human health, and food security.

Global conservation priorities for crop wild relatives.

The wild relatives of domesticated crops possess genetic diversity useful for developing more productive, nutritious and resilient crop varieties. However, their conservation status and availability for utilization are a concern, and have not been quantified globally. Here, we model the global distribution of 1,076 taxa related to 81 crops, using occurrence information collected from biodiversity, herbarium and gene bank databases. We compare the potential geographic and ecological diversity encompassed in these distributions with that currently accessible in gene banks, as a means to estimate the comprehensiveness of the conservation of genetic diversity. Our results indicate that the diversity of crop wild relatives is poorly represented in gene banks. For 313 (29.1% of total) taxa associated with 63 crops, no germplasm accessions exist, and a further 257 (23.9%) are represented by fewer than ten accessions. Over 70% of taxa are identified as high priority for further collecting in order to improve their representation in gene banks, and over 95% are insufficiently represented in regard to the full range of geographic and ecological variation in their native distributions. The most critical collecting gaps occur in the Mediterranean and the Near East, western and southern Europe, Southeast and East Asia, and South America. We conclude that a systematic effort is needed to improve the conservation and availability of crop wild relatives for use in plant breeding.

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation.

Agriculture research uses "recommendation domains" to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers' experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers' fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.

Variation and impact of drought-stress patterns across upland rice target population of environments in Brazil.

The upland rice (UR) cropped area in Brazil has decreased in the last decade. Importantly, a portion of this decrease can be attributed to the current UR breeding programme strategy, according to which direct grain yield selection is targeted primarily to the most favourable areas. New strategies for more-efficient crop breeding under non-optimal conditions are needed for Brazil's UR regions. Such strategies should include a classification of spatio-temporal yield variations in environmental groups, as well as a determination of prevalent drought types and their characteristics (duration, intensity, phenological timing, and physiological effects) within those environmental groups. This study used a process-based crop model to support the Brazilian UR breeding programme in their efforts to adopt a new strategy that accounts for the varying range of environments where UR is currently cultivated. Crop simulations based on a commonly grown cultivar (BRS Primavera) and statistical analyses of simulated yield suggested that the target population of environments can be divided into three groups of environments: a highly favorable environment (HFE, 19% of area), a favorable environment (FE, 44%), and least favourable environment (LFE, 37%). Stress-free conditions dominated the HFE group (69% likelihood) and reproductive stress dominated the LFE group (68% likelihood), whereas reproductive and terminal drought stress were found to be almost equally likely to occur in the FE group. For the best and worst environments, we propose specific adaptation focused on the representative stress, while for the FE, wide adaptation to drought is suggested. 'Weighted selection' is also a possible strategy for the FE and LFE environment groups.

Increasing homogeneity in global food supplies and the implications for food security.

The narrowing of diversity in crop species contributing to the world's food supplies has been considered a potential threat to food security. However, changes in this diversity have not been quantified globally. We assess trends over the past 50 y in the richness, abundance, and composition of crop species in national food supplies worldwide. Over this period, national per capita food supplies expanded in total quantities of food calories, protein, fat, and weight, with increased proportions of those quantities sourcing from energy-dense foods. At the same time the number of measured crop commodities contributing to national food supplies increased, the relative contribution of these commodities within these supplies became more even, and the dominance of the most significant commodities decreased. As a consequence, national food supplies worldwide became more similar in composition, correlated particularly with an increased supply of a number of globally important cereal and oil crops, and a decline of other cereal, oil, and starchy root species. The increase in homogeneity worldwide portends the establishment of a global standard food supply, which is relatively species-rich in regard to measured crops at the national level, but species-poor globally. These changes in food supplies heighten interdependence among countries in regard to availability and access to these food sources and the genetic resources supporting their production, and give further urgency to nutrition development priorities aimed at bolstering food security.

Addressing uncertainty in adaptation planning for agriculture.

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

A gap analysis methodology for collecting crop genepools: a case study with phaseolus beans.

BACKGROUND: The wild relatives of crops represent a major source of valuable traits for crop improvement. These resources are threatened by habitat destruction, land use changes, and other factors, requiring their urgent collection and long-term availability for research and breeding from ex situ collections. We propose a method to identify gaps in ex situ collections (i.e. gap analysis) of crop wild relatives as a means to guide efficient and effective collecting activities. METHODOLOGY/PRINCIPAL FINDINGS: The methodology prioritizes among taxa based on a combination of sampling, geographic, and environmental gaps. We apply the gap analysis methodology to wild taxa of the Phaseolus genepool. Of 85 taxa, 48 (56.5%) are assigned high priority for collecting due to lack of, or under-representation, in genebanks, 17 taxa are given medium priority for collecting, 15 low priority, and 5 species are assessed as adequately represented in ex situ collections. Gap "hotspots", representing priority target areas for collecting, are concentrated in central Mexico, although the narrow endemic nature of a suite of priority species adds a number of specific additional regions to spatial collecting priorities. CONCLUSIONS/SIGNIFICANCE: Results of the gap analysis method mostly align very well with expert opinion of gaps in ex situ collections, with only a few exceptions. A more detailed prioritization of taxa and geographic areas for collection can be achieved by including in the analysis predictive threat factors, such as climate change or habitat destruction, or by adding additional prioritization filters, such as the degree of relatedness to cultivated species (i.e. ease of use in crop breeding). Furthermore, results for multiple crop genepools may be overlaid, which would allow a global analysis of gaps in ex situ collections of the world's plant genetic resources.

Regional heterogeneity and gene flow maintain variance in a quantitative trait within populations of lodgepole pine.

Genetic variation is of fundamental importance to biological evolution, yet we still know very little about how it is maintained in nature. Because many species inhabit heterogeneous environments and have pronounced local adaptations, gene flow between differently adapted populations may be a persistent source of genetic variation within populations. If this migration-selection balance is biologically important then there should be strong correlations between genetic variance within populations and the amount of heterogeneity in the environment surrounding them. Here, we use data from a long-term study of 142 populations of lodgepole pine (Pinus contorta) to compare levels of genetic variation in growth response with measures of climatic heterogeneity in the surrounding region. We find that regional heterogeneity explains at least 20% of the variation in genetic variance, suggesting that gene flow and heterogeneous selection may play an important role in maintaining the high levels of genetic variation found within natural populations.

The role of geographic analysis in locating, understanding, and using plant genetic diversity.

The genetic structure of an organism is shaped by various factors, many of which vary significantly over space. In this chapter, we provide insight on how studying geographic patterns may contribute to an improved understanding of variability in genetic structure. We first review the theoretical background on how differences in genetic structure may be generated through processes that are inherently variable over space. We then present novices with some basics on how geographic information systems (GIS) may be adopted to study this variation, including advice on software, data, and the type of research questions that might be addressed. The chapter finishes with a brief review of how spatial analysis has contributed to the conservation and use of plant genetic resources, through an understanding of spatial patterns in species distribution and genetic structure. We conclude that spatial variation is a factor often overlooked in genetic studies and one that merits greater consideration. With the advent of functional genomics and improved quantification of adaptive traits, spatial analysis may be key in understanding variation in genetic structure through careful analysis of genotype-environment interactions.