For over six decades, international policy has enabled agricultural products to move relatively easily across national borders. Currently, however, the landscape is changing. Deglobalization and the erosion of multilateral principles threaten international food supply chains while climate change is increasingly undermining production. In addition, today's food systems contribute to major environmental and human health problems. The global agri-food research agenda must adapt quickly to these realities. Here we propose that a new research agenda be established based on three principles to help respond to challenging times, promote human rights, sustain gains made in the past, and support greater positive impacts in the future. Principle one-a strengthened commitment to community engagement. Principle two-better supporting interdisciplinary systems thinking. Principles three-combatting misinformation by enabling enhanced public communication. We believe that today's crises present an opportunity to establish the foundations of a food system transformation that is more equitable, transparent, sustainable, and democratic.

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling 'on-the-fly' in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK’s 4.5 million hectares of arable land whilst the country imports around half its food—nearly 40 per cent—from just four EU countries (The Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate-resilient and nutritious underutilised crops can help diversify the UK agrifood system, but research and investment in them is sporadic, piecemeal, and unfocused. In this paper, we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on UK Department for Environment, Food and Rural Affairs (Defra) Project CH0224, was delivered through literature and database searches and the expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second used the CropBASE digital knowledge base for underutilised crops. The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future.

AbstractMarginal environments are characterized by constrained agricultural potential and resource degradation attributable to biophysical and politico-socio-economic factors. These environments and the indigenous people who cultivate them rarely attract academic interest, policy studies or investment. The agricultural expertise of indigenous communities is often overlooked by decision-makers. Interventions based on mainstream crops and external technologies may fail indigenous communities where a vast range of crops are cultivated in diverse production systems and in marginal environments. Hunger, malnutrition, and poverty in indigenous communities are high. The challenges should be approached from the perspectives and resources of indigenous people. In this chapter, we discuss four biogeographical regions, arid, semi-arid, humid and mountainous, representing large parts of marginal lands and innovations, investment opportunities, and proposed action for the transformation of food systems in these areas. Marginal areas and indigenous people can benefit from improved linkages between formal and indigenous knowledge systems, participatory and demand-driven technologies, integration of indigenous knowledge in research, improvements in local crops, integrated management and access to markets. Our recommendations for the transformation of food systems in these areas include (1) Efforts to mainstream diverse value chains, (2) Development of evidence-based policies (3) Awareness of under-utilized and forgotten crops (4) Collective action and (5) Coordinated public and private investment in research and development for the empowerment of indigenous people and the development of their land.

We present a systematic framework for nationwide crop suitability assessment within the UK to improve the resilience in cropping systems and nutrition security of the UK population. An initial suitability analysis was performed using data from 1842 crops at 2862 grid locations within the UK, using climate (temperature and rainfall) and soil (pH, depth, and texture) data from the UK Met Office and British Geological Survey. In the second phase, additional qualitative and quantitative data are collected on 56 crops with the highest pedoclimatic suitability and coverage across the UK. An exercise was conducted on crops within each category using a systematic ranking methodology that shortlists crops with high value across a multitude of traits. Crops were ranked based on their nutritional value (macronutrients, vitamins, and minerals) and on adaptive (resistance to waterlogging/flood, frost, shade, pest, weed, and diseases and suitability in poor soils) and physiological traits (water-use efficiency and yield). Other characteristics such as the number of special uses, available germplasm through the number of institutions working on the crops, and production knowledge were considered in shortlisting. The shortlisted crops in each category are bulbous barley (cereal), colonial bentgrass (fodder), Russian wildrye (forage), sea buckthorn (fruit), blue lupin (legume), shoestring acacia (nut), ochrus vetch (vegetable), spear wattle (industrial), scallion (medicinal), and velvet bentgrass (ornamental/landscape). These crops were identified as suitable crops that can be adopted in the UK. We further discuss steps in mainstreaming these and other potential crops based on a systematic framework that takes into account local farming system issues, land suitability, and crop performance modelling at the field scale across the UK.

Current agricultural production depends on very limited species grown as monocultures that are highly vulnerable to climate change, presenting a threat to the sustainability of agri-food systems. However, many hundreds of neglected crop species have the potential to cater to the challenges of climate change by means of resilience to adverse climate conditions. Proso millet (Panicum miliaceum L.), one of the underutilised minor millets grown as a rainfed subsistence crop, was selected in this study as an exemplary climate-resilient crop. Using a previously calibrated version of the Agricultural Production Systems Simulator (APSIM), the sensitivity of the crop to changes in temperature and precipitation was studied using the protocol of the Coordinated Climate Crop Modelling Project (C3MP). The future (2040-2069) production was simulated using bias-corrected climate data from 20 general circulation models of the Coupled Model Intercomparison Project (CMIP5) under RCP4.5 and 8.5 scenarios. According to the C3MP analysis, we found a 1°C increment of temperature decreased the yield by 5-10% at zero rainfall change. However, Proso millet yields increased by 5% within a restricted climate change space of up to 2°C of warming with increased rainfall. Simulated future climate yields were lower than the simulated yields under the baseline climate of the 1980-2009 period (mean 1707 kg ha-1) under both RCP4.5 (-7.3%) and RCP8.5 (-16.6%) though these changes were not significantly (p > 0.05) different from the baseline yields. Proso millet is currently cultivated in limited areas of Sri Lanka, but our yield mapping shows the potential for expansion of the crop to new areas under both current and future climates. The results of the study, indicating minor impacts from projected climate change, reveal that Proso millet is an excellent candidate for low-input farming systems under changing climate. More generally, through this study, a framework that can be used to assess the climate sensitivity of underutilized crops was also developed.

Whilst simulating crop performance in different environments can help fill the knowledge gap and improve the adoption of crops that are currently neglected and underutilised in conventional agrifood systems, lack of experimental data remains a barrier to widespread modelling of these crops. To date, no attempt has been made to collate sub-species crop data that are specifically suited for modelling underutilised crops. This article describes the first attempt to develop a database for crop modelling data with a focus on European underutilised crops. Following a pilot study to identify crops with the potential across the EU, a structured dataset of detailed experimental data was developed by analysing more than 500 agronomic studies that were published across European agroclimatic zones from 1972 to 2022. The dataset contains minimum information for calibrating basic crop models for any location in the EU provided that enough experimental and environmental data are available. More specifically, the database includes crop phenology, yield, management practices, geographic and pedo-climatic details of select underutilised and neglected species. The information underwent a curation procedure to ensure its quality. The collated database will be used in CropBASE, the global knowledge base for underutilised crops.

Abstract While China has transformed its economy over recent decades, challenges such as climate change and land degradation have continued to impact its agriculture. These effects along with changes in diets and growing food imports will force China to look for alternative cropping options. Despite the broad potential of Bambara groundnut (Vigna subterranea L. Verdc) as a resilient and nutritious underutilized crop, less is known about its potential in Asia. Here, we explore the potential of Bambara groundnut to become a mainstream crop in mainland China. A suitability analysis is presented for Bambara groundnut to examine the degree of seasonal adaptability of this crop against its climate and soil requirements across China. Results showed that the crop has yield potential in areas that can be too marginal for production of other mainstream crops such as soybean (Glycine max). If realized, the potential of Bambara groundnut could contribute to China's agriculture and reduce its reliance on vegetable protein imports. Using an average seasonal potential yield of 0.85 t/ha over a potential available area of between 55 and 112 million ha (based on 4 land availability scenarios) and modest price of 143 USD/t, yearly income between USD 6 and 13 billion can potentially be contributed by widespread cultivation of this crop. As well as food security, this drought‐resistant nitrogen‐fixing legume could also contribute to land rehabilitation, particularly in the areas where shift in planting dates and land degradation is noticeable. This study demonstrates the need for more investment and research into adoption of Bambara groundnut and other underutilized crops that have the potential to transform agriculture in populous Asian countries.

Whilst current policies recognize the impacts that changes in climates and markets have imposed on the Italian food system, less attention is given to the development of systematic approaches to identify alternative cropping systems across Italy and the European continent. In this article, a novel evidence-based crop shortlisting method was developed to address crop diversification needs for Italy as an example for the whole of Europe. In order to shortlist possible options from a pool of 2700 crops, a crop–climate–soil matching exercise was performed across Italian territory, and crops with more than 70% suitability were chosen for further analysis. In the second phase, a multi-criteria ranking index was employed to assign ranks to chosen crops of four main types: (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/tubers, and (iv) vegetables. To provide a comprehensive analysis, all of the abovementioned criteria were compared for both major crops that are grown in the region and potential underutilised crops (UCs). The results of evaluation of four major criteria (namely (a) calorie and nutrition demand, (b) functions and uses, (c) availability and accessibility to their genomic material, (d) possession of adaptive traits, and (e) physiological traits) revealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd, and Watercress. We discuss the implication of utilising such systematic approaches to crop selection and developing transformative solutions for food security with the aim of providing a primer for mainstreaming UCs in policy and investment plans.

An evidence base was developed to facilitate adoption of hemp (Cannabis sativa L.) in tropical environments (Wimalasiri et al. (2021)). Agro-ecological requirements data of hemp were acquired from international databases and was contrasted against local climate and soil conditions using an augmented species ecological niche modeling. The outputs were then used to map the suitability for all locations for 12 possible calendar-year seasons within peninsular Malaysia. The most probable seasonal map was then used to generate a land suitability map for agricultural areas across 5 standard land suitability categories. Having developed the general suitability maps of hemp in Malaysia, detailed crop growth data were collected from literature and was then used to simulate an ideotype crop model (for both seed and fiber) for selected locations across Malaysia, where detailed daily climate data and soil information were available. Following the development of a downscaled future climate dataset, a simulated dataset of yield for the future conditions were also developed. Next, the simulated seed and fiber yield data were used to create yield maps for hemp across peninsular Malaysia. An economic value and cost-benefit analyses were also carried out using data that were collected from literature and local sources to simulate the true cost and benefit of growing hemp both for now and future conditions. This data provides the first ever evidence base for an underutilized crop in Southeast Asia. All data that was generated using the proposed published framework for the adoption of hemp in the future are stored in their original format in an online repository and is described in this article. The data can be used to map the suitability at finer scales, analyze and re-calibrate a yield model using any climate scenario and evaluate the economics of production using the standard methodology described in the above-mentioned publication.

Following the development of a database that was specifically designed to store value chain information, particularly for underutilised crops, this article describes the data that are currently stored in the database and accessible through its web portal. The data includes various datasets on utilisation status, agro-ecological requirements and season lengths, potential yield and nutritional composition of crops. The data are stored in the form of tables with fixed data elements (column attributes). This article outlines the standard procedures (SOPs) that were developed in-house for data collection, metadata creation and data curation. These processes were used to ensure the quality and reusability of the data that is made available publicly through the database interface. Various statistics and example visualisations are provided to demonstrate the significance of such data for developing solutions for sustainable agricultural diversification.

Evidence based crop diversification requires modelling for crops that are currently neglected or underutilised. Crop model calibration is a lengthy and resource consuming effort that is typically done for a particular variety or a set of varieties of a crop. Whilst calibration data are widely available for major crops, such data are rarely available for underutilised crops due to limited funding for detailed field data collection and model calibration. Subsequently, the lack of evidence on their performance will lead to the lack of interest from the policy and regulatory communities to include these crops in the agricultural development plans. In order to motivate further research into the use of state of the art techniques in modelling for less known crops, we have developed and validated an ideotyping technique that approximates the crop modelling parameters based on already calibrated crops of different lineage. The method has been successfully tested for hemp (Cannabis sativa L.) based on a well-known crop model. In this paper we present the method and provide an impetus on the way forward to further develop such methods for modelling the performance of minor crops and their varieties.•The approach works based on modelling the performance of hemp using the knowledge from an existing model that was developed for sugar cane.•The customisation uses one of the most prominent models (AquaCrop) to approximate growth coefficients for hemp (Cannabis sativa L.).•A sequential procedure was used to approximate the phenological stages in the growth model that performs well in the calibration and validation steps.

Digital agriculture is driven by the interrelated needs to increase crop production, develop sustainable food systems and cope with the global change. Databases exist for several major crops but a barrier to increasing agmbiodiversity is the lack, and dispersed nature, of information for variously termed minor, orphan or underutilised crops. In this article, we describe an attempt to build a globally accessible database that can be used to store information for underutilised crops. A relational data model was adopted due to its robustness in terms of prototyping and building user interfaces for data governance and dissemination. We have reviewed the design of related agricultural databases, data standards and crop diversification priorities, to build a data model that encompasses major elements of the value chain of crops in the food system. Due to the importance of data accuracy, we added a metadata table that stores information about the sources of all data recorded in the database. We also built a web-based user interface for data management and access. The open-access user interface allows simple data sorting and filtering operations based on the user's needs. As an example of potential use, data were used to build an automated crop selection tool that could shortlist suitable crops based on location at global scale. Other use-cases are discussed including the development of metrics, indicators for the United Nations Sustainable Development Goals and developing bespoke diversification solutions.

Hemp (Cannabis sativa L.) is a multipurpose industrial crop which is mainly cultivated in temperate regions. With its high potential for economic returns for its seeds and fiber, there is growing interest in cultivating hemp in many territories including Malaysia and other Asian countries, where its cultivation is currently illegal. To date, no comprehensive study on the suitability of this crop under Malaysian conditions has been conducted. In this paper, we propose an assessment framework as a madmap to develop the hemp industry in Malaysia and possibly other Asian countries with equatorial climates. This framework includes suitability assessment (climate and soil), crop modelling (current and future yields under climate change) and economic analysis (net present value (NPV), NPV benefit (NPVB) and benefit-cost ratio (BCR). The land suitability assessment classified hemp as an adaptable crop for most of the land in the country. The AquaCrop model, parameterised from secondary data collected from literature was used in simulations and potential yield mapping. The estimated average potential seed and fiber yield at six locations between 2010 and 2019 was 1.61 +/- 0.25 and 2.78 +/- 0.39 t ha(-1) respectively. Using five general circulation model (GCM) simulations, yields under future climates in Malaysia showed an increase in most of the locations. The highest NPVB of 1641 USD ha(-1 )(BCR of 1.33) for seed was estimated under current climate conditions. Yields of 1.38 t ha(-1) (seed) and 3.62 t ha(-1 )(fiber) are the minimum economically feasible yields with a Benefit-Cost Ratio of 1.00 suggesting a potential for hemp cultivation in comparison to countries with established hemp industries. The present framework could be used to develop a pathway for adoption of hemp as a crop for the future in tropical countries.

In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p < 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p < 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

Shortlisting ecologically adaptable plant species can be a starting point for agricultural diversification projects. We propose a rapid assessment framework based on an ecological model that can accelerate the evaluation of options for sustainable crop diversification. To test the new model, expert-defined and widely available crop requirement data were combined with more than 100,000 occurrence data for 40 crops of different types (cereals, legumes, vegetables, fruits, and tubers/roots). Soil pH, texture, and depth to bedrock data were obtained and harmonised based on the optimal rooting depths of each crop. Global baseline temperature and rainfall data were used to extract averages at each location. To evaluate the ability of the method to capture intraspecies variation, a test was performed using more than 1000 accession records of bambara groundnut (Vigna subterranea (L.) Verdc.) as an exemplar underutilised crop. Results showed that a suitability index based on soil pH and an index that combines the thermal suitability moderated by the soil pH, texture, and depth suitability have the potential to predict crop adaptability. We show that the proposed method can be combined with traditional land use and crop models to evaluate diversification options for sustainable land and agrobiodiversity resources management.