Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

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.

Marginal 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.

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.