A harmonised dataset for modelling select underutilised crops Across EU.

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.

Agro-climatic sensitivity analysis for sustainable crop diversification; the case of Proso millet (Panicum miliaceum L.).

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.

Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia).

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.

Crop model ideotyping for 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.

Open Data to Support Agricultural Diversification (version October 2020).

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.

Optimizing Traditional Cropping Systems Under Climate Change: A Case of Maize Landraces and Bambara Groundnut.

Traditional crop species are reported to be drought-tolerant and nutrient-dense with potential to contribute to sustainable food and nutrition security within marginal production systems under climate change. We hypothesized that intercropping maize landraces (Zea mays L.) with bambara groundnut (Vigna subterranea (L.) Verdc.), together with optimum management strategies, can improve productivity and water use efficiency (WUE) under climate change. Using an ex-ante approach, we assessed climate change impacts and agronomic management options, such as plant ratios, and plant sequences, on yield and WUE of intercropped maize landrace and bambara groundnut. The Agricultural Production Systems sIMulator (APSIM) model was applied over four time periods; namely past (1961-1991), present (1995-2025), mid-century (2030-2060) and late-century (2065-2095), obtained from six GCMs. Across timescales, there were no significant differences with mean annual rainfall, but late century projections of mean annual temperature and reference crop evaporation (ET0) showed average increases of 3.5°C and 155mm, respectively. By late century and relative to the present, the projected changes in yield and WUE were -10 and -15% and 5 and 7% for intercropped bambara groundnut and maize landrace, respectively. Regardless of timescale, increasing plant population improved yield and WUE of intercropped bambara groundnut. Asynchronous planting increased yield and WUE for both maize landrace (5 and 14%) and bambara groundnut (35 and 47%, respectively). Most significant improvements were observed when either crop was planted 2-3 months apart. To reduce yield gaps in intercrop systems, low-cost management options like changing plant populations and sequential cropping can increase yield and WUE under projected climate change. To further increase sustainability, there is a need to expand the research to consider other management strategies such as use of other traditional crop species, fertilization, rainwater harvesting and soil conservation techniques.

The first version of nation-wide open 3D soil database for Sri Lanka.

Soil data for Sri Lanka are available through semi-detailed series maps that were developed based on limited soil profile data combined with expert knowledge. This data plays a vital role in decisions at national and regional levels. However, the present format of this database does not allow for their wider use in crop simulation modelling and other related agricultural research that require finer scale data. This is due to the fact that cross-country profile data are not harmonised based on standard depths. Several attempts were made to produce digital soil data for Sri Lanka at different geographic scales, however, a completely harmonised data that covers variability across depths and properties is yet to be made available. In this article, we describe the first version of the open digital soil database that was developed using a database of 122 locations across the country. Soil properties were harmonised for standard depths using equal-area quadratic smoothing splines. Out of several interpolation methods that were evaluated for univariate interpolation, maps which were produced with the least overall error (RMSE) in the process of cross-validation were selected. The newly developed digital soil database contains 9 soil properties; pH, bulk density, cation exchange capacity, organic carbon, volumetric moisture content at 0.33 and 15 bars levels, sand silt and clay content. Moreover, the data are available for five standard depth layers as 0-5, 5-15, 15-30, 30-60 and 60-100 cm in raster format at 1 km spatial resolution. Both interpolated property maps and their error maps were stored in an open repository and made available for public use. The first version of all maps is also showcased online through open web mapping services. The repository will be gradually updated with higher resolution and more accurate maps as more samples become available and better interpolation method are used. This data could provide complementary information for insight generation at finer scales where limited local informaiton about soils hinders agricultural development.