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.

A multiscale mathematical model describing the growth and development of bambara groundnut.

A principal objective in agriculture is to maximise food production; this is particularly relevant with the added demands of an ever increasing population, coupled with the unpredictability that climate change brings. Further improvements in productivity can only be achieved with an increased understanding of plant and crop processes. In this respect, mathematical modelling of plants and crops plays an important role. In this paper we present a two-scale mathematical model of crop yield that accounts for plant growth and canopy interactions. A system of nonlinear ordinary differential equations (ODEs) is formulated to describe the growth of each individual plant, where equations are coupled via a term that describes plant competition via canopy-canopy interactions. A crop of greenhouse plants is then modelled via an agent based modelling approach in which the growth of each plant is described via our system of ODEs. The model is formulated for the African drought tolerant legume bambara groundnut (Vigna subterranea), which is currently being investigated as a food source in light of climate change and food insecurity challenges. Our model allows us to account for plant diversity and also investigate the effect of individual plant traits (e.g. plant canopy size and planting distance) on the yield of the overall crop. Informed with greenhouse data, model results show that plant positioning relative to other plants has a large impact on individual plant yield. Variation in physiological plant traits from genetic diversity and the environmental effects lead to experimentally observed variations in crop yield. These traits include plant height, plant carrying capacity, leaf accumulation rate and canopy spread. Of these traits plant height and ground cover growth rates are found to have the greatest impact on crop yield. We also consider a range of different planting arrangements (uniform grid, staggered grid, circular rings and random allocation) and find that the staggered grid leads to the greatest crop yield (6% more compared to uniform grid). Whilst formulated specifically for bambara groundnut, the generic formulation of our model means that with changes to certain parameter's, it may be extended to other crop species that form a canopy.

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.