Model and Scenario Variations in Predicted Number of Generations of Spodoptera litura Fab. on Peanut during Future Climate Change Scenario

Abstract

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)—2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1–2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18–22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods.

Dynamical climatic model for time to flowering in Vigna radiata

“…The daily weather forecasts for Taiwan from 2020 to 2030 were produced using the weather generator program MarkSim. MarkSim was designed to simulate weather from known sources of monthly climate data [ 33 – 37 ] and takes into account the socio-economic development scenarios described by the four representative carbon dioxide concentration profiles (RCPs) adopted by the Intergovernmental Panel on Climate Change (IPCC) in the fifth assessment report (AR5) in 2014. The profiles correspond to a wide range of possible changes in future anthropogenic emissions of greenhouse gases and are called rcp26, rcp45, rcp60 and rcp85 in accordance with the possible violation values for radiation earth balance in 2100 in respect to the preindustrial epoch (+2.6, +4.5, +6.0 and +8.5 W / m 2 , respectively) [ 38 ].…”

Spatial Modelling of Weather Variables for Plant Disease Applications in Mwea Region

“…The tool was chosen for a variety of reasons; First, it was developed specifically for applications of agricultural modelling [7] [10], with already wide applications in weather variable prediction [12]. Secondly, the tool is freely available (http://gismap.ciat.cgiar.org/MarkSimGCM) and can be used as an online application without any restrictions.…”

“…Using an ensemble of 17 established General Circulation models (GCMs) and accounting for the greenhouse gas (GHGs) emission scenarios, data was extracted at 2 km intervals covering the whole area. The tool has already been used to generate point based weather variables for any position in the world [9] [12]. To assess the usability of the tool over the region, weather information was generated at the exact same locations where weather stations are located.…”

A Simple Technique for Obtaining Future Climate Data Inputs for Natural Resource Models

“…The DSSAT weather file generator was developed for use with the DSSAT crop model, but can also be used to produce rainfall, temperature, and solar radiation information for other model applications. MarkSim has been applied globally (Bharati et al, 2014;De Trincheria et al, 2015;Rao et al, 2015); however, limited information exists in the peer-reviewed literature regarding testing and validation of the tool. Some evaluation articles (Mavromatis and Hansen, 2001;Mzirai et al, 2005;Kahimba et al, 2009) indicate that MarkSim can perform acceptably, but for some locations does not do well at reproducing interannual variability and may not always perform as well as other weather generators at specific locations.…”