Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the ‘Swiss Cheese’ nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22–53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types.

Fall armyworm, Spodoptera frugiperda (J. E. Smith) is a polyphagous and highly destructive invasive insect pest of many crops. It was recently introduced into India and widely reported in almost all parts of India. Development of a temperature-based phenology model for predicting its rate of development and distribution will help in understanding the establishment and further spread of introduced invasive insect pests. Development, survival and reproduction parameters of S. frugiperda at six constant temperature conditions (15, 20, 25, 27, 30 and 35°C) were investigated and further validated with data generated under fluctuating temperature conditions. The estimated lower developmental threshold temperatures were 12.1°C for eggs, 11°C for larvae, 12.2°C for pupae, 15.13°C for males and 12.66°C for females. Degree-day (DD) requirements for the development of the different stages of S. frugiperda were 50, 250 and 200 DD for egg, larva and pupa, respectively. The best-fitted functions were compiled for each life stage to yield a phenology model, which was stochastically simulated to estimate the life table parameters. The developed phenology model predicted temperature ranges between 27 and 30°C as favourable for S. frugiperda development, survival and reproduction. The results revealed that maximum net reproductive rate (215.66 females/female/generation) and total fecundity (981.08 individuals/female/generation) were attained at 30°C constant temperature. The mean length of generations decreased from 74.29 days at 15°C to 38.74 days at 30°C. The maximum intrinsic rate of increase (0.138 females/female/day) and shortest doubling time (4.9 days) were also observed at 30°C. Results of simulated life table parameters showed high temperature-dependent development of S. frugiperda and complete development within all the tested constant temperature ranges (15–35°C). Simulated life table parameters for predicting risk indices of S. frugiperda in India indicated a significant increase in activity indices and establishment risk indices with a higher number of generations during future (2050 and 2070) climatic change scenarios compared to present conditions. Our results indicate that India will be highly suitable for the establishment and survival of S. frugiperda in future time periods.

Studies were conducted to develop temperature-based phenology model for Spodoptera litura on groundnut, at both constant and fluctuating temperatures and to predict the possibility of pest risk in future climate change scenarios of India using ‘stochastic simulation tool’ in Insect Life Cycle Modelling (ILCYM) software ,which is based on rate summation and cohort up-dating approach. Phenology model predicted temperatures between 25oC and 30oC as the favourable range for S. litura development, survival and reproduction. The intrinsic rate of increase (rm), and finite rate of increase (») increased with increase in temperature from 15oC to 30oC and decreased with increase in temperature. Intrinsic rate of increase (rm), varied from 0.05 females/female/day at 15oC to 0.17 females/female/day at 30oC. S. litura population attained a maximum net reproductive rate ‘Ro’ (334.09 females/female/generation) and total fecundity (1041.88 individuals/ female/generation) at 27°C temperature. Simulated life table parameters were used to determine indices such as the establishment risk index (ERI), the generation index (GI), and activity index (AI) by using the ‘Population distribution and risk mapping’ module of software during present and future climatic scenarios and significant increase in AI and ERI with higher GI at future (2050) climatic conditions compared to current (2000) climatic conditions indicating the strong suitability for establishment and survival of S.litura in India.

Various mathematical models were fitted to describe total dry matter production (DMP) and cob weight in two maize cultivars viz., Deccan hybrid and Deccan 101. The data on periodical crop growth from an agronomic trial conducted at the University of Agricultural Sciences, Bangalore, were used to predict crop and cob growth empirically. In cv. Deccan hybrid, Gompertz followed by Richards models predicted DMP by 99 % nearer to the actual values. Whereas in cv. Deccan 101, Richards‐cum‐logistic for vegetative‐cum‐reproductive stage simulated DMP with R2 of 92 to 99 %. Further, cob growth was estimated realistically with high R2 of 97 to 98 % using empirical models of logistic followed by Richards in cv. Deccan 101 and Richards followed by Gompertz in cv. Deccan hybrid. Comparing the empirical models in describing DMP and cob growth, the models showing higher predictions in DMP also estimated cob growth meaningfully in both the cultivars, indicating similarity in growth functions.