Climate change will alter the photochemistry of crops, especially the C3 plants. Cowpea (Vigna unguiculata L.) being an annual, photo-insensitive C3 legume is benefitted by altered climate. Any changes in the nutritional quality of host will certainly affect its herbivore. In this view, study was conducted on crop-pest interaction i.e., to know the impact of climate change on the growth and development of cowpea alone and in presence of associated sucking pest aphid, Aphis craccivora (Koch) during kharif 2019-20. The crop was raised in Open Top Chambers previously set with different climatic treatments. Observations on change in plant growth and phytochemistry along with its effect on herbivore aphid was studied. The results revealed that elevated CO2 and temperature influenced the crop positively in terms of growth and also have registered higher concentrations of leaf pigments, carbon and ‘C’ based metabolites. In contrast, lower concentrations of flavonoids, ‘N’ based compounds were recorded which ultimately altered the C: N ratio in plant system. The obtruded phytochemistry of cowpea under elevated treatments resulted in decreased development (12.73 ±0.07 days) and survival fitness of aphids, wherein increased fecundity (34.56 ±0.18days) and population density (73.43/ top three leaves) was noticed when compared to ambient treatments.

Aims: Accurate estimates of evaporation by employing efficient and proven soft computing techniques that involve least number of influencing variables are important to tackle present water crisis. Place and Duration of Study: In the present study, Artificial Neural Network (ANN) and fuzzy logic models were developed to predict the pan evaporation (Ep) in Raichur, Karnataka, using six input parameters viz., maximum and minimum temperatures, maximum and minimum relative humidity, sunshine hours and wind speedfor the period of 30 years (1990-2019). Methodology: Comparison between models was done to select best suitable model to predict pan evaporation. The ANN models were trained withthree training algorithms. Gaussian membership function was used in fuzzy logic (FL) model. Results: The results revealed that, the ANN-GDX model performed better over ANN-LM, ANN-BR and fuzzy logic models during validation period. The correlation coefficient (r), coefficient of efficiency (CE), mean absolute error (MAE) and root mean square error (RMSE) were observed to be 0.7637, 0.5831, 1.3880 and 1.8541 respectively during validation period between actual and predicted pan evaporation (Ep) with 1.3880 mm root mean square error. Therefore, ANN-GDX model was chosen for predicting pan evaporation in the study area. Conclusion: ANN-GDX model was chosen for predicting pan evaporation in the study area.

Climate change has a widespread influence on agricultural productivity and relies upon the fact that responses vary between crops. Any changes in the nutritional quality of the host plant will have direct influences on associated insect pests and the damage that it causes. In this context, a study was conducted to measure the impact of elevated CO2 and temperature on the growth and development of cowpea (C3 plant) and its herbivore pod borer, Maruca vitrata (Fabricius) under open top chambers at the Center for Agro-climatic Studies, UAS, Raichur, Karnataka. Elevated CO2 had a positive impact on cowpea growth by encouraging crop boom along with vital changes in its phytochemistry. The increased supply of CO2 resulted in higher concentrations of ‘C’ and C-based metabolites and chlorophyll (39.81 µg/cm2) and nitrogen balance index (104.52). However, ‘N’ and N-based compounds were reduced. Larvae fed upon such nutrient-deficient food increased their development period (36.07 ±0.42days) and compensatory feeding inflicting more damage (7.72 webbings/plant). Although the larvae consumed more food, it decreased the body weight of the larvae and pupae, which in turn decreased the percent moth emergence, ultimately decreasing the fecundity (60.09 ± 0.20/female) and fitness of the pest in the long run.