Background: Chickpea is one of the major protein rich legume crops predominantly cultivated in North Interior Karnataka (NIK). The study aimed to determine water requirement of chickpea (variety BGD- 103) using CROPWAT model that helps in tapping the potential yields of the crop through proper irrigation management by the farmers of North Interior Karnataka (NIK), which consists of 12 districts with 88,361 km2 area. Methods: The crop was simulated by considering recommended practices of UAS, Dharwad across four dates of sowing from 01st October to 15th November at quarterly interval for the past (1991-2020) and projected period (2021-2050), and the decadal analysis was done for the past and projected climate. The analysed climate, crop and soil data were used for simulation using CROPWAT model. Results: The average crop evapotranspiration (ETc), effective rainfall (ER) and irrigation requirement (IR) under past climate (1991-2020) for NIK were 292.6, 57.4 and 245.9 mm, respectively. Decrease of 67 mm in ETc, 91.3 mm in IR and an increase of 40.8 mm in ER were observed under projected climate. Sowing early i.e., on 01st October under projected climate (2021-2050) simulated the lowest water requirement and irrigation requirement for all the 12 districts of NIK.

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The El Niño-Southern Oscillation (ENSO) is a recurring climate pattern involving changes in the temperature of waters in the central and eastern tropical Pacific Ocean. The three phases of the El Niño–Southern Oscillation (ENSO) namely the neutral phase, El Niño and La Niña oscillations. El Niño refers to the above-average sea-surface temperatures that periodically develop across the east-central equatorial Pacific. It represents the warm phase of the ENSO cycle. La Niña refers to the periodic cooling of sea-surface temperatures across the east-central equatorial Pacific. The state of Karnataka is located on a table land in the angle where the Western and Eastern Ghat ranges converge into the Nilgiri hill complex. 41 year (1980-2020) average annual rainfall of Karnataka collected from the rain gauge station located under the farm universities of Karnataka and, SST and SOI data collected from NOAA were used to study the rainfall variability while ENSO events. The El Niño events will deviate the rainy winds towards eastern pacific region causing lesser rainfall on Indian sub-continent or draughts in some years, but when it comes to Karnataka El Niño events have given above average rainfall. There were 8 episodes of excess rainfall and 6 episodes of deficient rainfall during the 14 El Niño episodes, and 3 episodes of excess rainfall and 8 episodes of deficient rainfall during the 11 La Niña episodes. The remaining 16 episodes were neutral years, with 10 episodes having excessive rainfall and the remaining 6 having deficient rainfall. Hence the El Niño episodes is good when compared to La Niña episodes over Karnataka.

Rainfall is one of the important weather factors deciding the economy, food habits, industries and work force of the area. Distribution of rainfall throughout the year further decides cropping pattern, variety to be cultivated and duration of the crop and management practices for efficient rainfall utility. In this regard 51 year past rainfall data (1970-2020) of Dharwad district of Karnataka was collected from Main Agricultural Research Station (MARS), UAS, Dharwad for analysing seasonal, annual and monthly variabilities. From the analysis we found that the rainfall varied in the range of 417 to 1316 mm with the average of 748 mm and with the variation of 22 per cent. Five years moving rainfall average showed declining trend. July month recorded highest average rainfall of 136 mm with the lowest variation (39 per cent). Though January month recorded lowest rainfall (one mm), the highest variation was observed in February. Southwest (Jun-Sept), Northeast (Oct-Dec), Winter (Jan-Feb) and Post monsoon (March-May) contributed 65 per cent, 19 per cent, 1per cent and 15 per cent, respectively of the annual rainfall of Dharwad district. The lowest variation in rainfall over the years was observed in Southwest monsoon (27 per cent) while winter season showed highest variation. Farmers can be advised to take long duration crops like Cotton and Maize in kharif because of nearly even distributed rainfall for the months from June- October and early sowing in case of rabi to harness the more moisture for better yield. Summer sowing should be avoided because of more uncertainty in rainfall.

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage.