The current study was carried out to analyze the trend and forecast in area, production and productivity of mango crop in Karnataka. It was determined by using the secondary data of area, production and productivity of mango for the period of 18 years (2000-01 to 2017-18) was collected from Directorate of Economics and Statistics, Karnataka. To estimate the trend and its forecast for the next 5 years, up to 2022-23, linear, quadratic, exponential, logistic and Gompertz models were fitted and the best-fitted model was selected based on lowest MAPE. Result revealed that exponential model was best-fitted for area and production of mango, and the logistic model was found to be the best-fitted model for the mango productivity. The result also explored that the area, production and productivity of mango crop have an upward trend in Karnataka state in above study period. Based on this trend to forecast area, production and productivity of mango crop for the period from 2018-19 to 2022-23.

Precise estimation of rainfall is a crucial and challenging task in environmental science. It involves the use of advanced and powerful models to forecast non-linear and dynamic changes in rainfall. Deep learning, a recently developed method for handling vast amounts of data and resolving complex problems, has proven to be an effective tool for rainfall forecasting. In this study, we applied various deep learning models such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Stacked LSTM, Gated Recurrent Units (GRUs), and a traditional model called Autoregressive Integrated Moving Average (ARIMA), to forecast monthly rainfall data (mm) for three regions of Karnataka: Coastal Karnataka, North Interior Karnataka (NIK), and South Interior Karnataka (SIK). Trend analysis was conducted using the Mann-Kendall trend test (MK test) and the Seasonal Mann-Kendall trend test, along with Sen's Slope Estimator, to determine trends and slope magnitudes. The results showed that deep learning models perform better than traditional methods in forecasting rainfall. The performance of different models was evaluated using forecasting evaluation criteria and found that the LSTM model performed best for Coastal Karnataka, with an RMSE value of 149.45, while the Bi-LSTM model performed best for NIK, with an RMSE value of 32.57, and the Stacked LSTM model performed best for SIK, with an RMSE value of 45.33. Therefore, deep learning models can be effectively used to predict rainfall data with greater accuracy.

Coffee is a significant commodity crop worldwide, and Karnataka, an Indian state with coffee-growing regions such as Chikkamagaluru, Kodagu, and Hassan, is a major producer. Chikkamagaluru, also known as the Coffee Land of Karnataka, is the primary location for Arabica coffee production and cultivation of various other spice crops such as areca nut, pepper, cardamom, vanilla, lime, clove, and cinnamon. Despite the importance of coffee production, coffee growers encounter multiple challenges in cultivating and yielding high-quality coffee. Therefore, researchers have explored issues associated with coffee production and yield and suggested feasible solutions. To make informed decisions, it is essential to analyze the productivity and production of the coffee-growing region. This study used 25 years of coffee time series data obtained from the Coffee Board of India, Bengaluru, from 1995-1996 to 2019-2020, to investigate the coffee-growing region's problems. The data was analyzed using linear (linear, cubic) and nonlinear (exponential, logistic, and Gompertz) growth models. The results showed that the cubic model provided the best fit for the Chikkamagaluru district's coffee-growing region. Meanwhile, the linear and Gompertz models were the best fit for coffee output and productivity, respectively. The study revealed a decrease in Chikkamagaluru coffee productivity over the study period, despite an increase in the coffee-growing area.