Rice is an important food crop cultivated all over the world and in India. There are different factors such as temperature, relative humidity, rainfall and solar radiation are influencing on rice crop production. Not only these factors, but also, some of the micro climatic factors such as canopy temperature, leaf temperature, soil temperature and stomatal conductance are also influencing on crop production. Microclimate, which refers to the climatic factors in the immediate proximity of the plants it controls and influences the physiological responses of the plants as well as the activities of energy exchange between the plant and its surroundings. It is expected that increased year-to-year yield variability in crop production will result from an increase in the frequency and severity of droughts and floods, as well as from irregular precipitation patterns.In order to promote food security and agricultural sustainability in this changing climate, it is necessary to use such microclimatic alterations in crop production in order to reduce the risk of extreme weather events and increase crop output. This study aims to increase crop output and land productivity through microclimate modification as a demonstration of the effectiveness and efficiency of growth factor utilisation. The detailed description of microclimate and its role with reference to rice crops are reviewed under this chapter.

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. Pulse production is influenced by a variety of elements such as rainfall, fertilizer, crop area as well as productivity. Analysis of production behavior, modeling and forecasting of productivity taking all these factors in to consideration play vital roles in human nutritional security. The present investigation is an attempt to predict and forecast the productivity of total pulses in Tamil Nadu using time series data. The present study was carried out to efficiently forecast the productivity of black gram, chickpea, green gram, horse gram, red gram, and total pulses in Tamil Nadu. Yearly data were used for the period from 1970 to 2020. based onthe results of model adequacy criteria, the most suitable ARIMA (autoregressive integrated moving average) model and Holt's Linear Trend model are chosen to capture the pulse productivity. Results revealed that Holt's linear trend model fits best for black gram, chickpea, green gram, and red gram. ARIMA (0,1,1) fits best for horse gram and ARIMA (3,1,0) fits best for the total pulses productivity. The forecasted value of pulses using the bestfitted model shows that there is a steady increase in the productivity of pulses. The productivity of total pulse increases in 2021,2022,2023 but slightly decreases in 2024 and again increases in 2025. This study will play an important role in determining the gap between the productivity of and demand for pulses in the future.

Background: In India, the dairy business is expanding dramatically. Tamil Nadu milk cooperatives significantly contribute to the growth of the dairy sector in the state. In terms of delivering economic income for dairy smallholders and satisfying customer demand, the identification of milk production is one of the primary financial operations made in India. Considering this, it is crucial to understand future production to enhance and sustain the sector’s growth and development. Methods: The present investigation attempts to predict and forecast milk production in Tamil Nadu using time series models. Yearly milk data from 1976 to 2020 was taken. The study considered Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to select the appropriate stochastic model for forecasting milk production in Tamil Nadu. Further statistical modeling procedures employed for milk production reveal that the selection of a suitable time series model will always depend on the nature of the data. Result: Results revealed that the ARIMA model is selected as the best model despite ANN, even if it is considered the most powerful model. The CAGR for forecasted milk production from 2020-2025 was 0.02%. Model adequacy criteria like RMSE, MAPE and MAE are used. Based on observation ARIMA model (1, 1, 2) is chosen as the best model over the ANN model.