Background: Blackgram [Vigna mungo (L.) Hepper] has significant agronomic and nutritional significance. Its productivity is insufficient to fulfil the expanding local demand in India. Increasing its productivity using appropriate agronomic practices is crucial. With this background, an experiment was conducted to study the effect of foliar application of liquid organic bio-stimulants on development, production and physiological characteristics of blackgram under irrigated conditions. Methods: Seven treatments comprising recommended dose of fertilizers (RDF) with foliar spray of dhasagavya, liquid rhizobium, fish amino acid, panchagavya, PPFM and seaweed extract at 1% and 3%, respectively were tested in randomised block design with three replications. The dimension of blackgram quantitative characters, viz., grain yield, plant height, number of branches per plant, dry matter production (DMP), leaf area index (LAI), number of pods per plant, number of seeds per pod, pod weight per plant, pod length, crop growth rate, total chlorophyll content, soluble protein content and nitrate reductase activity were reduced using principal component analysis (PCA). Result: The PCA was performed on all the attributes as correlation between the quantitative characters was found to be stronger among most of the biometric observations. It was noticed that almost 67% of the data’s total variability, as reflected by the first two principal components. It demonstrated that grain production, DMP, nitrate reductase activity, pods per plant and leaf area index were the primary contributors.

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.

Split plot design experiments were conducted to assess the performance of growth regulating compounds for mitigating moisture stress and the incidence of Brown Plant Hopper (BPH) in rice. The main plot treatments (4) comprised moisture stress free control (M₁), moisture stress during panicle initiation stage alone (M₂), moisture stress during flowering stage alone (M₃), and moisture stress during both panicle initiation and flowering stages (M₄). The sub-plot treatments (5) were foliar application of growth regulating compounds including chlormequat chloride at 200 ppm (S₁), mepiquat chloride at 200 ppm (S₂), brassinolide at 0.1 ppm (S₃), pink pigmented facultative methylotrophs (PPFM) at 1% (S₄), and no spray control (S₅). The reduced plant growth attributes were registered under moisture stress at both panicle initiation and flowering stages. The spraying of 1% PPFM during the flowering or both at panicle initiation and flowering stages led to better performance than the other treatments. Also, spraying 1% PPFM brought down the population of BPH to a considerable level during both years of experiments. This suggests that spraying 1% PPFM in the post-flowering stage helps to mitigate the ill effect the moisture stress and BPH incidence.

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.

Background: The AquaCrop model for bengal gram successfully matches the biomass and yield of reported crop data under various planting dates, according to results that have been validated. Further this study was carried out in different agroclimatic zones of Tamil Nadu to assess variations in yield and water requirement. Methods: In order to simulate realistic yields for the Bengal gram in response to planting date impacts in the Guziliamparai Block in the Dindugal District of Tamil Nadu, the study parameterized and verified performance of the AquaCrop model. Four field trials planted in D1 - 1st November, D2 - 15th November, D3 - 1st December, and D4 - 15th December provided the model calibration data. Result: Fruit yield and biomass had R2 values of 0.9 and 0.8, respectively, indicating strong agreement between observed and model-simulated data. Fruit output and biomass were found to have RMSE values of 0.4 and 0.2, respectively. The NRMSE values for fruit yield and biomass were determined to be 0.3 and 0.1, respectively. The AquaCrop model somewhat overestimated fruit production and biomass since the BIAS was less than 0.4 and 0.2 for yield and biomass, respectively. Fruit production and biomass showed less agreement between simulated and actual D levels (0.4 to 0.6). Across Tamil Nadu's ACZ, a 20C temperature increase decreased the yield by an average of 6%, with minimums of 2% and maximums of 13%. An rise in temperature of 3°C and 4°C, respectively, decreased the yield by 9% and 19% on average.