The indoor cultivation of lettuce in a vertical hydroponic system (VHS) under artificial lighting is an energy-intensive process incurring a high energy cost. This study determines the optimal daily light integral (DLI) as a function of photoperiod on the physiological, morphological, and nutritional parameters, as well as the resource use efficiency of iceberg lettuce (cv. Glendana) grown in an indoor VHS. Seedlings were grown in a photoperiod of 12 h, 16 h, and 20 h with a photosynthetic photon flux density (PPFD) of 200 µmol m−2 s−1 using white LED lights. The results obtained were compared with VHS without artificial lights inside the greenhouse. The DLI values for 12 h, 16 h, and 20 h were 8.64, 11.5, and 14.4 mol m−2 day−1, respectively. The shoot fresh weight at harvest increased from 275.5 to 393 g as the DLI increased from 8.64 to 11.5 mol m−2 day−1. DLI of 14.4 mol m−2 day−1 had a negative impact on fresh weight, dry weight, and leaf area. The transition from VHS without artificial lights to VHS with artificial lights resulted in a 60% increase in fresh weight. Significantly higher water use efficiency of 71 g FW/L and energy use efficiency of 206.31 g FW/kWh were observed under a DLI of 11.5 mol m−2 day−1. The study recommends an optimal DLI of 11.5 mol m−2 day−1 for iceberg lettuce grown in an indoor vertical hydroponic system.

An effort has been made to get precise forecast of rice yield through ARIMAX and proposed hybrid models using weather variables. In this article, two hybrid approaches like ARIMAX-ANN and ARIMAX-SVM have been proposed. Firstly, ARIMAX model was fitted for the considered time series data. Rice yield along with weather variables of Aligarh district of Uttar Pradesh have been considered to evaluate the forecasting performance of the proposed hybrid models. The residuals obtained from the fitted model which exhibit nonlinear pattern were fitted employing ANN and SVM. Using the fitted yield values through the hybrid approaches via ANN and SVM, MAPE under ARIMAX (0,1,1)-ANN and ARIMAX (0,1,1)-SVM are estimated to be 0.37 and 1.11, respectively, as compared to 12.18 under ARIMAX (0,1,1) model. Based on the results obtained, we infer that although performance of proposed ARIMAXSVM and ARIMAX-ANN models are close to each other but much superior to the conventional ARIMAX model for the considered data set. Performance of hybrid ARIMAX model is found to be quite encouraging. Yield has also been forecasted up to 2020 on the basis of forecasted rainfall using ARIMAX (0,1,1) model.

Long term forecasting of crop production is required to establish long term vision, say by 2025, to meet growing demand of population at that point of time. Existing univariate linear time series ARIMA approach is valid for short term forecast only. In this paper, a technique for long term yield forecast has been proposed. Initially, we have tried to improve short term forecast of yield by using hybrid ARIMA through ANN approach. The forecast values of yield through hybrid approach was considered as baseline data for long term forecast of yield. Time series data on rice yield was considered for Aligarh district of Uttar Pradesh for the study. Through ARIMA (2,1,0), we got short term forecast of yield by 2020 and the residuals obtained by 2013 were used to model and forecast through ANN approach. For the residuals, 05:04s:1l (05 time delay and 04 hidden nodes) model was identified as suitable one as it has minimum values of mean absolute percentage error (MAPE) for training and testing sets. Using 05:04s:1l model, residuals were forecasted by 2020, forecast values of yield obtained through ARIMA (2,1,0) were corrected by forecasted residuals and eventually get forecast of yield through hybrid approach. The estimated MAPE for ARIMA (2,1,0) and hybrid approach were 17.677% and 4.65%, respectively. Significant reduction in MAPE through hybrid approach indicates it’s much better performance as compared to ARIMA alone. Using hybrid approach, we got forecast of yield by 2020 and considering this forecasted yield as baseline data, we got forecast by 2025 through the proposed approach.

To investigate the interdependence between Indian onion markets in terms of price volatility, the present study was conducted in four different vital onion markets in India, viz. Mumbai, Nashik, Delhi and Bengaluru. The long term monthly data, from March, 2003 to September, 2015 was collected from the website of agmarknet.nic.in. We have employed the VEC-MGARCH model to estimate mean and volatility spillover simultaneously among the different markets and also examined the nature of dynamic correlation using the DCC model. The presence of mean and volatility spillover was found between the markets. This type of significant interaction between the volatility of different markets is highly useful for cross market hedging and for sharing of common information by market participants. The empirical results also suggest for a very close observation on different market behavioral pattern since, “news” in one market may impact other market through the number of interdependencies. Key words: Dynamic conditional correlations, Market behavior, Price volatility spillover

No abstract