Maize is widely cultivated throughout the world and has highest production among all the cereals. India is the sixth largest producer of maize in the world, contributing 2% of global production and accounting for 9% of the total food grain production in the country. Based on increasing growth rates of poultry, livestock, fish, and milling industries, the demand for maize is expected to increase from the current level of 17 to 45 million tons by 2030. To understand the growing pattern and economics of crop production, it is necessary to predict crop yield using statistical models and geographic information system soil mapping and the impacts of insect and pest damage. In this study, the focus was to forecast maize yield in India using an autoregressive integrated moving average (ARIMA) model and genetic algorithm (GA) approach. GA simulates the evolution of living organisms, where the fittest individual dominates the weaker ones by mimicking the biological mechanism of evolution, such as selection, crossover, and mutation. GA has successfully been applied to solve optimization problems. The study reveals that implementation of GA in ARIMA enhances the prediction accuracy of the model.

The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses.

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.

The study looks into past trends and volatility in the demand and supply components of the last 50 years (1970 to 2019) besides assessing the reliability of macro-economic scenarios of rice by 2020 to 2030 published by OECD and NITI Aayog. The study infers the growth in the area under rice cultivation is 0.30 per cent per annum but yield growth is 1.79 per cent per annum. Yield growth rather than area growth would drive future increases in rice production. Scanning of scenarios of reduced rice land revealed that India would need to boost its rice yield by a maximum of one tonne per hectare to maintain future output levels. The reliability of the projection of rice by OECD and NITI Aayog is very high since the mean absolute percentage error of demand is below 2 per cent and of supply is below 16 per cent. Discussion on future outlook suggests that India needs to either boost up its agri-infrastructure or free up some of its rice area in favour of resource conservation and crop diversification. The outlook for rice throws light on upcoming possibilities and challenges and suggests recommendations for alternative policy options to address the dynamics in the rice sector.

The present study analysed the primary data collected from 180 randomly selected kinnow growing farmers of three districts of Haryana and Punjab during the year 2016–17. It was revealed that kinnow cultivation in north western India has advantage over the traditional wheat-cotton farming; yielding 121.33% higher net return per ha. Kinnow cultivation involved lesser investment on irrigation, fertilizer and plant protection chemicals, but generated 12.78% higher employment than that of wheat-cotton farming system. Kinnow cultivation has proved out to be a viable enterprise. This is, therefore, empirically proven that it is a suitable option to diversify from the prevailing rice-wheat cropping system in certain parts of north western India.