Food Grains In India Research Articles

Artificial Bee Colony Algorithm-based Feature Selection and Hybrid ML Framework for Efficient Rice Yield Prediction

India's economy predominantly depends on monsoon and agricultural output. Agribusiness products contribute to nearly a quarter of its gross domestic product and 58% of its population depends on agriculture for their livelihood. Certain crops, like rice, are vital to its food security being the most widely grown crop and accounting for one-third production of foodgrains in India. Understanding and enhancing its production is critical in ensuring food availability and promoting sustainable agricultural practices. Rice yield prediction has been a most researched area in the agriculture domain. Machine Learning (ML) frameworks have been found to perform well in patches with large, complex datasets as insufficient feature engineering and temporal dependencies plague efficacy. In this paper, we propose a swam-based meta-heuristic artificial bee colony (ABC) algorithm for feature selection from the dataset sourced from the Agricultural Production and Statistical Division of the Department of Agriculture Cooperation and Farmers Welfare, Government of India. The feature engineering is further optimized by a hybrid model comprising a convolutional neural network (CNN) for learning hierarchical representations and identifying relevant attributes from the complex dataset and long short-term memory (LSTM) for temporal aspects. Finally, a random forest (RF) regressor provides the benefits of ensemble learning, which merges multiple decision trees to remove bias, and variance and improve prediction accuracy. From the results, it is observed that the proposed hybrid model outperforms existing state-of-the-art standalone and hybrid models with the highest coefficient of determination (R2) and lowest mean square error (MSE) of 0.989 and 13613 respectively. The reliable and efficient hybrid model can aid farmers and policymakers in making informed decisions related to rice yield prediction leading to sustainable agricultural practices.

ICT outreach for farmers and other stakeholders to enhance productivity of oilseeds

Oilseeds are very important next to food-grains in India, in terms of acreage, production and value. Edible oilplays an important role in nutrition since, it acts as a carrier for fat soluble vitamins in the body as well as otherphysiological functions in the body. Therefore, there is an urgent need to increase the productivity of oilseeds in order to meet the daily requirements. Information and communication technology (ICT) could be a possible solution which can share timely and accurate information on weather, inputs, markets, and prices, by feeding information into research and development initiatives, by disseminating knowledge to farmers, by connecting producers and consumers, and through many other avenues. The research efforts of schemes and mobile apps on oilseeds under ICAR has resulted in widespread network in development of high yielding varieties/hybrids coupled with the location-specific crop production and protection technologies. This paper deals with importance of ICT outreach for farmers and other stakeholders that increases production of oilseed crops and reduction of oil imports.

Growth of Foodgrain Production in India

"Food is an essential need for the survival of mankind. Food security and its maintenance are key concerns of developing countries. India was primarily an agricultural economy like many other developing countries as its agriculture sector has a greater share in the Gross Domestic Product (GDP) of the country. The understanding of foodgrain production is substantial to administer the food security level of the country. The present paper aims to analyze the growth of foodgrains production in India. The paper also assesses the current status of foodgrain production in India and identifies the factors that have an impact on the growth of foodgrain production. The data relating to the area, production, and yield of major food grains in India for the period 2010-11 to 2020-21 is collected from various secondary sources. The percentile method is used to analyze the data. The results highlighted that the production of wheat and rice has increased year by year, whereas the production of coarse cereals and pulses fluctuates. It was found that the overall foodgrain production has been on a rising trend with a minor variation in the study period. The present study may persuade policymakers to re-examine and modify the current policies to increase the production of foodgrain, which could meet the food requirements of the country."

Growth and Trends of Agriculture: Food Grain Production and Area in India

Abstract Subject and purpose of work: The purpose of this study is to determine the area and production status of food grains in India. Materials and methods: The current study was conducted using secondary data that was gathered from numerous sources, including the Statistical Abstract of Haryana, and the Economic Survey. Between 1991-1992 and 2021-2022, the area and production of food grains have seen a compound growth rate. Results: The results show a fluctuating trend in area and production during the years 1991-92 to 2021-22. For the study period, the mean average of the production and area of food grains was estimated. Conclusions: Agriculture is still a significant sector for the economy from the p erspectives of self-sufficiency and employment, despite the sector’s diminishing GDP proportion over time. The trajectory and expansion of Gross Value Added (GVA), investments in agriculture, and food grain crops were all examined in this article.

“Millet” as a postcolonial-masculinist sign of difference: tracing the effects of ontological-epistemic erasure on a food grain

ABSTRACT In this paper, I use deconstructive theory to analyze the category of “millet” and the endangerment of food grains in India. I argue that “millet” cohered as a sign of difference from the 1960s through India’s Green Revolution, which created a national infrastructure for the materialization of colonial and masculinist ideology. In the hills of Uttarakhand and through the food grain regionally known as mandua, we see how India’s postcolonial success relied on the ontological-epistemic erasure of women’s food/land practices and assaulted the intertwined “rootedness” (place-making faculties) of women and the crops they cultivate. Reading mandua as “millet” under erasure (millet) reveals how mixed crop systems and practices of socio-ecological reciprocity eroded in the face of Green Revolution ideology and functioned as a bulwark against it. I turn finally to the counterhegemonic potential of “millet,” as Uttarakhandi seed activists link with decentralized third world networks, which are exchanging seeds and building power across and from marginalized places. This opens a potential space of visibility and belonging for Uttarakhandi women farmers in the national arena at a time when the ecological and alimentary value of “millet” has entered national and global conversations, infusing the sign of difference with new meaning.

Performance of Sorghum Crop in India - An Analytical Study

Agriculture, with its allied sectors, is the largest source of livelihoods in India. One third its rural households still depend primarily on agriculture for their livelihood, with 86 percent of farmers being small and marginal. Millets (sorghum, pearl millet and small millets) are the important food and fodder crops in semi-arid tropics. Millets are important cereals which play a significant role in the food and nutrition security of developing countries of Asia and Africa, especially in India, Nigeria. Sorghum (jowar) is one of the main coarse cereal crops of India. Sorghum is widely grown both for food and as a feed grain, while millets are produced almost entirely for food. The economic importance of the millets is increasing in terms of feed value, particularly that of sorghum though it is grown in contrasting situations in different parts of the world. The economic importance of the millets is increasing in terms of feed value, particularly that of sorghum though it is grown in contrasting situations in different parts of the world. Sorghum is the fourth most important food grain in India after Rice, Wheat, and Maize. During 1950-51, it was grown over 15.57 million hectares with a total production of 5.50 million Tones. Though the area under Sorghum in India has declined over time, production has remained more or less constant due to increase in yield. The present study is based on secondary data for the 60 years. The study examines growth rates of area, production and yield of Sorghum in India and as well as major Sorghum growing states. In order to examine the degree of relationship in area, production and yield, the statistical tools have been used. The study indicates that there will be deficit case of in the coming years which is a matter of concern. There is a scope for augmenting the production in the short run by improving the productivity without increasing the area through popularizing new varieties, expanding certified seed distribution, and improving crop management practices.

Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025.

An empirical study on assessment of trend analysis: Food grain production in India

Considering the importance of the food grains to be specified-rice, wheat, coarse cereals, and pulses-is vital in developing the Indian economy. This paper carries out change point detection and trend analysis for the annual historical series of food grains in India. The change points were identified by the methods of the Pettitt?s test (PT), the Buishand?s range test (BRT), and the standard normal homogeneity test (SNHT). On the other hand, the Mann-Kendall (MK) test was used to determine the presence and absence of trends in food grain production, and their slopes were assessed by the Sen?s slope estimator. The change point analysis result shows significant change points for food grains at a 95% level. The discrete change points were observed from 1982 to 2009, but the ideal change point, i.e., 1987, was captured in all food grains. In India?s history, agricultural production in the year 1987 was vulnerable due to drought, and thus it is strong evidence of the need to consider methods based on proven facts. The MK test results reveal that the trend in food grain production was statistically significant and had an upward direction in all three segmentation periods after 1987.

Modeling and Forecasting for Agricultural Production – Food grains in India

Modeling and Forecasting for Agricultural Production – Food grains in India

Recent trends in crop breeding, the varietal induction in seed chain and its impact on food grain production in India

Food grains in India achieved an all-time high production (310.74 mt) during 2020-21. Varietal and seed replacement rates played an important role in achieving this milestone. A total of 1646 varieties of food crops comprising 1273 of cereals and 373 of pulses were released from 2011 to 2022. Of these 977 varieties consisting of cereals (667) and pulses (310) have been inducted into the seed chain during 2020-21. New breeding technonologies such as bio-fortification of food crops and marker-assisted breeding were applied to improve crop varieties with respect to specific desirable traits. A total of 65 bio-fortified varieties comprising rice (8), wheat (28), maize (14), pearl millet (9), finger millet (3), little millet (1) and lentil (2) have been released with improved levels of proteins, essential amino acids (lysine, tryptophan), vitamins (pro-vitamin A) and micro/macro-nutrients (zinc, iron, calcium). Marker-assisted breeding adopting foreground and background selections with targeted gene(s) utilizing molecular markers such as RFLPs, SSRs, CAPs, STS and INDEL resulted in to the development and release of 66 cultivars comprising rice (43), wheat (5), maize (10), pearl millet (2) and chickpea (6) with one/and or combination of traits such as tolerance to biotic/abiotic stresses, herbicide tolerance, nutrient use efficiency, earliness, nutritional and other quality traits. These non-genetically modified crop varieties are expected to play vital role in achieving food and nutrition security in India. The paper also provides insight in to the maintenance breeding of food crops. The analysis of seed requirement and availability during 2010-11 to 2020-21 revealed that seed requirements for cereals and pulses have increased consistently by 31.6% and 62.3%, from 186.8 to 245.8 lakh q and 22.3 to 36.2 lakh q, respectively. The production of rice, wheat, maize, pearl millet and pulses during 2020-21 increased over that of 2010-11 by 27.4%, 26.0%, 45.2%, 4.4% and 41.3%, respectively. The productivity of sorghum was higher by 18.9% over that of 2010-11 but production decreased by 31.7% on account of a reduction in acreage by 42.5% during the corresponding period. The net per capita availability of food grains increased, by 17.2% from 159.5 kg to 187.0 kg during this period. The cereals and pulses production needs to be up-scaled by 5.3% and 24.4% to meet the demand by 2030 from 285.28 mt and 25.46 mt, respectively, in 2020-21, with almost the similar acreage, degrading and depleting natural resources, thus, requiring a greater focus on enhancing yield/ha. Crop-specific breeding programmes should be accelerated with the adoption of new technologies like GM, gene editing, marker-assisted selection and speed breeding to address the challenges of climate change and degrading natural sources.

Structural Change in Rice-Wheat Crop Yield in India: A Multiple Breakpoint Analysis

Rice and wheat are grown in sequence on the same land in the same year in South and South-East Asian cropping land to meet the demand for food of a rapidly increasing population. However, the rice-wheat system being the dominant cropping system in India is exhibiting a declining trend in recent times. This study aims at investigating a structural break in annual crop yield of two major foodgrains in India namely rice and wheat following multiple structural break analysis due to Bai-Peron for the period 1961 to 2018 using FAO(Food & Agricultural Organization) dataset of the United Nations following semi-logarithmic growth model both at level and growth. The results show that both rice and wheat yield are non-stationary at level but stationary in the first difference. The Bai-Perron multiple breakpoint test shows the presence of a structural break in both rice and wheat yield and these are present only in level but not in growth. This study has found a single break in rice yield in the year 1988, and two structural breakpoints in the years 1969 and 1998 for wheat yield. The yield growth has fallen for both rice and wheat in the post-break period compared to the pre-break period. Although the growth of rice yield in the post-break period was 1.388% compared to the pre-break period of 1.772%, the wheat yield exhibited acceleration after the green revolution with growth of 3.018% in the post-break period. The wheat growth, however, has slowed down since the late 1990s due to several ecological constraints including climate change. The above observations are quite consistent with the view that the dual goals of self-reliance and sustainable resource management are mutually incompatible. Resource-conserving technologies and crop diversification are required to be implemented to ensure the sustainability of the rice-wheat cropping system in the Indo-Gangetic plains to overcome food security problems in the region. Appropriate policy reform both at the macro level as well as at the sectoral level is necessary for arresting and reversing the present degradation trends. caused by both institutional and ecological factors

Forecasting Foodgrains Production Using Arima Model and Neural Network

The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.

Foodgrains Production in India: Trend and Decompositions Analysis

The present study was undertaken to analyze the trends in area, production and productivity of major food grain crops for the period 1995-96 to 2015-16 obtaining data from the Directorate of Economics and Statistics, Ministry of Agriculture, Government of India, New Delhi. Foodgrains are cultivated in India in an area of 123.22 million hectares with a production of 251.57 million tonnes. The results of the study revealed that the food grains production in the country has increased at a rate of 1.73 per cent per annum which was contributed by negligible increase in area and productivity during the study period. The production-wise scenario of foodgrains has indicated that, major cereals like rice, wheat and maize showed consistent improvement, but pulses production was not consistent. As a coarse cereal, maize stands in an important position next to rice and wheat, due to its multiple uses with yield increases from about 1595 Kg per ha during 1995-96 to 2563 Kg per ha during 20015-16. The computed growth trend for total food grains in India was positive (0.008) and significant (P<0.01) for production and growth for productivity was positive (0.006) and significant (P<0.01).

Growth and Instability Analysis of India’s Food Grain Sector in the Post Liberalisation Period

Background: Having a population growing at an annual average rate of 1.2 per cent and more than half of its total size depend on primary sector for their livelihood, agricultural sector occupy significant role in the economy of India. The extent of area, production and productivity of food grains and their instability therefore attracts special attention. The present study thus analyses the performance of agricultural sector in India in terms of trend in growth and instability of area, production and productivity of food grain during the three decades of liberalisation. The specific objectives are (i) to examine the growth rate and instability in area, production and yield of food grains in India and (ii) to explain the decomposing changes in the variability of food grains production in India. Methods: In this field-laboratory investigation during 1990-91 to 2018-19, official secondary data has been utilized. Statistical tools like Least Square Growth Rate, Coefficient of Variation of area, production, and yield etc. have been applied for analysis. Result: The study highlighted that area under food grain cultivation remained almost stagnant. However, the overall food grain production registered growth rate of 2 per cent while the production instability was 15.88 per cent. The trend lines fitted to the production of foodgrains shows positive growth rate. The co-efficient of determination (R2) shows 87 percent for production and 92 percent for productivity which determines scope for further production for India. But in area it shows just 3.9 per cent. The result of the study shows that the production of food grains has increased with due instability during given the time period. It also calls for adopting alternate production technologies which will further improve the productivity of food grains to meet increasing demand from the growing population.

Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR

PurposeThis paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.Design/methodology/approachThis paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.FindingsThe findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.Practical implicationsThe outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.Originality/valueSpecifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.

An Empirical Analysis on India’s Food Grain Cultivation, Production and Yield in Pre & Post Globalisation

Globalization is, directly and indirectly, contributing to its effect in all sectors of an economy. The agricultural sector is not exempted from the effect of change due to globalization as a component of the primary sector and a prime sector for human survival needs. The status of self-sufficient in the production of food grain will lead a nation to make a walk of pride among the other member globally. India is an agro-economy. In other words, agriculture is the backbone of the Indian economy. So the production of food grain and its cultivation and yield should be normally high to meet out the demand of a growing population. Also, with the implementation of a policy of globalization, there might be some change in cultivation, production, and yield of food grains in India. In this paper, an attempt was made to examine/ identify the change in area under cultivation, yield, and production by using the secondary sources of data from 1970 to 2017. The selected breakeven point of time was 1991-1992. The annual growth rate pictured the change in a particular point of time; the linear and quadratic model gave the growth over the period selected for the study, and dummy used regression model presented the difference in structural change. AGR results dominated by the negative growth rate; the linear growth model for production depicts that 3.6 percentage of tons of production will be move when a year moves upward. The area under cultivation is deteriorating in AGR, and other models used gave a weakness in explanatory level concerning time for the area under cultivation of food grain. Regarding the obtained results for yield reflect that a positive change exists after globalization, even though a reduction in area under cultivation.

Using Artificial Neural Networks (ANNs) to Improve Agricultural Knowledge Management System (KMS)

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.

Knowledge of improved chickpea production technology of chickpea growers benefited under national food security mission of Narsinghpur district of Madhya Pradesh

Chickpea (Cicer arietinum) generally known as “Chana”/ “Gram” or “Bengal gram” is an important leguminous food grains in India. Chickpea is the world’s third most important food legume with 96% cultivation in the developing countries. The present study entitled “Knowledge of Improved chickpea production technology of chickpea growers benefited under National Food Security Mission of Narsinghpur district of M.P.” was undertaken to evaluate the knowledge level of the chickpea growers towards the improved production technology. There were 120 respondents were selected for the investigation. Total five villages were selected from Narsinghpur block of Narsinghpur district. The findings revealed that higher percentage (50.00%) of chickpea growers had high knowledge of improved chickpea production technology.

Predictive models for rail-wagon detention in food grain logistics: a technological intervention

This work deals with the movement of food grains in India undertaken by a food grain procurement and storage organisation. The movement is primarily achieved through the railway network, followed by the road network. The scope of the work is confined to the movement of food grains in Kerala region through railway network. This work applies machine learning algorithms to predict the occurrence of rail-wagon detention in the warehouses. Classification models are developed to predict the occurrence of detention at warehouses, and regression models are developed to predict the detention hours, based on the historical data. Popular algorithms used in this work are logistic regression, k-Nearest Neighbour, Naive Bayes, decision tree, random forest, support vector machine and multiple linear regressions. Various performance parameters are used to evaluate the different models, and the best model is chosen for further prediction.

Modelling supply chain network for procurement of food grains in India

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision.