Food Grain Production In India Research Articles

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."

Agri-Inputs Consumption Patterns, Access, and Delivery Mechanism in India

The Government of India has initiated several programs to provide timely inputs to farmers with special subsidies on regular inputs such as seed material, fertilizers, irrigation, animal purchase, etc. These inputs are supplied through various mechanisms such as government departments, agricultural universities, KVKs, NGOs, local shop owners, producer's associations etc. Studying the agri-inputs available and their supplying mechanisms in India provides clarity on the status of farmers and the number of farmers who benefi ted from diff erent services provided by GOI. A survey questionnaire was developed to collect data from the 14 states with a sample size of 409 farmer respondents and was administered to the Foundation course for Agricultural Research Service (FOCARS)-82 batch, who were undergoing fi eld experience training (FET) at ICAR-NAARM, Rajendranagar, Hyderabad, Telangana State. The survey revealed that most farmers felt satisfi ed with the quality of seed, followed by agrochemicals and chemical fertilizers. Among all the assets, farmers spent the most money on purchasing animal feed (INR 46047.05) followed by planting material- saplings (INR 34445.07) and adult draught animals (INR 31805.07). The maximum number of respondents specifi ed that storage facility for grains, electricity, and byproducts were suffi ciently available. More money is spent on purchasing the recurrent associated input, i.e., irrigation sprinkler/drip, followed by irrigation wells. The Maximum number of farmers has access to Common Property Resources (CPRs) such as pasture land, irrigation sources, threshing fl oors, water harvesting structures, producer's associations, selfhelp groups, and primary cooperative society. About 269 farmers availed credit facilities; among them, 103 farmers took a credit of >10,000-50,000, which was majorly used for input purchase (240 farmers responded). About 132 farmers said they receive subsidies on seed/propagating material, fertilizer/agrochemicals, and irrigation. Most farmers received quality seed/propagating material from the local shop owners, followed by neighboring farmers, govt. departments and private companies. Very few are using/visiting Agricultural Universities, KVKs, cooperatives, NGOs, Agri-clinics, producers' associations, and village panchayats for seed purposes. In terms of credit suppliers, farmers listed public sector banks fi rst, followed by cooperative society credit cards and moneylender. Farmers with medium-sized land holdings have a better chance of availing subsidies compared to small-category farmers. Most small landholders need more opportunities to access agricultural credit. An adequate supply of timely inputs and support of low-cost credit from institutional sources is of great importance to small and marginal, farmers who contribute almost 60 per cent of total food grain production in India. The output of this study would become a benchmark for future assessments and decision-making in the fi eld of agri-inputs.

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India.

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4years) and 4 to 5 consecutive drought years (once in 120years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R2 (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

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.

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.

Designing a food supply chain for enhanced social sustainability in developing countries

The food grain production in India has progressively risen in the past few decades, whereas the storage capacity has remained limited. The policymakers in India are attempting to close this capacity gap while addressing sustainability objectives. However, the quantification and integration of multiple social sustainability factors have remained a challenge. To improve the overall sustainability, the study attempts to develop a mathematical model considering procurement, transportation, inventory, and location-related issues. Several supply chain network factors are integrated and assessed while focussing on the social sustainability dimension. Three cases of India's largest food grain-producing and consuming states are analysed with the help of two Pareto-based algorithms. Multiple relationships between variations in supply, demand, and the capacity of silos with three defined objectives are evaluated. It is observed that, the demand significantly influences the economic and environmental objectives compared with the supply and silo capacity. The capacity of silos has a more significant impact on social objectives than economic and environmental objectives. Results reveal the importance of establishing a sufficient number of modernised silos, which reduces environmental impact and improves social factors such as farmers’ economic condition and welfare, balanced economic development, number of jobs created, and public health level.

Intelligent Quality Monitoring System for Food Grain Storage Using IoT

A Developing country like India which is among the most important agricultural manufacturer within the globe has about 179.9 million hectares of its land under cultivation. About 70% of the rural population relies on cultivation for their livelihoods, since it is their sole source of income. The total food grain production in India is projected to increase 2% by the year2023 with about 303 million tons being produced each year which is a lot compared to other counties. A major problem stems from the fact that most of these food grains are mass-produced, which causes them to be wasted if they have been damaged by rains, floods, variations of temperature and humidity, attacks of rodents, insects. It is imperative to reduce the number of food grains being wasted, which can be achieved through the application of proper storage techniques. This is accomplished with the aid of automation, such as the Internet of Things (IoT), which enables continuous monitoring and tracking of food grains at storage warehouses. Hence, the proposed method utilizes an IoT System with sensors that collect data such as humidity, temperature, and C02 percentage. The data is notified to the user when parameters change and control measures are taken to ensure that the food grains remain in optimum conditions for the best quality.

Phosphorus management strategies to enhance P-use efficiency and sustainable crop production

With increase in food grain production in India from 50.8 million tonnes (Mt) in 1950-51 to 295.67 Mt in 2019-2020, the production and consumption of phosphorus (P) fertilizer has also increased from 9.8 thousand tonnes in 1950-51 to 4790 thousand tonnes in 2019-2020 and from 6.9 thousand tonnes in 1950-51 to 7464 thousand tonnes in 2019-2020, respectively (FAI 2020). Phosphorus is the second important primary nutrient element after nitrogen. Low P use efficiency is the significant challenge for agricultural production on P-deficient soil as well as in acidic and calcareous soils (Shenoy et al. 2005). Acquisition of soil and fertilizer P by crops depends on soil and plant properties. Soil processes determining P availability to plants are P solubility/sorption, P transport, root/soil contact and mineralization/ immobilization (Horst et al. 2001). Agronomic strategies for enhancing P use efficiency includes selection of fertilizer, soil test based P application, methods of P fertilizer application, fertigation, residual P utilization by different crops, utilization of insoluble P sources by addition of organic matter and phosphate solubilizing microorganisms (PSM), integrated nutrient management (Subba Rao 2010). Improved P use efficiency has been observed by Mengel (1997) in acidic soil by applying P at the time of sowing and liming. The two main strategies which help plants to improve P use efficiency are (i) root-foraging strategies that improve P acquisition; and (ii) P-mining strategies to enhance the desorption, solubilization or mineralization of P from sparingly soluble sources in soil using root exudates (Richardson et al. 2011).

Design and development of compact Foxtail millet deshelling machine

Abstract Millets are cereal crops that fall under the category of small seeded grasses. Millets are the basic cereals in India and eaten by a majority of the rural community, and constitute around 25 percent of the total food grain production in India. Millets are among the most nutritious food grains available in India and are rich source of vitamins, minerals, calcium, fiber and also has low glycemic index. Millets contain a dry external cover called husk/ shell. Removal of the husk is called De-husking/ De-shelling. Manually Deshelling is quite difficult and also requires lot of human effort and there will be damage to the seeds. To overcome these difficulties, machines have been developed. However, the existing/conventional machines have the drawbacks such as larger in size and capital intensive. The maximum number of farmers involved in millet cultivation are small land holding farmers producing millets in small quantity and the existing machines are not suitable for small quantity processing. Hence an attempt is made to overcome the difficulties of small farmers, by developing a small machine suitable for multiple millets. The specification developed included important parameters like Method of Deshelling, type of deshelling, weight of Millets for each batch, diameter of abrasive disc, motor power, distance between the abrasive discs, total weight of the system, operating speed. Different product concepts were developed to meet the developed specifications and the most suitable one was selected. The selected concept consisted of different subsystems like the deshelling mechanism, power system and frame. All these subsystems were synthesized and designed successfully to meet the requirements. The complete fabrication of all the subsystems and final assembly was carried out. The developed compact foxtail millet deshelling machine has been tested for its working and found to be satisfactory.

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.

EFFICIENCY IN FOOD GRAINS PRODUCTION IN INDIA USING DEA AND SFA

Aim: Food security is a multi-dimensional issue and is concerned with aspects such as availability, access and utilisation. It would require major interventions that will transform the current patterns and practices of food production, distribution and consumption. Food security can be attained by increasing the level of agricultural productivity and efficiency and improvements in agricultural efficiency are at the core of the quest for food security. This paper seeks to examine the efficiency in food grains production in India for the period 1960-61 to 2013-14. Design / Research methods: The key idea is to employ the non-parametric Data Envelopment Analysis and the parametric Stochastic Frontier Analysis to measure the efficiency of food grains production in India. We have estimated an input oriented single output, multi – input DEA models (CRS – DEA and VRS- DEA) of agricultural production to measure the efficiency in food grains production for two time periods 1960-61 to 1989-90 and 1991-92 to 2013-14. The analysis of super efficiency was conducted for both these time periods helped identify the years in which food grains production was most efficient. Conclusions / findings: We find high average efficiency in farming operations for both the frontier methods. However, the range of efficiency obtained varies considerably for the different frontier methods. The period after 1990 has witnessed improved agricultural performance as can be inferred from the frequency distribution of the efficiency scores which indicates that during this period the overall efficiency scores have been higher and there was not a single year in which the efficiency levels have been less than 0.9. The analysis of super efficiency also vindicates the improved performance of the agricultural sector in the post 1990 periods as greater number of years recorded an efficiency score greater than 1 as compared to the previous period. However, the super efficiency scores recorded in the period 1961-1990 were higher than those in the post 1990 years suggesting thereby that there could be a tapering of the positive impact of the Green Revolution. Efficiency estimates obtained by the SFA model are marginally lower than that of the DEA model and the results of the SFA model indicate net sown area, net irrigated area and pesticides to be statistically significant inputs. Originality / value of the article: This study contributes significantly to the literature on efficiency measurement of agricultural production in India by focussing on efficiency measurement of food grains. Most studies focus on farm level data and /or on individual crops.Implications of the research: This results of this study have implications for the issue of food security in India. Its results indicate that a need to expand irrigation facilities and net sown area to improve efficiency in food grain production which is vital for the issue of food security.

Robust modeling in the presence of outliers for food grain production in India

The traditional ordinary least squares procedure (OLS) is the most frequently used method for analyzing food grain production data (1983-2014), but ignore the presence of outliers or influential data points which may distort the regression estimates obtained from OLS. These data points may remain unnoticed and can have a strong adverse affect on the regression estimates. In this paper, two approaches i.e., robust M-regression and quantile regression to linear robust regression analysis are presented, as these methods provide formal procedure to overcome from the situation of outliers and influential observations and to reduce their influence on the final estimates of the regression co-efficients by using Cobb-Douglas production function. Moreover, 0.90 th quantile regression model comes out to be best on the basis of AIC (-47.17), SBIC (-36.91), elasticity of production, marginal value productivity, sign, size and the variables significant effect on foodgrain production than OLS and robust M-regression. Also, the variables NSA and AC were best in order to increase the food grain production on the basis of quantile 0.90 th regression, elasticity of production and MVP at 0.90 th quantile.

Trends in Fertilizer Consumption and Foodgrain Production in India: A Co-integration Analysis

Food grain production in India has increased considerably from 50 MT in 1951 to 266 MT by 2013-14. Introduction of green revolution, modernization of agriculture, encouragement to research and extension in agriculture are some of the factors contributed for this growth. However, this is accompanied by considerable increase in the usage of chemical fertilizers starting with the introduction of green revolution in 1960s leading to unsustainable agriculture. The Green Revolution was a technology package and it contains technical component of improved high yielding varieties of two staple cereals (rice and wheat), irrigation and use of fertilizers, pesticides, and better management practices. The high yielding varieties demanded more usage of fertilizers and it is supported by the subsidy policy on fertilizers. In the early 1980s, India introduced New Economic Policy (NEP) which has opened up the economy to privatization and globalization. In the globalized economy agricultural sector became more commercial and production is oriented to export market. This also led to increase in fertilizer consumption. The main objective of this paper is to analyse the trends in the consumption of chemical fertilizers and food grain production in India and to identify the relationship between these two. The study is based on the secondary data and Co-integration Technique and CAGR are used to analyse the trends. The analysis revealed long run relationship between these two variables in India. Encouraged by the increased production, farmers have increased fertilizer consumption without considering the environmental consequences and sustainability of agriculture.

Comparative decision models for anticipating shortage of food grain production in India

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage (true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

Coping strategies for extreme weather in dryland agriculture

Dryland areas account for 48% of area under food crop and 52% under non-food crop cultivation and contribute about 42% of total food grain production in India. Drought is the predominant weather extreme influencing the socio-economic structure of not only dry land regions but also the entire country. Various drought monitoring techniques and mechanisms aim at mitigating the drought impacts at different spatial scales. They are discussed briefly here with strategies to cope up this weather anomaly at different time scales. Dry land districts that are prone to frequent hail episodes are identified and measures to minimize damage to agriculture are also discussed.

Comparative analysis of processing conversion in conventional and modern rice mills of southern Tamil Nadu

Rice is the staple food of Asian countries. China and India are the two important rice production centres that supply half of the world’s rice. During 2012, the total food grains production in India reached an all-time high of 259.32 million tonnes. Tamil Nadu ranks fifth in rice production (6.32 lakh tonnes) during the year 2011–2012. Rice milling is the largest agro-based industry wherein the outer layer (husk) and inner layer (bran) of the paddy was removed and rice is obtained. The conversion ratio of paddy depends on the milling efficiency of the unit, technology adopted for paddy processing and quality of the procured paddy. The modern paddy processing unit has more head rice recovery and better utilization of the capacity when compared to the conventional unit.

Nonlinear impact of rain on foodgrain production in India

Owing to a very high population and low land resources, the economic importance of crop production is very high in India. However, in the absence of adequate irrigation facility, crop production of the country largely depends on rainfall during monsoon months. I, therefore, analysed the impact of rainfall on crop production using the ARDL approach of Pesaran et al. (2001). As the impact of rain might have an asymmetric effect on crop production, I analysed such possibility using the nonlinear ARDL model proposed by Shin et al. (2013).

On the association between pre-monsoon aerosol and all-India summer monsoon rainfall

Summer monsoon rainfall which gives 75–90% of the annual rainfall plays vital role in Indian economy as the food grain production in India is very much dependent on the summer monsoon rainfall. It has been suggested by recent studies that aerosol loading over the Indian region plays significant role in modulating the monsoon circulation and consequent rainfall distribution over the Indian sub-continent. Increased industrialization and the increasing deforestation over past few decades probably cause a gradual increase in the aerosol concentration. A significant negative relationship between pre-monsoon (March–May i.e. MAM) aerosol loading over BOB and IGP regions and the forthcoming monsoon rainfall have been observed from the thorough analysis of the fifteen years (1997–2011) monthly Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) and All-India Summer Monsoon Rainfall (AISMR) data. Composite analysis revealed that AI anomalies during pre-monsoon season are negative for excess year and positive for deficient monsoon years over the Indian subcontinent, with strong variation over Bay of Bengal (BOB) and Indo-Gangetic Plain (IGP) regions from the month of March onwards. The correlation coefficients between AISMR and pre-monsoon AI over BOB and IGP regions are found to be negative and significant at 5% level. The study clearly brings out that the pre-monsoon aerosol loading over the BOB and IGP regions has a significant correlational link with the forthcoming monsoon intensity; however a further study of the aerosol properties and their feedback to the cloud microphysical properties is asked for establishing their causal linkage.

Carbon Footprint and Sustainability of Agricultural Production Systems in India

The agriculture sector, which accounts for about 52% of the total workforce despite a steady decline of its share in the gross domestic product (GDP), is still the largest economic sector that plays a significant role in the overall socio-economic development of India. Sustainability of agricultural production systems depends on their carbon (C) footprint and the C output-input ratio. Thus, the present study was conducted with the objectives to: (i) assess C emissions in relation to predominant agricultural systems in India; (ii) evaluate C-use efficiency of production systems; and (iii) determine the relative sustainability of agronomic production systems as determined by their C footprints. The data collated on C-based input into the soil for predominant agricultural and horticultural crops included the amounts of fertilizers (N, P, K), herbicides, and pesticides used for crops annually, cropland area, total production of each crop, water-management practices, energy used for different operations, and total number of livestock. These data were used to compute C equivalent (Ce) per hectare of input and output, and the relative sustainability indices as a measure of the C-production efficiency. Beginning with low C-based input of 69.7 Tg Ce/yr (1 Tg = teragram = 1012g = 1 million ton) in 1960–61, input of fertilizers, pesticides, farm power, feed, fodder, and electricity increased to 281.2 Tg Ce/yr by 2008–09. The output in agriculture increased from 578.6 Tg Ce/yr in 1960–61 to 1239.1 Tg Ce/yr in 2008–09. The C-sustainability index was high in 1960, and was indicative of the minimum usage of inputs prior to the onset of the Green Revolution. Thereafter, the C-sustainability index decreased during 1970s and 1980s because of increased C-based inputs. There existed a linear relationship between C input and C output, indicating that an increase of 1 Tg Ce/yr of C input resulted in a corresponding increase in C output of ∼21 Tg Ce/yr. Total food grain production in India increased from 89 million ton in 1960–61 to 262 million ton in 2008–09.