Foodgrain Production Research Articles

Regional Disparities in Foodgrain Production: Spatial-temporal Dynamics and Decomposition Analysis in India

Regional Disparities in Foodgrain Production: Spatial-temporal Dynamics and Decomposition Analysis in India

Reducing the true cost of food-based safety nets: evidence from India’s subsidized food program

Public procurement of food plays a pivotal role in determining the production and consumption of various food items. This is particularly true for staple grains in countries such as India, where the government procures over 40% of rice and wheat. This grain is redistributed to approximately 60% of India’s population through the public distribution system (PDS), the national food-based safety net. Although the PDS plays a critical role in ensuring food security for millions of beneficiaries, there is scope for reducing the hidden costs associated with the production of PDS food grain. As costs such as unsustainable use of groundwater and subsidies for power and fertilizers are not accounted for in the ‘financial’ cost of the program, we use true cost accounting to identify, quantify, and monetize these hidden costs associated with the PDS. In 2021–22, the Indian government and PDS beneficiaries spent US$16.5 billion and US$0.9 billion, respectively, on the PDS. However, accounting for hidden costs associated with the production of PDS food grain reveals that the true cost of the PDS is more than twice its financial cost. Using true cost estimates of distributing rice, wheat, and millets through the PDS, we find that replacing one kg of rice with one kg of millets for a quarter of PDS beneficiaries every month has the potential to reduce the true cost of the PDS by US$1.37 billion annually.

An Analytical Study on Growth Performance of Major Food Grain and Oilseed Crops in Context of Food Security: Rajasthan, India

Background: Productivity, as a source of growth has been a significant premise of analytical investigation in economics all along, however it gained an empirical momentum in the recent years only and has become a necessary index to growth. Present paper estimates growth rate of area, production and productivity of food grain and oilseed crops in Rajasthan. Methods: To analyse growth performance of food grain and oilseed crops Rajasthan time series, data on area, production and yield were collected from 1976-77 to 2018-19. The time-series data were collected on area, production and yield of selected crops from the Directorate of Economics and Statistics (DES), Directorate of Agriculture (DOA) and Rajasthan State Agriculture Department. The compound growth rates (CGRs) of area, production and productivity of selected crops in Rajasthan were calculated using the exponential function. Result: The study revealed that production, productivity and area of total food grains was recorded a positive and highly significant annual growth at the rate of 3.18%, 2.19% 0.63% per annum, respectively. The production, productivity and area of total oilseed was observed a positive and highly significant annual growth rate of 7.63%, 2.07% and 4.27% per annum, respectively. The area under barley, gram and sesamum reduced during the study period. Therefore, the technological support through the varietal improvement and extension support is required.

MSP and Cost of Cultivation of Crops in Relation to Mechanisation of Cultivation of Crops

Agricultural cost is the key to finding the farmers’ cost for a particular crop. India is a nation where a sizable section of the population relies on the agriculture sector, yet it is the only industry that consistently experiences challenges and produces at a rate that is lower than what is anticipated. Indian farms must be mechanised in order to boost input usage effectiveness, decrease human labour-intensive tasks, raise production and productivity of food-grains, lower production costs, and handle difficulties with labour shortages in agriculture and timely farm operations. The use of engineering and technology in agricultural operations to perform a task more effectively and increase production is known as farming mechanization. Mechanization will be used in various stages of the cultivation process where the human intervention will be reduced. Improving crop production involves a combination of factors ranging from selecting the right crop varieties and implementing effective agricultural practices to managing resources efficiently and mitigating risks. Mechanization in the process of cultivation involves the use of various machines and equipment to perform agricultural tasks more efficiently and effectively. Here's how mechanization is applied at different stages of the cultivation process: land preparation, planting, weed control, irrigation, fertilization, crop monitoring, harvesting etc.

Rainfall Variability and Crop Planning of Dahod: A Case study from Tribal District in Gujarat, India

India is an agrarian economy mostly depends on monsoon for its crop success. The onset and cessation of monsoon along with unpredictability in weather conditions, reflects the statistics in foodgrain production in this region. Planning of crop requires details on period of dry and wet spell of an area. The present study takes into account Markov chain model for rainfall probability analysis. The mean annual rainfall of Dahod district is 1073 mm with 59 rainy days. Only 41% shows rainfall above normal. 95% of annual rainfall is contributed by South-west monsoon whereas only 4% was contributed by North-east monsoon followed by 0.9% and 0.2% in summer and winter season. Analyzed data (1998-2019) reveals that monsoon start effectively from 25th SMW and cease on 40th SMW. The average monsoon period is of 16 weeks, i.e., 112 days. Initial, conditional and consecutive probability of dry and wet week indicates a shift in weather pattern around 20th SMW, transition of dry to wet weeks. More than 75% of wet probability can be observed from 27th to 35th SMW. Onwards 40th SMW, likelihood of P(D/D) keeps increasing up to 52 SMW. P(W/W) probability more than 75% can be observed from 27th to 32nd SMW. The district’s priority lies in poultry farming and vegetable cultivation along with sustaining soybean and chickpea. The cropping pattern of majority of district falls in maize-maize, maize-chickpea, maize-wheat-green gram, soybean-wheat-green gram and pigeon pea- groundnut, respectively. The area frequently experiences extended dry spells between monsoon. To address these challenges, crop diversification and efficient irrigation practices are prioritized.

The Effects of Structural Transformation of the Indian Economy on the Land Use Pattern

Due to high density of population the per capita availability of land in India is comparatively less than the rest of the world. As a result, issues concerning the effective and efficient use of scarce land resources are becoming increasingly important in the Indian context. Importance of agriculture in the economy has been declining in terms of the sector’s share in GDP. On the other hand, the importance of the service sector is rapidly growing. Urbanisation has also increased significantly. Given these trends, it is quite possible that the land-use pattern in India has also been changing over the years in response to the structural changes that its economy has been going through. With this possibility in mind, the paper aims to examine whether changes in sectoral composition of the economy are reflected in the country’s land use pattern also. Further, it explores if changes in the sectoral land shares have any correspondence with comparative sectoral land productivity trends. Data on Gross Value Added (GVA) and Land -Use Pattern, accessed from relevant websites of the Government of India have first been processed by computing ratios and percentage, and drawing trend lines. Further, econometric methods have been used to estimate annual compound growth rates. Results show that agriculture has been able to sustain overall growth, including an increase in food-grain production, despite stagnations in its land share. Non-agricultural sectors have been able to claim increasing shares of land from the reduction of barren, unculturable, and cultivable wastelands. But the important point to note here is that the land-share of non-agriculture has been increasing not at the expense of agriculture but from progressively bringing to use lands which were previously not under any economic use. This is a positive development in economic use of the country’s limited land resource.

Growth and development of winter wheat when using growth regulators with retardant properties under conditions of the Non-Chernozem belt

High efficiency of using growth regulators is achieved only against the backdrop of balanced plant nutrition and a high level of agricultural technology. The use of growth regulators alone without balanced mineral nutrition of agricultural plants does not give the desired results. Thus, due to lodging of plants we can lose from 10 to 50 % of the cultivated products. As a result, in the country as a whole the shortage of grain can reach up to 15 million tons, which negatively aff ects the production of food and feed grain. The introduction of new progressive technologies using retardants is highly relevant, because will allow us to obtain stable grain yields even under unfavorable weather conditions. The purpose of the research was to identify the effectiveness of using growth regulators with retardant properties of a new generation when growing winter wheat with diff erent seeding rates, as one of the elements of agricultural technology under the conditions of the Central region of the Non-Chernozem belt of the Russian Federation. Under the conditions of the Central region of the Non-Chernozem belt of the Russian Federation the infl uence of growth regulators with retardant properties of a new generation on the productivity of winter wheat variety Nemchinovskaya 17 and the degree of plant lodging was studied in a multifactorial experiment. Features of the growth and development of winter wheat plants of Nemchinovskaya 17 variety, the formation of the main structural indicators and yield under the infl uence of growth stimulants with retardant properties were revealed. It was established as a result of the conducted research that when cultivating winter wheat under the conditions of the Central region of the Non-Chernozem belt of the Russian Federation on sod-podzolic, medium loamy soils, it was recommended to use the growth regulator with retardant properties Raggi at a dose of 1,5 l/ha in the tillering phase – the beginning of booting with a working solution consumption of 300 l/ha. When cultivating we recommend to use a sowing rate of viable seeds of 5,5 million/ha.

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.

Dynamics of Foodgrains Production and Decomposition of Output Components of Foodgrains in Bihar

"Bihar stands in seventh place in foodgrain production in the country. It produced 15.38 million tonnes in 2020-21 from an area of 6.36 million hectares. Production of cereals was 15 million tonnes, from which rice accounts for 6.74 million tonnes and maize accounts for 2.08 million tonnes of production, but pulses contribute only 0.38 million tonnes. This paper calculated the growth rate, instability index, and decomposition analysis of the area, production, and yield of food grains in Bihar. The study period was from 1991-92 to 2020-21. It was divided into three decades. The area under foodgrains was showing a negative growth rate every decade. Production showed an increasing growth rate in the first and third decades, but in the second decade, it showed a negative growth rate. The yield of foodgrains showed a positive growth rate every decade, which was the main source of production growth. Cereals followed a similar trend as food grains, but the scenario was somewhat different for pulses. Pulse area and production showed a negative growth rate every decade, but its yield showed a positive growth rate in the first two decades and a negative growth rate in the last decade. In cereals, the rice area showed a negative growth rate in all the decades, but production and yield showed a positive growth rate in the first and third decades and a negative growth rate in the second decade. The area under maize showed a negative growth rate in every decade except in the second decade, where it was found a positive growth rate. Production of maize showed a positive growth rate in all the decades though its yield also had a positive growth rate in the first and third decades and a negative growth rate in the second decade. The highest instability in the area of foodgrains was observed in the first decade, but production and yield of foodgrains showed the highest instability in the second decade. Yield effects of crops were contributing more than area effect in increasing foodgrains production. Net cropped area and gross cropped area in Bihar had decreased. Due to more intensive cultivation, the cropping intensity in Bihar has increased from 138% in 2001-02 to 143% in 2018-19. More effective production methods are used, fallow land is used for the production of pulses, short-duration varieties may be encouraged, and focused efforts from various line departments be made in order to ensure that Bihar is both food and nutritionally secure."

Impact of Various Cropping Systems on the Status and Distribution of Selected Micronutrients in Subtropical Region of Himachal Pradesh, India

Globally, the majority of farmers use various cropping systems. In order to increase the production of food grains, the adaptation of these cropping systems necessitates intensive cultivation, which eventually calls for a greater quantity of macronutrients as well as micronutrients. The availability of micronutrients is greatly influenced by cropping practices, their distribution by profile and chemical pools, and their accessibility to plants. Many crops can reach beneath the soil layers and access the micronutrients with their deep roots, which they require to grow. The present study was conducted to quantify the impact of different cropping systems on DTPA extractable micronutrients viz., Zn, Fe, Cu, and Mn over time. Soil samples were collected from the surface layer (0-15 cm) of four cropping systems i.e., cereal-cereal (CC), cereal-oilseed (CO), vegetable-vegetable (VV), and fodder-fodder (FF). The micronutrient cations were found highest under the VV cropping system when compared with all four cropping systems. The results illustrate that availability of micronutrients is affected by the presence of high organic carbon content and favourable soil reaction.

Impact of Aatmanirbharta (Self-reliance) Agriculture and Sustainable Farming for the 21st Century to Achieve Sustainable Growth

Purpose: Indian Agriculture is 3rd most significant contributor to GDP and more than 50% of the population is dependent on agriculture, Indian agriculture accounts for15% of the country’s Gross Value added to the economy, and agriculture policies and schemes implemented by the government to increase the production, income level of farmers, employment, growth of the rural area, provide Digital agricultural stack in area of IoT, block chain, Geographic Information System(GIS) Kisan Drones, Big data, as per the report of NITI Aayog AI in agriculture would be worth $ 2.6 bn and Agri-Startups promote the circular economy with sustainable farming in the area of Millet, organic farming and vertical farming. It is an important pillar of sustainability and it is recognized as 7 out of 17 Sustainable development goals. Sustainable agriculture farming is the order of the 21st century in India which currently has 4.43 million organic farmers, according to the Economic Survey 2022-23, and a much-needed alternative to conventional for protecting the well-being of the farmers and Society, Economy and Ecosystem. India is Self-reliant in the production of Sugar, Rice, Wheat and food grains. Due to Government Schemes like Atmanirbhar, Minimum Support Price(MPS), PM Kisan Scheme, PM Kisan Samman Nidhi, Pradhan Mantri Fasal Bima Yojana (PMFBY), PM Gati-shakti, National Agriculture Market(e-NAM), Young –Entrepreneurs Accelerator Fund for Agri-Startups, Paramparagat Krishi Vikas Yojana (PMVY), establishing more Farmer producers Organizations(FPO), National Mission for Sustainable Agriculture (NMSA), Agricultural Technology Management Agency(ATMA), NABARD in providing finance for farm and non-farm sector, PM-PRANAAM for protecting mother earth. These initiatives are to increase the investment opportunities in agriculture, efficiency, and productivity in agriculture for doubling the income of the farmers. Design/Methodology/Approach: The research article is a descriptive study with exploratory research and secondary data collected from News articles, Journals, Government websites, Books, magazines, and reports for the analysis. Findings: Organic farming and sustainable agriculture is good for the economy and environment. The study reveals that in India Sustainable green revaluation in Agriculture Technology and moving towards achieving sustainable development goals (SDG). Type of Paper: Case Study.

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.

Non-Linear Growth Models for Acreage, Production and Productivity of Food-grains in Haryana

In order to understand the growth patterns of diverse commodities, agricultural research heavily relies on the computation of growth rates. For the computation of the growth rate, many researchers adopted the parametric approach rather than the non-linear model. In this paper, an attempt has been made to develop non-linear growth models for acreage, production and productivity of total (kharif + rabi) food-grains in Haryana from 1966 to 2021. We discussed different non-linear growth model viz. Logistic, Gompertz and Monomolecular and also determined the initial value for each parameter. The parameters were estimated using Levenberg - Marquardt’s iterative method of non-linear regression. Best model was selected based on goodness of fit statistics such R2, RMSE and MAE. Finally we concluded that Logistic model was found suitable to fit for acreage, production and productivity of food-grains grown in Haryana followed by Gompertz model. Forecasting for the period 2022–2026 was done using the selected best non linear growth model.

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.

Performance Evaluation of Low-cost Grain Dryer

Paddy is India’s most significant agricultural crop, accounting for more than 40% of the nation’s overall production of food grains. The moisture content of 10.0 to 12.0 %, which is suitable for safe storage, must be achieved after the drying of paddy. Drying of materials having high moisture content is complicated process, involving simultaneous heat and mass transfer. In this study the performance of grain dryer was evaluated for paddy grains for two different paddy varieties, Rajshree weighed 372 kg and Rajendra Kasturi weighed 366 kg with Initial Moisture Content IMC of Rajshree 14.00 % w.b. (V1) and of Rajendra Kasturi 13.40 % w.b. (V2). Initial Moisture Content (all in w.b.) of variety V1 was 14.00 % and of variety V2 was 13.40 %. It was reduced to the Final Moisture Content (all in w.b.) of 10.10 % for variety V1 and 9.60 % for variety V2. It was found that 1000 grains weight of paddy grains was reduced due to loss of moisture. It is also observed that average length and average breadth of grains decreases with decrease in moisture content for both the varieties of paddy grains. It was found that the bulk density of paddy grains after drying was 578.9 kg/m 3 and 555.58 kg/m3 V1 and V2. Decrease in bulk density is natural phenomenon during drying which was also observed here. It was observed that percent moisture reduction was lower at initial stage of drying for variety V1, which became higher at later stage of drying. However, the percent moisture reduction was higher at initial stage of drying for variety V2, which became lower at later stage of drying. Paddy was dried up to 10.10 % and 9.60 % for variety V1 and V2 respectively during this time. The drying efficiency for both varieties V1 and V2 was found as 35.36 % and 36.85 % respectively. The heat utilization factor and coefficient of performance was found as 0.948 and 0.052 for variety V1 and 0.960 and 0.040 for V2. Grain dryer is capable of drying paddy grains within reasonable time. Temperature and moisture profiles during paddy drying are found well accepted for drying of paddy. The quality of dried paddy grains was reasonably good and acceptable. Considering the level of technology and the capacity, the dryer meets the requirements of paddy traders and farmers of Bihar.

A machine learning approach to assess implications of Climate Risk Factors on Agriculture: The Indian case

The paper examines the direct and indirect implications of the risk factors relating to climate change on various parameters of agricultural production/productivity in India. India, being an emerging economy with considerable dependence on agriculture both for food security and employment generation, offers an important case study for understanding the macro-economic issues and designing the right set of policy approach for mitigating implications of climate change. Furthermore, unlike other central banks, the Reserve Bank of India (RBI) shares a close association with agriculture owing to the continued credit support to the sector as part of its priority sector lending policy, and because it is closely involved in addressing climate change. The paper focuses on the decade of 2010s, the recorded warmest decade till now and adopts a machine learning approach (sequential multivariate adaptive regression splines model) to assess the interaction between climate risk factors and agriculture. To the best of our knowledge, the modelling approach applied in this paper is unique, novel and of the first kind to assess the implications of climate change on agriculture. The results indicate that carbon dioxide (CO2) emission, precipitation, irrigation water used, and rainfall are the most prominent factors affecting different parameters of agricultural production. These factors are taken at the yearly aggregate level and their interactions among themselves, particularly CO2 emissions, affect productivity of foodgrains and oilseeds, providing a detailed insight about the recent decade of climate change in the context of Indian economy.

Food Grains Production in Nagaland: An Evaluative Study

The present study attempts to analyze the trends in the area, production and productivity of major food grain crops in the state of Nagaland for a period of ten years i.e., from 2011-12 to 2020-21. Statistical tools such as average, percentage and line charts have been used to study. To measure the variability in the area, production and productivity of major food grains, Coefficient of variation (CV) has been computed. Compound Annual Growth Rate (CAGR), has also been used to estimate the growth in the area, production and productivity of food grains in the state. The finding shows that the production and productivity of food grains have experienced an upward trend, registering a percentage increase of 24.50 percent and 13.26 percent, respectively. Cultivation of food grains occupies more than 75 percent of the total cropped area throughout the period. Under food grain area, cultivation of cereal occupied more than 66 percent of the total cropped area. Rice being the stable crop of the people occupied more than 47 percent of the total cropped area. The percentage area of jhum paddy and maize to total cropped area witnessed a decreasing trend during the period. The share of agriculture and allied sector in Gross State Value Added (GSVA) has also been decreasing during the period.

B. M. Bhatia. Indian Agriculture: A Policy Perspective. New'Delhi: Sage Publications India Pvt. Ud., 1988.191 pp.Rupees (Indian) 140.00 (Hardbound Edition).

India is self-sufficient in the production of foodgrains, and in good years has substantial exportable surpluses. This is no small achievement for a country which, in the late Sixties, had to import as much as 13 percent of its requirements of foodgrains. The turnaround came as a result of the distribution of high-yielding seeds, fertilizer, modern agricultural technology, and provision of extension services. But agricultural growth has remained concentrated in the north-west of the country, which is well-endowed with infrastructures like irrigation, rural electrification, roads, markets, etc. By one estimate, these areas, which account for less than 15 percent of the total area under foodgrains cultivation in the country, have contributed as much as 56 percent of the increase in foodgrain production in the post-green· revolution period. No doubt, this has led to an increase in the regional disparities as well. Another serious imbalance in Indian agriculture has arisen because of cropwise disparities in growth, between foodgrains and non-foodgrains on the one hand, and among different foodgrains on the other. About 70 percent of the total cultivated area in the country is rain-fed, which contributes a large proportion of the total output of important crops like cereals, pulses, oilseeds, and cotton; and over 40 percent of the total food grains production of the country. But productivity in these areas is low and fluctuates according to the amount of rainfall. Dr Bhatia shows that stagnation in the agriculture sector has coincided not only with adverse weather conditions but also with adverse inputoutput price ratios, particularly between fertilizer and procurement prices of cereals.