Misallocation in Indian Agriculture

We exploit substantial variation in land-market institutions across Indian states and detailed micro household-level panel data to assess the effect of distortions in land rental markets on agricultural productivity. We provide empirical evidence that states with more rental-market activity feature less misallocation and reallocate land more efficiently over time. We develop a model of heterogeneous farms and land rentals to estimate land-market distortions in each state. Land rentals have substantial positive effects on agricultural productivity: an efficient reallocation of land increases agricultural productivity by 38 percent on average and by more than 50 percent in states with highly distorted rental markets. Both farm and state-level land market distortions are quantitatively important, with state-level wedges accounting for a significant fraction of rental market participation differences across states. Land market distortions contribute about one-third to the large differences in agricultural total factor productivity across Indian states.

Misallocation in Indian Agriculture

Nearly seven decades after independence, still, various vulnerable areas are persisting in Indian Agriculture over the years like the problems of India farmers in many parts of the country as these areas are prone to low rainfall and drought, as the water level in reservoirs is going down. In case of poor or scanty rainfall, the contingency plan for the affected districts is dormant. Agricultural production and productivity of various crops are absurdly low in dry land areas, Eastern Indian States, Central Indian States and Hilly regions of North-eastern States. The problems of crores of landless agricultural labourers and marginal farmers doing subsistence agriculture are still to be solved. The main source of rural poverty is from these two categories of farmers. But the process of economic liberalization still bypasses these segmentswho arenearly themajority of Indianfarming community. ANationwide Skill Development linked Economic Empowerment of these battered sections of farming community is the need of the hour. Hopefully new Central Government is putting due importance on the aspects such as Skill Development, Economic and Financial Inclusion etc. There is a huge work to be done in the spheres of dairy farming, fishery, soil and water testing, integrated pest management and horticulture.

A salient theme in D. Gale Johnson’s work is the importance of agricultural development for general prosperity and for economic diversification (e.g., Johnson 2000). Johnson has also noted that most of the world’s poor are engaged in farming, so that a key focus of development policy is to raise the incomes of farmers. From a global perspective, increasing the productivity of agriculture, given the fixity of land, is necessary for both poverty reduction and the development of the nonagricultural sector. At the level of the world, agricultural productivity gains, poverty reduction, and the growth of the nonfarm sector are complements. However, the question remains whether these observations imply that every poor country should focus its public resources on agricultural development in order to raise the incomes of people now engaged in farming and whether such a policy is necessary for obtaining economic diversity. In this article, we use the experience of India over the past 30 years to address the issue of whether agricultural technical change actually leads to economic diversification and income growth within the rural sector in the context of an open-economy country in which there are cross-area trade and capital flows. We focus in particular on the rural sector because this is the sector in which linkages between agricultural and nonagricultural sectors are thought to be the strongest. We exploit the fact that India has maintained a policy of openness with respect to agricultural technology over this period, permitting and actively supporting agricultural development, and has moved to a reformed regime in which goods are traded and capital is more mobile in the 1990s. Evidence on the relationship between agricultural growth and nonfarm

Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI- weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI- weather and LAI- weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare, and rice crop yield 0.029 tons per hectare.

PurposeThe present paper makes an attempt to investigate the determinants that affect FDI inflows distribution among Indian states. Together with traditional determinants, the impact of institutional determinants on state-level FDI inflows distribution in India has been analysed.Design/methodology/approachThe study uses panel data for a period of 20 years (2000–2019) for 17 groups of Indian states (29 states and 7 UTs). The empirical evidence is based on the panel data method and the findings support Dunning's OLI theory. As the data for some indicators for the institutional environment is not available at the state level, hence we used component analysis to arrive at the single component for the institutional factor. The study takes into account corruption, legal system, industrial disputes, man-days lost, labour availability, political risk, protection of IPR and agglomeration as potential macroeconomic and institutional determinants.FindingsResults show that FDI inflows into Indian states is driven mainly by institutional environment. From our analysis, the author infers that the institutional variables such as legal system, IPR, corruption, political instability play an important role in determining the distribution of FDI inflows at the state level in India. Together with that GFCF and agglomeration are also important determinants of state-wise FDI inflows.Research limitations/implicationsThe major limitation of the study is that it doesn't include moderated impact of economic and institutional determinants of FDI inflows in Indian states, which can be an avenue for future research. Future research can also carried out taking district-level data to further examine the determinants at district level in India.Originality/valueThe contribution of the present paper is three-fold, first, the author constructs a measure of different institutional variables, after normalization of data for the period 2000–2019, and the author choose the highest explaining factor with the highest variance explained then we constructed the indices for select variable, which further has been used in the panel data analysis technique. The author has found that macroeconomic variables, as well as institutional variables, are significant to attract FDI at the state level in India. The paper shows that corruption, political risk, IPR and legal system are the major institutional determinants of FDI inflows in India at the state level. States with higher domestic investment attract more FDI inflows, moreover, agglomeration is a very important determinant as the investors are more confident in investing at the same location, the reason behind this may be that the investors want to avoid the registration procedure for new land, administrative formalities or they feel more secure at the same place and keen to invest at the same place again.

Institutional and technological changes, mainly the HYV technology introduced in the 1960s, turned India into a progressive food grain producing country. Since the early-1990s, Indian agriculture has also faced competition from the global economy in the wake of globalisation. This has led to the emergence of a decelerating tendency in Indian agriculture especially in terms of growth of production of major crops and total agricultural production. In view of the above, diversification of agricultural activities is considered important for enhancing agricultural production and productivity in India. This study examines cropping pattern changes in Indian agriculture during the period 1970-71 to 2006- 07. In terms of Herfindal index and substitution and expansion effects, the concentration and/or diversification reveals that the cropping pattern in India in terms of allocation of acreage is skewed towards food grains. However, few non-food grain cash crops such as cotton, sugarcane, oilseeds, and vegetables have emerged as popular crops among the farmers in recent years.

Population and Agro Climatic Zones in India: An Analytical Analysis

Modern agriculture, plant breeding, and agrochemicals such as pesticides, fertilizers, and technological improvements have increased agricultural productivity sharply, but at the same time these technological improvements have caused widespread environmental damage and an increased negative impact on human health associated with organic organic consumption. The agricultural practice is a foundation for all global development. Increasing its production and productivity may affect the land capacity in the future if not managed sustainably. Sustainable agriculture could be one way to preserve land use potential. Agriculture could become sustainable with careful management of its components. The major components of sustainable agriculture are building healthy soil and preventing erosion, managing water wisely, increasing carbon sequestration, increasing resilience to extreme weather, and promoting biodiversity. Food and nutrition security could result from sustainable agriculture without harming the environment for future generations. Sustainable agricultural development seeks not only to conserve and conserve natural resources, but also to develop them, because future generations will demand more in quantity and quality in terms of agricultural and food products. Hence, challenges and opportunities were discussed to support decision-making related to land and water resource management as a prerequisite for sustainable development. In the Indian agriculture sector the challenges facing sustainable agricultural development. So, improving agricultural productivity, while preserving and enhancing natural resources, such as water, is a prerequisite for farmers to increase global food supplies on a sustainable basis. The role of small farmers and their families in sustaining sustainable agricultural productivity growth will be crucial.

It has often been argued that the only long-term solution for Indian agriculture is a radical reorganisation of the institutional structure. There are two lines of thought about how we should proceed: the first one believes in a once-for-all change towards socialised agriculture, perhaps along the present Chinese pattern; the second argument is that one should break up the institutional power structure by ceilings on land ownership and the transfer of ownership rights to tenant cultivators.1 The first suggestion is politically unfeasible unless there is a revolutionary change. To evaluate the impact on agricultural production under these circumstances would be difficult, since we would have to separate out temporary (short-run) and permanent (long-run) effects. One possible method would be to do a comparative study of Indian and Chinese agriculture, normalising for all but the institutional differences. This is clearly a difficult task. To evaluate the impact on agricultural production of land reforms is a relatively easier task. The impact of land ceilings can be assessed by looking at whether there is any significant difference between small and large farms, assuming that the components into which a large farm might be split up behave like small farms of that size. The impact on agricultural production of transferring ownership to the tenant cultivators can be studied by looking at the two types of farmers, ‘owners’ and ‘tenants’.KeywordsProduction FunctionAmerican Economic ReviewSmall FarmLand TenureFarm SizeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper aims to provide an analysis of the agricultural productivity of food grains across the states of India. Despite agriculture's diminishing role as a share of overall gross domestic product (GDP) in India, it plays a crucial role by providing a large proportion of jobs to the workforce. Acknowledging the central role of agriculture in the economy as well as the diversity between the states in terms of availability of resources, this study analyses the impact of various inputs like land area, fertilizers, electricity and labour use on the production efficiency of food grains at the state level from 2006 to 2017 using Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). The results highlight the variation in the performance of states with five out of selected 20 states experiencing efficiency under the CCR model of DEA and nine out of 20 states being efficient under the BCC model of DEA. MPI results indicate that 11 out of 20 states have progressed in terms of MPI during the period under consideration. The paper highlights the possibility of increasing agricultural production with optimizing the inputs by focusing on efficient resource deployment and enhanced production management. The paper investigates the relationship between various agricultural inputs and food grain production efficiency using DEA and provides a way to improve on the allocation of these resources across various Indian states.

The present study aims to identify the drivers of innovation for sustainable farming among various models of farming and understand the influence of innovations in sustainable agriculture and their impact in enhancing the sustainable agricultural value chain. This research has been conducted in Uttar Pradesh (UP), which is the largest agricultural producing state of India. Data were collected using questionnaire from 600 farmers of thirty districts in UP and nonparametric test were used to analyse the data in the study. The results document that various R&D based modern and innovative technology/technical know-how has significant impact on agriculture produce of the farmers. It was also found that various organisations play an important role in know-how and knowledge creation among the farmers in India. The findings should be very useful for policymakers to implement R&D advancement in sustainable agriculture and systematic overhaul in agriculture sector. The findings of the study should be very useful for policymakers, practitioners and regulators to promote and implement sustainable agricultural innovation and adaptations in India.

The case of Indian agricultural performance was impressive. The food production and increases in productivity are essential for meeting the growing demands for food in the future. There is widespread opinion that this growing demand can be met by increased use of inputs or increases in agricultural productivity. Productivity growth of agriculture in India over the past four decades was the result of a combination of factors such as new incentives to farmers offered by the government who considered them as autonomous economic agents, and physical factors such as land, labour, capital (in the form of machines, working animals, irrigation system, and so on), and intermediate inputs such as fertilizer. Indian agricultural growth has been less dependent on the conventional inputs of capital. Capital was computed as the sum of the value of agricultural machinery, farm equipment and tools, transport equipment in farm business, land improvements, investments in private and public irrigation, and farm houses in Indian agriculture. As the growth of agriculture increases the importance of conventional inputs of capital becomes lesser in comparison to modern inputs of capital. Since mid 1960s, a package of modern inputs of capital such as high yield variety seeds, chemical fertilizers, tractor etc. has been continuously used with increasing trend in Indian agriculture. This was main cause of the remarkable growth in output of agriculture during 1970s and 1980s decades. This paper is aimed at analyzing the impact of some production variables (input) on agricultural productivity growth (output) in Indian agriculture from 1969-70 to 2005-06. The question here is whether or not these different variables have an impact on agricultural production.

Andrade-Servin, A.G., Guerrero-Garcia Rojas, H.R., Colin-Martinez, R. 2020. Econometric analysis of water availability for agricultural irrigation production in Mexico (2003-2015). Ecosistemas 29(2):1816. https://doi.org/10.7818/ECOS.1816 From an econometric model with panel data, an assessment of agricultural irrigation production (ton) was made with respect to the volume of water used (millions of m3) in the Mexican territory, for 13 consecutive years (2003 to 2015). It should be noted that Mexico has 13 basin organizations, which behaved differently both in agricultural irrigation production and in the amount of water required to obtain agricultural production. The objective of this study was to analyze the behavior of the amount of water required to generate diversity of agricultural irrigation products in Mexico. From the panel data it was shown that agricultural irrigation production is determined by 94% by the availability of water. The percentage change in agricultural production with the use of water ranges from 47 to 60% for each unit of water destined for irrigation. This type of model allows analyzing that the quantity and diversity of agricultural products is closely linked to the availability of water in each basin organization. In addition, consider being more efficient in the use of water for irrigation of agricultural products, use the amount necessary for irrigation, improve irrigation systems to avoid wasting water resources, especially in the northern part of Mexico, with the North Pacific generating the greatest agricultural production and required more water resources.

The VDSA panel dataset (vdsa.icrisat.ac.in) was generated by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in partnership with the Indian Council of Agricultural Research (ICAR) Institutes and the International Rice Research Institute (IRRI). The VDSA has operated over a total period of 40 years from 1975 to 2015 but with discrete periods of data collection. In the most recent period (2009-2014), the period used for this analysis data were collected for a larger number of households and with vastly increased survey efforts focused on detailed data collection covering production information, GPS-measured plots, and 3-weekly household visits to record input and output data for each plot owned/leased by participants. The resultant data set covers the period 2009 and 2015 with 1,129 households participating from 30 villages in 9 states of India (vdsa.icrisat.ac.in/vdsa-map/vdsa-location-map.html). Study sites were selected using a stepwise purposive sampling strategy in order to cover the agro-ecological diversity of the region. The current dataset, based on the VDSA raw data, has been compiled to assess the relationship between farm size and agricultural productivity. The STATA program file (.do file) is also shared along with data. This program imports raw VDSA data and with necessary processing develops the variables needed to run the models to study the relationship between agricultural productivity and plot size. The raw data files for different modules can be downloaded from this dataset or can also be generated from vdsakb.icrisat.ac.in, raw data option, selecting all the available Indian states.