Fertilizer Adoption Research Articles

Education and Technical Efficiency during the Green Revolution in Pakistan

Previous articleNext article No AccessEducation and Technical Efficiency during the Green Revolution in PakistanRauf A. AzharRauf A. Azhar Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Economic Development and Cultural Change Volume 39, Number 3Apr., 1991 Article DOIhttps://doi.org/10.1086/451894 Views: 55Total views on this site Citations: 21Citations are reported from Crossref Copyright 1991 The University of ChicagoPDF download Crossref reports the following articles citing this article:Iskid Jacquet, Jieyong Wang, Jianjun Zhang, Ke Wang, Sen Liang An Understanding of Education in Supporting Cotton Production: An Empirical Study in Benin, West Africa, Agriculture 12, no.66 (Jun 2022): 836.https://doi.org/10.3390/agriculture12060836Jacques C. Julien, Boris E. Bravo-Ureta, Nicholas E. Rada Assessing farm performance by size in Malawi, Tanzania, and Uganda, Food Policy 84 (Apr 2019): 153–164.https://doi.org/10.1016/j.foodpol.2018.03.016, Kanyi Eing, Sanhei Nhor Determinants of Agriculture in Cambodia, Journal of Advanced Agricultural Technologies 6, no.22 (Jan 2019): 91–95.https://doi.org/10.18178/joaat.6.2.91-95Kirtti Ranjan Paltasingh, Phanindra Goyari Impact of farmer education on farm productivity under varying technologies: case of paddy growers in India, Agricultural and Food Economics 6, no.11 (May 2018).https://doi.org/10.1186/s40100-018-0101-9Cathal O’Donoghue, Kevin Heanue The impact of formal agricultural education on farm level innovation and management practices, The Journal of Technology Transfer 43, no.44 (Dec 2016): 844–863.https://doi.org/10.1007/s10961-016-9529-9A. Narayanamoorthy, N. Devika Economic and Resource Impacts of Drip Method of Irrigation on Okra Cultivation, Journal of Land and Rural Studies 6, no.11 (Nov 2017): 15–33.https://doi.org/10.1177/2321024917731840Sean Ryan, George N. Curry, Emmanuel Germis, Gina Koczberski, Merolyn Koia Challenges to the Democratisation of Knowledge: Status Hierarchies and Emerging Inequalities in Educational Opportunities Amongst Oil Palm Settlers in Papua New Guinea, (Jun 2016): 123–139.https://doi.org/10.1007/978-981-10-0216-8_9Abdul Jalil, Muhammad Idrees Modeling the impact of education on the economic growth: Evidence from aggregated and disaggregated time series data of Pakistan, Economic Modelling 31 (Mar 2013): 383–388.https://doi.org/10.1016/j.econmod.2012.11.035Malte Reimers, Stephan Klasen Revisiting the Role of Education for Agricultural Productivity, American Journal of Agricultural Economics 95, no.11 (Jan 2013): 131–152.https://doi.org/10.1093/ajae/aas118Khalid Zaman, Muhammad Mushtaq Khan, Mehboob Ahmad, Rabiah Rustam The relationship between agricultural technology and energy demand in Pakistan, Energy Policy 44 (May 2012): 268–279.https://doi.org/10.1016/j.enpol.2012.01.050Munir A. Hanjra, Tadele Ferede, Debel Gemechu Gutta Reducing poverty in sub-Saharan Africa through investments in water and other priorities, Agricultural Water Management 96, no.77 (Jul 2009): 1062–1070.https://doi.org/10.1016/j.agwat.2009.03.001Elan Satriawan, Scott M. Swinton Does human capital raise farm or nonfarm earning more? New insight from a rural Pakistan household panel, Agricultural Economics 36, no.33 (May 2007): 421–428.https://doi.org/10.1111/j.1574-0862.2007.00217.xAriel Dinar, Giannis Karagiannis, Vangelis Tzouvelekas Evaluating the impact of agricultural extension on farms' performance in Crete: a nonneutral stochastic frontier approach, Agricultural Economics 36, no.22 (Mar 2007): 135–146.https://doi.org/10.1111/j.1574-0862.2007.00193.xAbay Asfaw, Assefa Admassie The role of education on the adoption of chemical fertiliser under different socioeconomic environments in Ethiopia, Agricultural Economics 30, no.33 (May 2004): 215–228.https://doi.org/10.1111/j.1574-0862.2004.tb00190.xJan P. Voon Measuring social returns to higher education investments in Hong Kong: production function approach, Economics of Education Review 20, no.55 (Oct 2001): 503–510.https://doi.org/10.1016/S0272-7757(00)00021-2Theocharoula Magoula, George Psacharopoulos Schooling and monetary rewards in Greece: an over-education false alarm?, Applied Economics 31, no.1212 (Dec 1999): 1589–1597.https://doi.org/10.1080/000368499323111Kwasi Boahene, Tom A.B Snijders, Henk Folmer An Integrated Socioeconomic Analysis of Innovation Adoption, Journal of Policy Modeling 21, no.22 (Mar 1999): 167–184.https://doi.org/10.1016/S0161-8938(97)00070-7Richard V. Llewelyn, Jeffery R. Williams Nonparametric analysis of technical, pure technical, and scale efficiencies for food crop production in East Java, Indonesia, Agricultural Economics 15, no.22 (Nov 1996): 113–126.https://doi.org/10.1111/j.1574-0862.1996.tb00425.xGeorge Psacharopoulos Returns to investment in education: A global update, World Development 22, no.99 (Sep 1994): 1325–1343.https://doi.org/10.1016/0305-750X(94)90007-8Ricardo Godoy The Effects of Rural Education on the Use of the Tropical Rain Forest by the Sumu Indians of Nicaragua: Possible Pathways, Qualitative Findings, and Policy Options, Human Organization 53, no.33 (Sep 1994): 233–244.https://doi.org/10.17730/humo.53.3.e221170878h4701uBoris E. Bravo‐Ureta, Robert E. Evenson Efficiency in agricultural production: the case of peasant farmers in eastern Paraguay, Agricultural Economics 10, no.11 (Jan 1994): 27–37.https://doi.org/10.1111/j.1574-0862.1994.tb00286.x

Adoption of new technologies in Ethiopian agriculture: The case of Tegulet-Bulga district Shoa province

Abstract Adoption of agricultural production technologies in developing countries is influenced by a wide range of economic and social factors as well as physical and technical aspects of farming and the risk attitude of farmers. It is important to understand the role of these factors to ensure the development of appropriate technologies and the design of successful development projects. This study examines the impact of such factors on the adoption of single-ox, fertilizer and pesticide technologies as part of a post-drought recovery project in Tegulet-Bulga district in Ethiopia. Models to evaluate the probability of adoption are specified for the respective technologies and are estimated using a logit maximum likelihood procedure, results indicate that the most significant variable affecting the probability of adoption of all three technologies is farm size; the impact is negative for single-ox technology and positive for fertilizer and pesticide use. Economic factors such as income, wealth and debt generally exhibit statistically significant influence on the adoption of single-ox and pesticide technologies as do family size, access to outside information, education and experience. The effect of socio-economic factors on adoption of fertilizer and pesticide technologies is greater in the area which has more access to outside information and off-farm activities (Ankober) than in more ‘self-contained’ area (Seladengay). The impact of the degree of risk aversion of farmers is found to be significant and negative for single-ox technology in both areas, and for fertilizer and pesticide technologies in only one area. The predicted probabilities of technology adoption by an average farmer are found to increase dramatically with the level of education and access or exposure to outside information.

Adoption of New Technologies in Ethiopian Agriculture: The Case of Tegulet‐Bulga District, Shoa Province

AbstractAdoption of agricultural production technologies in developing countries is influenced by a wide range of economic and social factors as well as physical and technical aspects of farming and the risk attitude of farmers. It is important to understand the role of these factors to ensure the development of appropriate technologies and the design of successful development projects. This study examines the impact of such factors on the adoption of single‐ox, fertilizer and pesticide technologies as part of a post‐drought recovery project in Tegulet‐Bulga district in Ethiopia.Models to evaluate the probability of adoption are specified for the respective technologies and are estimated using a logit maximum likelihood procedure, results indicate that the most significant variable affecting the probability of adoption of all three technologies is farm size; the impact is negative for single‐ox technology and positive for fertilizer and pesticide use. Economic factors such as income, wealth and debt generally exhibit statistically significant influence on the adoption of single‐ox and pesticide technologies as do family size, access to outside information, education and experience.The effect of socio‐economic factors on adoption of fertilizer and pesticide technologies is greater in the area which has more access to outside information and off‐farm activities (Ankober) than in more ‘self‐contained’ area (Seladengay). The impact of the degree of risk aversion of farmers is found to be significant and negative for single‐ox technology in both areas, and for fertilizer and pesticide technologies in only one area. The predicted probabilities of technology adoption by an average farmer are found to increase dramatically with the level of education and access or exposure to outside information.

The study of socio-economic factors influencing fertilizer adoption decisions in Nigeria: A survey of Oyo State farmers

An attempt has been made in this paper to study the socio-economic factors influencing the adoption of farm innovations in Oyo State of Nigeria. The framework is the simultaneous treatment of adoption decisions in respect of farm innovations. A multistage-random-sample of farmers was studied using multivariate probit analysis. Results emanating from the study reveal that infrastructural and economic factors play significant roles in adoption decisions. Inappropriateness of certain innovations to farmers' circumstances was also identified as militating against adoption.

ADOPTION OF MODERN VARIETIES AND FERTILIZER USE ON RICE IN THE EASTERN TARAI OF NEPAL

Factors influencing the adoption of MV (modern variety) rices and fertilizer in the Tarai of southeastern Nepal were examined through multivariate probit (varietal adoption) and tobit (fertilizer adoption and use rates) regression models. This study reaffirms the finding that the adoption of modern rice technology is highest where irrigation exists. Tenure status and access to credit were significantly related to varietal adoption but family and farm size and the operator's education were not. Similar variables, in addition to fertilizer price, influenced the probability of fertilizer adoption and use rates on rice.

A Tobit Analysis of the Adoption and Use Rates of Fertilizer on Wheat in the Eastern Tarai of Nepal

A Tobit Analysis of the Adoption and Use Rates of Fertilizer on Wheat in the Eastern Tarai of Nepal

Adoption of Agricultural Innovations in Developing Countries: A Survey

This paper reviews various studies which have provided a description and possible explanation to patterns of innovation adoption in the agricultural sector. The survey points out that the tendency of many studies to consider innovation adoption in dichotomous terms (adoption/nonadoption) may not be appropriate in many cases where the actual decisions are defined over a more continuous range. More attention needs to be given to the socio-cultural and institutional environment in area studies so that their interrelation with economic factors affecting adoption can be inferred. The presence of several interrelated innovations is another aspect that needs to be considered more carefully in future research, since a number of simultaneous decisions may be involved. Furthermore, the possibility of regular sequential patterns in adopting components of a new technological package should be specifically addressed in future studies. Finally, the impact of differential adoption rates on land holding distribution merits attention in future research.

Econometric Evaluation of New Technology with an Application to Integrated Pest Management: Discussion

To analyze technical change in agriculture, economists have had to select from a limited menu of research approaches. The first course or approach, introduced by Griliches, is to treat technical change as the adoption of a particular improved input, such as a crop variety or piece of equipment. Diffusion of the innovation among profit-maximizing firms is modeled as a contagious, stochastic process with a resulting S-shaped curve time. This is primarily a micro model, but often it is applied to secondary data. The second major approach is to examine factor-augmenting technical change by means of production functions or, more recently, with dual cost functions (Binswanger). In these more aggregate models, technical change is merely represented by a time trend variable. There are three other entrees on the research menu: (a) treatment of joint adoption of improved inputs by simultaneous equation models such as adoption of high-yielding grains and fertilizer, (b) explicit inclusion of both price and yield risk with the diversification offered by technology mixes, and (c) models incorporating fixed factors to depict the adjustment cost of adoption. The two studies offered to us today are likely to expand the research menu. The Hall and Duncan (HD) study extends and continues the work of labor economists by applying a qualitative choice model to pest consultant hiring decisions. Zilberman offers a tasty dish that has a novel blend of micro realism and input market rigor. The topping is the potential for aggregating the micro decisions to evaluate the effects of government commodity policies on natural resource utilization. His effective-water could be effective pesticides or any other scarce resource. I will direct my comments to the HD paper. The primary foods-for-thought that HD serve (in about 100 equations) are alternatives to Heckman's self-selection bias model. HD's estimator corrects for both self-selection and sample-proportion bias. Incidently, there have been several modifications offered to the Heckman model, yet its use seems to be widespread because it is easily understood and can be modified to the special problems at hand. Both the self-selection and choice-based biases can occur frequently; the latter problem is often encountered because it usually is less expensive to use observable choices as a basis for sampling. The analysis of the discrete technology choices of individual farmers several years with better econometric models is a research area that is underexploited. HD are to be commended for extending the work of Rodriquez and Burrows, who apply Heckman's model to pest management choices. Unfortunately, the HD study is warmed over in the sense that it uses the IPM choices of about fifty California farmers that have been used for five or six other studies. The data are decisions made in the early 1970s, but now there are new technologies, new pest species in the area, and different relative prices. The ideal farmer adoption sample would be different from that used in the HD study. There is a need for observations on many farmers choosing for several years among the newest technical developments. Multiple years and locations are needed to give variation in relative prices, pest levels, and weather conditions. Fixed factors such as soil fertility and managerial ability should be collected as well as prices of variable inputs, product prices, yields, and any direct adoption costs. Such a survey compiled for several years is very expensive to collect; one can see why good data are reheated.

Household Production in the Philippines: A Non-Neoclassical Approach

The starting point of any economic analysis is a microeconomic theory of the decision-making unit. By providing a set of explicit assumptions regarding individual choices, a microeconomic theory lays the necessary foundation for the explanation of economic behavior. Just as a theory of the firm underlies analyses of market production, a theory of the family underlies analyses of household production. Explanations of long-run trends such as fertility decline grow out of assumptions regarding the nature of household decision making. Developing what they term "the economic approach" to human behavior, Gary Becker and other neoclassical economists forcefully argue that household behavior is motivated primarily by a collective concern for economic efficiency.' They assume that households seek to maximize exogenously given joint utility functions, and they hypothesize that differences in household behavior represent efficient responses to differences in the prices and incomes which households face. This approach provides the foundation for an explanation of fertility decline based on long-run changes in relative prices associated with economic development. Simply stated, the argument runs as follows. As the rate of return to human capital rises and levels of education increase, the cost of rearing children goes up. As higher wages draw women out of the household into the labor force, the opportunity cost of mothers' time goes up, further increasing the cost of children. The demand for children decreases, and fertility levels gradually adjust.2 The assumption that joint utility functions are exogenous constant over time, and vary randomly, if at all, across households, is crucial to this argument. Systematic differences in unobservable joint utility functions could seriously confound the interpretation of observed relationships between changes in household behavior and changes in rela-

Projections of Effects of Modern Inputs on Agricultural Income and Employment in a Community Development Block, Uttar Pradesh, India

AbstractWhile the green revolution has increased food production, its effects on rural employment and income distribution are causing concern. This paper presents empirically based estimates of the impact of new technology on employment and incomes at the local level. Results indicate that although adoption of new sceds, fertilizers, and irrigation increases employment and agricultural wages, it also increases the disparity between incomes of farmers and agricultural laborers, a disparity which will increase further if labor‐saving machinery is introduced. New technology will reduce the disparity between incomes of large and small farmers ir credit and fertilizers are distributed according to area operated.

Cultural and Attitudinal Factors Affecting Agricultural Development in Eastern Nigeria

Previous articleNext article No AccessCultural and Attitudinal Factors Affecting Agricultural Development in Eastern NigeriaDavid R. SmockDavid R. Smock Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Economic Development and Cultural Change Volume 18, Number 1, Part 1Oct., 1969Part 1 Article DOIhttps://doi.org/10.1086/450407 Views: 66Total views on this site Citations: 7Citations are reported from Crossref Copyright 1969 The University of ChicagoPDF download Crossref reports the following articles citing this article:David K. Smith A bibliography of applications of operational research in West Africa, International Transactions in Operational Research 15, no.22 (Mar 2008): 121–150.https://doi.org/10.1111/j.1475-3995.2008.00625.xAbay Asfaw, Assefa Admassie The role of education on the adoption of chemical fertiliser under different socioeconomic environments in Ethiopia, Agricultural Economics 30, no.33 (May 2004): 215–228.https://doi.org/10.1111/j.1574-0862.2004.tb00190.xH. O. M. Iwuji Bibliography on Agriculture and Rural Development in Imo State, A Current Bibliography on African Affairs 21, no.44 (Jun 1989): 375–396.https://doi.org/10.1177/001132558902100403 BIBLIOGRAPHY, (Jan 1976): 141–157.https://doi.org/10.1016/B978-0-08-020533-5.50009-5Gary Palmer The Ecology of Resettlement Schemes, Human Organization 33, no.33 (Sep 1974): 239–250.https://doi.org/10.17730/humo.33.3.k76v182570u52183 Alphonse A. Gintzburger Psychoanalysis of a Case of Stagnation, Economic Development and Cultural Change 21, no.22 (Oct 2015): 227–246.https://doi.org/10.1086/450626JAN B. DEREGOWSKI INDUSTRIALIZATION OF DEVELOPING COUNTRIES: PROBLEM OF SIMPLE SKILLS, Applied Psychology 22, no.11 (Jan 1973): 77–83.https://doi.org/10.1111/j.1464-0597.1973.tb00294.x