Heterogeneous information exposure and technology adoption: the case of tissue culture bananas in Kenya

Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low. We employ the average treatment effects approach to account for selection bias and extend it by explicitly differentiating between awareness exposure (having heard of a technology) and knowledge exposure (understanding the attributes of a technology). Using a sample of Kenyan banana farmers, we find that estimated adoption parameters differ little when comparing the classical adoption model with one that corrects for heterogeneous awareness exposure. However, parameters differ considerably when accounting for heterogeneous knowledge exposure. This is plausible: while many farmers have heard about TC technology, its successful use requires notable changes in cultivation practices, and proper understanding is not yet very widespread. These results are also important for other technologies that are knowledge‐intensive and require considerable adjustments in traditional practices.     

A conceptual framework of adoption of an agricultural innovation

In this paper we present a conceptual framework of individual farmers' decisions on adoption of a new innovation, using the example of a new crop species. This framework overcomes the shortcomings of a number of previous studies. It represents the adoption of an innovation as a dynamic decision problem spanning at least several years. The model allows for generation of potentially valuable information from trialing the innovation. The value of such trials is due to development of skills (e.g. in agronomic management of a crop) as well as reduction in uncertainty about the innovation's long-term profitability. The framework also includes the farmer's personal perceptions, managerial abilities and risk preferences in order to properly represent the adoption decision process. The influences of socio-demographic factors within the framework are discussed.     

Risk, uncertainty, and learning in adoption of a crop innovation

Risk and uncertainty have often been suggested as causes of poor adoption of rural innovations, but empirical evidence has been scarce. This study focuses on a new crop‐type, chickpeas, in Western Australia to gather such evidence. The empirical models developed are based on a theoretical framework that conceptualizes adoption as a dynamic decision process involving information acquisition and learning‐by‐doing by growers who vary in their managerial abilities, risk preferences, and their perceptions of the profitability and riskiness of the innovation. Learning encompasses improvements in skill as well as reductions in uncertainty. An annual face‐to‐face survey of over 100 farmers was conducted over 3 years, eliciting the farmers' risk attitudes and their subjective distributions of yields and prices. Two limited dependent variable models, Tobit and Probit, are used to estimate the empirical model. There is a high degree of goodness‐of‐fit for both models. The study provides strong empirical support for the primarily economic character of the adoption decision, and highlight the importance of economic risk in the process. The two risk‐related factors with greatest impact on the adoption decision were risk aversion and relative riskiness of the innovation. Risk aversion tended to reduce adoption, and to do so to a greater extent as relative riskiness and scale increased. Results also reveal the key role that trialing of the innovation plays in adoption.     

WOOL-GROWER ADOPTION OF SALE BY SAMPLE

Adoption levels of the innovation, sale of wool by sample, were less than complete but still increasing even after the innovation had been available for a number of years. A model that offers an explanation of the innovation process by individual growers is discussed and tested using econometric methods. The model is converted to testable form, given the data available, with the help of techniques used in studies of demand for travel. Variables found to have a significant influence on adoption levels include grower characteristics, which influence the profitability of innovation, and search variables such as contact with other growers and policies of wool brokers.     

Decision Process for Adoption of Innovative Products in the European Seafood Market: The Importance of Supply and Demand Factors

This study proposes a new approach with two-dimensional point of view to test predicting factors that are influencing European trader decisions for the adoption of innovative seafood products in the distributor channels. A combination of two main predicting factors for adoption is investigated in the “supply factors” (4Ps: product, promotion, place, and price) and the “five innovation demand factors” (5IDs: relative advantage, compatibility, complexity, trialability, and observability). The findings indicate that 4Ps and 5IDs have a significant effect as controlling variables in the decision-making process for innovation adoption. This fact shows a strategic function and illustrates that supply–demand factors have a pressure effect on the innovation adoption process in the market. The results are important both for satisfying consumer expectations and for industrial innovation strategies.     

Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model*

The main objective of this paper is to evaluate and analyse technical efficiency (TE) levels for hillside farmers under different levels of adoption of soil conservation in El Salvador and Honduras. A switching regression model is implemented to examine potential selectivity bias for high and low level adopters, and separate stochastic production frontiers, corrected for selectivity bias, are estimated for each group. The main results indicate that households with above-average adoption show statistically higher average TE than those with lower adoption. Households with higher adoption have smaller farms and display the highest partial output elasticity for land. Constraints in the land and credit markets are likely explanations for these differences. In addition, all estimated models show that TE has a positive and significant association with education and extension.     

Technology characteristics,farmers' perceptions and adoption decisions: A Tobit model application in Sierra Leone

The objective of this paper is to test the hypothesis that farmer perceptions of technology-specific characteristics significantly condition technology adoption decisions. Earlier adoption studies have not considered this in the analysis of the determinants of adoption decisions. The omission of farmers' evaluation of technology-specific attributes may bias the results of factors conditioning adoption choices. A Tobit model was used to test this hypothesis using a stratified random sample of 124 mangrove swamp rice farmers in Sierra Leone. The issue investigated is the adoption of improved mangrove swamp rice varieties. The estimated model results show that farmer perceptions of the technology-specific attributes of the varieties are the major factors determining adoption and use intensities. Indicators of adoption determinants traditionally used in adoption-diffusion studies were found not to be important in driving adoption decisions. Therefore, there is need for adoption studies to consider farmers' perceptions of technology-specific attributes in the assessment of technology adoption decisions.     

DISTANCE TO INFORMATION SOURCE AND THE TIME LAG TO EARLY ADOPTION OF TRACE ELEMENT FERTILISERS

Some hypotheses about the timing of farmers becoming aware of an innovation and the subsequent decision to use that innovation are derived from a recently developed, decision-theoretic model of the adoption process. They are tested using empirical evidence on the time taken by early adopters of trace element fertilisers in S.A. to discover and decide to use this innovation. The central role of information search in the adoption process is emphasised and it is postulated that various distance measures provide a useful measure of information availability and reliability. The results of the empirical analysis are consistent with the hypothesised relationships. Another finding is the importance of distinguishing between early adopters who are genuinely innovative, and those potential later adopters who adopt early because they happen, by chance, to operate a farm in close proximity to another early adopter.     

Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta-analysis

Despite a wealth of case-specific insights from agricultural adoption studies, we lack systematic evidence on which technology characteristics matter for adoption across different innovation contexts. We synthesise the results of 304 quantitative farm-level adoption studies for a wide range of agricultural innovations across more than 60 countries using multi-level meta regression. Our results show that land, capital and knowhow are generally more important when an innovation uses the respective factor intensively, but this effect is reduced in contexts where the factor is abundant. Our findings have implications for the design of rural development and agricultural extension programmes. Both should consider the interplay of geographic context and innovation characteristics to develop more effective sustainable intensification strategies.     

Interaction between crop insurance and technology adoption decisions: The case of wheat farmers in Chile

This paper examines relationships between crop insurance and input technology decisions among Chilean wheat farmers. Using nationwide farm‐level data, a bivariate probit model is estimated. We investigate the extent to which the adoption of production input technologies is associated with farmers’ participation in the insurance program. We find that relationships between insurance and technology decisions are significant only for family farmers. In particular, there is a negative relationship between participation in the insurance program and the adoption of modern irrigation. Interpretations based on the role of input technologies on insurance adoption and adverse selection behaviours are discussed.     

Issues in benefit-cost analysis of agricultural research projects

Use of benefit-cost analysis for economic comparison of agricultural research projects remains confounded, inter alia , by lack of rigour in specifying the without-project scenario and how benefits from an innovation endure after its adoption declines. Failure to account for the without-project scenario favours projects to the extent that more benefits are foregone than costs avoided. Moreover, it is unreasonable to assume generally that aggregate benefits from an innovation continue at the peak level until the end of a 30–40 year planning horizon. A general BCA model for agricultural research projects is presented to enable flexible handling of these issues.     

A TEST OF BAYESIAN LEARNING FROM FARMER TRIALS OF NEW WHEAT VARIETIES*

In this study, elicited estimates of farmers' subjective beliefs about the mean and variance of wheat variety yields were used to test propositions about Bayesian learning developed in the recent literature on innovation adoption. A series of empirical tests of the Bayesian adoption model were conducted using beliefs elicited from farm surveys conducted in 1982, 1983 and 1984. The results of the analysis neither confirm nor reject the Bayesian approach as a model of how farmers revise subjective beliefs, but do raise serious doubts about its realism, and suggest some issues requiring further investigation. Shortcomings in the elicitation techniques are discussed and the assumptions of the Bayesian model are reviewed.     

Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies

A model of farmer decision making is developed to determine the extent to which uncertainties about soil fertility and weather affect the value of site-specific technologies (SSTs) using jointly estimated risk and technology parameters. Uncertainty can lead risk-averse farmers to apply more fertilizers and generate more pollution than in the certainty case. Ignoring uncertainty and risk aversion would overestimate the economic and environmental benefits of SSTs and underestimate the subsidy required to induce adoption. Accounting for uncertainties and risk preferences might explain the low observed adoption rates of SSTs. Improving the accuracy of SSTs would increase the incentives for adoption.     

Cotton farmers’ willingness to pay for pest management services in northern Benin

This study was carried out to assess cotton farmers’ willingness to pay (WTP) for pest management services in northern Benin. Targeted staggered control (TSC) has been introduced to reduce pesticide use in cotton cropping and generate an estimated benefit of FCFA48,800 (€74.40) per cotton hectare accruing from increased productivity and reduced pesticide cost. However, TSC application requires extra time for pest identification and scouting, and its adoption remains low due to the lack of funding to boost farmers’ awareness and cover training costs. An interval regression model was used to analyze responses to a double‐bounded contingent valuation survey with data collected from 300 cotton farmers. The results showed that 87.3% of cotton farmers were willing to pay for TSC services. Annual WTP per cotton hectare was estimated at FCFA16,962 (€25.80), revealing an existing demand for TSC adoption. Respondents' WTP was driven by farm and socio‐economic characteristics. Financial mechanisms managed by farmers could thus potentially foster technology adoption, and in turn, generate economic and environmental benefits.     

Role of information in the adoption of best management practices for water quality improvement

This study investigates the role of information in influencing the adoption of improved farm management practices. A lack of producer information regarding both the profitability and the environmental benefits of adopting improved practices may be a reason why widespread adoption of these practices has not occurred. Compared to direct regulation or financial incentives, raising producer information levels may be a more cost-effective method of increasing adoption. The United States Department of Agriculture has recently established and begun implementing a program based on this idea. To test the validity of the program, a two-stage adoption model is specified and estimated using data from a survey of producers in the program area. The results indicate that producer perceptions Play an important role in the decision to adopt. Changing these perceptions by means of an educational program may be a reasonable alternative to financial incentives in encouraging BMP adoption.     

Adoption of agricultural innovations as a two-stage partial observability process

In this paper, we reconsider the appropriateness of certain statistical analyses in innovation adoption studies and suggest that partial observability models may sometimes be more useful. The proposed models allow for a flexible specification of the process of adoption from one stage to two stages, facilitate the modelling of non‐adopters and remedy the violation of the assumption of full information. An application to the adoption of organic cultivation in Greece demonstrates the relative merits of the proposed analysis.     

Models of land use regulation adoption

Two basic models of community support for the adoption of land use controls have been developed. One model is based upon the sociological theory of innovation diffusion while the other comes from the economic theory of market failure. The relationship of each of these models to the constitutional legitimacy of land use regulation in the USA is significant, and the models may provide a theoretical framework for judicial decision making in cases where property owners affected by controls demand compensation under the taking clause of the US Constitution.     

THE RETURNS TO INVESTMENT IN RESEARCH ON AUSTRALIAN WOOL PRODUCTION*

A production function approach is used to estimate growth in farm productivity in the Australian wool industry from an estimated level of expenditure on wool production R & D. A market equilibrium model of the wool industry is then used to measure the share of total benefits from this productivity growth accruing to Australia and its wool growers. A net return is estimated after allowing for lags in the development and adoption of technology.     

Ex post impacts of improved maize varieties on poverty in rural Ethiopia

Public agricultural research has been conducted in Africa for decades. While many studies have examined its aggregate impacts, few have investigated how it affects the poor. This paper helps fill this gap by applying a new procedure to explore the ex post impacts of improved maize varieties on poverty in rural Ethiopia. Plot‐level yield and cost changes due to adoption are first estimated using instrumental variable and marginal treatment effect techniques where possible heterogeneity is carefully accounted for. A backward derivation procedure is then developed to link treatment effect estimates with an economic surplus model to identify the counterfactual household income that would have existed without improved maize varieties. Poverty impacts are finally estimated by exploiting the differences between observed and counterfactual income distributions. Improved maize varieties have led to a 0.8–1.3 percentage drop of poverty headcount ratio and relative reductions of poverty depth and severity. However, poor producers benefit the least from adoption due to the smallness of their land holdings.     

Energy‐saving Technology Choices by Dutch Glasshouse Firms

This paper estimates a sequence of energy‐saving technology choices by Dutch glasshouse firms. The model allows for time‐constant, firm‐specific effects and serial correlation of errors and it is estimated on panel data over the period 1991–1995. The unobserved error sequences are simulated in the model in such a way that they are consistent with the observed choices. The results suggest that positive revenue shocks encourage adoption of new technologies. The choices also exhibit firm‐specific persistency over time. An increase of the price of energy encourages the adoption of a highly efficient energy‐saving technology. The probability of investing in energy‐saving technologies also decreases with capital invested in structures (e.g. double glazing of glasshouses) and increases with capital in energy installations.