Risk,learning, and technology adoption

This article explores how decision makers learn and use information, with an application to the adoption of biotechnology in agriculture. The empirical analysis relies on experimental and survey data measuring risk preferences, learning processes, and the adoption of genetically modified (GM) seeds among U.S. grain farmers. While controlling for risk aversion, we link individual learning rules with the cognitive abilities of each decision maker and their actual GM adoption decisions. We find evidence that very few individuals are Bayesian learners, and that the population of farmers is quite heterogeneous in terms of learning rules. This suggests that Bayesian learning (as commonly assumed in the analysis of agricultural technology adoption) is not an appropriate characterization. In addition, we do not find a strong relationship between observed learning styles and the timing of GM seed adoption. To the extent that learning is a key part of the process of technology adoption, this suggests the presence of much unobserved heterogeneity in learning among farmers.     

The effect of information sources on technology adoption and modification decisions

This article focuses on adoption and modification decisions on improved maize storage technologies in southern Benin. Modification implies changing a technology to adapt to farmers' circumstances. A sample selection framework is used to account for selectivity bias as some farmers were not aware of the new technologies. Using this framework, the study investigates the effect of alternative information sources on adoption and modification decisions. Farmers are either informed by extension agents or by other farmers. The empirical results show that there are differences in adoption and modification decisions between these two groups.     

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.     

Factors influencing adoption of conservation tillage in Australian cropping regions*

The purpose of this research is to improve understanding of conservation tillage adoption decisions by identifying key biophysical and socio‐economic factors influencing no‐till adoption by grain growers across four Australian cropping regions. The study is based on interviews with 384 grain growers using a questionnaire aimed at eliciting perceptions relating to a range of possible long‐ and short‐term agronomic interactions associated with the relative economic advantage of shifting to a no‐tillage cropping system. Together with other farm and farmer‐specific variables, a dichotomous logistic regression analysis was used to identify opportunities for research and extension to facilitate more rapid adoption decisions. The broader systems approach to considering conservation tillage adoption identified important determinants of adoption not associated with soil conservation and erosion prevention benefits. Most growers recognised the erosion‐reducing benefits of no‐till but it was not an important factor in explaining whether a grower was an adopter or non‐adopter. Perceptions associated with shorter‐term crop production benefits under no‐till, such as the relative effectiveness of pre‐emergent herbicides and the ability to sow crops earlier on less rainfall were influential. Employment of a consultant and increased attendance of cropping extension activities were strongly associated with no‐till adoption, confirming the information and learning‐intensive nature of adopting no‐till cropping systems.     

Estimating Input Complementarities with Unobserved Heterogeneity: Evidence from Ethiopia

The level of technology adoption is often characterised as low in Africa. Recent evidence, however, points to the coexistence of substantial heterogeneity across farm households and the lack of a suitable mix of inputs for farmers to take advantage of input complementarities. We use a random coefficients multivariate probit model to quantify the complementarities between agricultural inputs and alternative forms of unobserved heterogeneity effects in modeling farmers' technology adoption decisions. The empirical analysis reveals that, conditional on various types of unobserved heterogeneity effects, farmers' technology adoption decisions exhibit strong complementarity for some inputs. The analysis also reveals substantial unobserved heterogeneity effects. We show that ignoring these behavioural features (unobserved heterogeneity and input complementarity) has important implications in quantifying the effect of some policy interventions that are meant to facilitate technology adoption. In particular, ignoring these features leads to significant overestimation of the effectiveness of extension services.     

Welfare impacts of maize–pigeonpea intensification in Tanzania

This article examines the driving forces behind farmers’ decisions to adopt improved pigeonpea and maize and estimates the causal impact of technology adoption on household welfare using data obtained from a random cross‐section sample of 613 small‐scale farmers in Tanzania. We use seemingly unrelated and recursive bivariate probit regressions to test the endogeneity and joint decision making of pigeonpea–maize production. A double hurdle model is used to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. To address the impact of adoption on welfare, the article employs both propensity score matching and switching regression techniques. Results from bivariate probit models show that unobservable factors cause both decisions to be correlated but the finding does not support the conjecture that both decisions are made jointly. Overall the analysis of the determinants of adoption identifies inadequate local supply of seed, access to information, human capital, and access to private productive asset as key constraints for pigeonpea technology adoption. The causal impact estimation from both the propensity score matching and switching regression suggests that maize/pigeonpea adoption has a positive and significant impact on income and consumption expenditure among sample households.     

Productivity and Land Enhancing Technologies in Northern Ethiopia: Health, Public Investments, and Sequential Adoption

The adoption of more efficient farming practices and technologies that enhance agricultural productivity and improve environmental sustainability is instrumental for achieving economic growth, food security, and poverty alleviation in sub-Saharan Africa. Our research examines the interaction between public investments, community health, and adoption of productivity and land enhancing technologies by households in the northern Ethiopian state of Tigray. Agricultural technology adoption decisions are modeled as a sequential process where the timing of choices can matter. We find that time spent sick and opportunity costs of caring for sick family members are significant factors in adoption. Sickness, through its impact on household income and labor allocation decisions for healthcare and other activities, significantly reduces the likelihood of technology adoption. Our findings suggest that agencies working to improve agricultural productivity and land resource conservation should consider not only the financial status of potential adopters, but also their related health situation.     

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.     

The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar

This article explores the dynamics of smallholder technology adoption, with particular reference to a high‐yielding, low external input rice production method in Madagascar. We present a simple model of technology adoption by farm households in an environment of incomplete financial and land markets. We then use a probit model and symmetrically censored least squares estimation of a dynamic tobit model to analyze the decisions to adopt, expand, and disadopt the method. We find that seasonal liquidity constraints discourage adoption by poorer farmers. Learning effects—both from extension agents and from other farmers—exert significant influence over adoption decisions.     

When my neighbors matter: Spillover effects in the adoption of large-scale pesticide-free wheat production

We investigate the spillover effects in farmers’ adoption decisions of a novel pesticide-free wheat production system. To this end, we exploit the variability and asymmetry in the social ties among neighboring farmers. We find evidence of spillover effects in farmers’ adoption decisions as well as in farm and farmer characteristics. Our results further highlight the importance of accounting for potentially heterogeneous social ties in farmer networks beyond pure measures of spatial proximity: spillover effects are only robust once we account for the strength of social ties through farmers’ stated tendency to consult peers on agricultural decisions. Our findings highlight the relevance of peer influence in the diffusion of sustainable agriculture practices even in contexts of well-functioned institutions and high interest in environmental protection such as European agriculture. We discuss implications for the design of policies and programs for sustainable agriculture, which are currently in the center of attention in agricultural policymaking.     

自然风险对农户技术采用行为的影响——以荔枝为例

[目的]由于近年来台风灾害、高温干旱、低温冷害、阴雨天等异常气候对荔枝产量、品质造成了不可低估的损失,有必要研究农户荔枝生产中应对自然灾害的防灾减灾技术的采用情况及其影响因素。[方法]通过对荔枝主产区广东、广西、海南的257荔枝种植户的入户调查,运用二元选择模型实证分析自然风险对农户采用荔枝间伐、高接换种、水肥药一体化技术行为的影响。[结果]农户间伐技术采用率最高,其次为高接换种技术,水肥药一体化技术采用率最低;广东省一半以上的农户采用了间伐技术,广西间伐技术采用率次之为39.0%,而海南省仅15.2%的农户实施了间伐;广西一半以上的农户采用了高接换种技术,而广东省采用率为34.0%,海南省农户基本没有采用高接换种技术;水肥药一体化技术采用率由高到低依次为广东、海南、广西。[结论]是否受灾与影响农户是否采用高接换种技术显著正相关;受灾减产比例与农户是否采用间伐技术、高接换种技术呈反比;地区虚拟变量与农户是否采用间伐技术、水肥药一体化技术显著负相关。     

Husbands’ and wives’ risk preferences and improved maize adoption in Tanzania

Research on technology adoption typically assumes that the preferences of a single individual—the household head—determine household-level decisions. This study uses experimentally derived prospect theory-based risk preferences from couples in Tanzania to test whether men's and women's risk preferences, which often diverge, influence the adoption of improved maize varieties. We find that women's risk aversion and men's loss aversion are negatively correlated with improved variety use. The tendency of men to overweight small probabilities is negatively associated with adoption, whereas the tendency of women to do so is positively associated. These findings suggest that technology adoption research should look beyond the preferences of the household head, and that technology promoters should consider targeting both men and women in their education and dissemination efforts.     

Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya

CIS‐derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey‐derived information. Location, with all its dimensions of market access, demographics and agro‐climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIs‐derived measures. This paper attempts to demonstrate the usefulness of integrating CIS‐measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIs‐derived measures of market access and agro‐climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo‐referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with CIS‐derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo‐referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.     

The role of social networks in the adoption of competing new technologies in Ghana

We use a detailed dataset to examine the impact of social networks, conditional on contextual and individual confounders, on farmers' adoption of competing improved soybean varieties in Ghana. Based on the contagion conceptual framework, we employ a spatial autoregressive multinomial probit model to examine how neighbours' varietal and cross-varietal adoption of improved varieties affect a farmer's adoption decision in the social network. Our results show that adoption decisions in a network tend to converge on one variety, such that beyond a threshold of adopting neighbours of that improved variety, the cross-varietal effects tend to lose significance in the network. If the shares of adopting neighbours of the improved varieties are equal, we find evidence that farmers are not more likely to adopt either improved variety compared to farmers with no neighbours who have adopted the improved varieties. The findings demonstrate the significance of neighbourhood effects in the adoption of competing technologies.     

人格特质对茶农有机茶技术采纳的影响

根据福建省安溪县656份的问卷调查数据,基于大五人格特质理论,运用结构方程模型分析人格特质对茶农的有机茶技术采纳行为的影响。结果表明:安溪县茶农的经营特征与其所处的技术环境对有机茶技术采纳行为不存在显著直接效应,但都通过技术采纳态度对技术采纳的行为产生正向影响作用,其中有机茶技术采纳态度起完全中介作用;人格特质对有机茶技术采纳行为既存在直接正向预测作用,又通过有机茶技术采纳态度对技术采纳行为产生间接正向预测作用,并且间接效应大于直接效应,起中介变量的作用。因此,运用人格测评工具来发挥技术采纳者的性格优势在技术采纳过程中的作用,释放有机茶技术的生态溢出效应,将为有机茶技术推广研究提供新思路。     

The Adoption and Profitability of rbST on Connecticut Dairy Farms

This work estimates probit and tobit models of the adoption of recombinant bovine somatotropin (rbST) on Connecticut dairy farms and then endogenizes that adoption in estimates of milk production and farm profit rates. The work improves on the current literature by allowing the rbST decision to be both continuous and contingent on other technology adoption decisions. The results show that larger farms, with more productivity technologies, and with younger, more educated farmers, are more likely to adopt rbST. While rbST is shown to significantly increase milk production, there is no evidence that it increases profits on a per cow basis.     

Digging deep and running dry—the adoption of borewell technology in the face of climate change and urbanization

In this article, we analyze the effects of household location and weather variability on the adoption of borewell technology along the rural–urban interface of Bangalore, India. Understanding these effects can help to design policies that ensure smallholders’ livelihoods and the functioning of ecosystems in drought-prone areas. First, a theoretical framework was developed that conceptualizes how household location and weather can influence farmers’ adoption decisions. Then, an empirical analysis based on a primary data set collected in 2016 and 2017, covering 576 farm households, was conducted. With a semiparametric hazard rate model, determinants of the borewell adoption rate were analyzed. Different rainfall variables were included to capture the effect of changing climate conditions as well as a two-dimensional penalized spline (P-spline) to estimate the effects of household location. Results show that proximity to Bangalore, but also secondary towns accelerate adoption rates. In terms of weather variability, the study finds that a higher amount of total annual rainfall decelerates adoption rates, whereas higher amounts of rainfall during the southwest monsoon (the most important cropping season) accelerate adoption.     

The impact of social networks on hybrid seed adoption in India

This article adds to the literature about the impact of social networks on the adoption of modern seed technologies among smallholder farmers in developing countries. The analysis centers on the adoption of hybrid wheat and hybrid pearl millet in India. In the local context, both crops are cultivated mainly on a subsistence basis, and they provide examples of hybrid technologies at very different diffusion stages: while hybrid wheat was commercialized in India only in 2001, hybrid pearl millet was launched in 1965. The analysis is based on surveys of wheat and millet farmers in the state of Maharashtra. Comprehensive data on farmer characteristics and social interactions allow for identifying individual networks, thereby improving upon previous research approaches that employed village-level variables as proxies for network effects. Using econometric models, we find that individual social networks play an important role for technology adoption decisions. While village-level variables may be used as suitable proxies at later diffusion stages, they tend to underestimate the role of individual networks during early phases of adoption.     

Measuring Farm and Market Level Economic Impacts of Improved Maize Production Technologies in Ethiopia: Evidence from Panel Data

While it is often recognised that agricultural technology adoption decisions are intertwined and best characterised by multivariate models, typical approaches to examining adoption and impacts of agricultural technology have focused on single technology adoption choice and ignored interdependence among technologies. We examine farm‐ and market‐level impacts of multiple technology adoption choices using comprehensive household survey data collected in 2010/11 and 2012/13 in Ethiopia. Economic surplus analysis combined with panel data switching endogenous regression models are used to compute the supply shift parameter (K‐shift parameter), while at the same time controlling for the endogeneity inherent in agricultural technology adoption among farmers. We find that our improved technology set choices have significant impacts on farm‐level maize yield and maize production costs, where the greatest effect appears to be generated when various technologies are combined. The change in maize yield and production costs results in an average 26.4% cost reduction per kilogram of maize output (the K‐shift parameter). This increases the producer and consumer surpluses by US$ 140 and US$ 105 million per annum, respectively. These changes in economic surplus help to reduce the number of poor people by an estimated 788 thousand per year. We conclude that deliberate extension efforts and other policies that encourage integration of technologies are important for maize technologies to yield their full potential at both farm and market levels.     

Consumption risk,farm characteristics,and soil conservation adoption among low-income farmers in the Philippines

This paper investigates patterns of soil conservation adoption among low-income farmers in the Philippines. A model is presented that focuses attention on the role ot assets and consumption risk in conservation adoption decisions. Results Iron, a reduced-form probit model of adoption are reported. These econometric findings indicate that patterns of soil conservation adoption reflect relative risk considerations in addititon farm and household characteristics. Farm size, tenure security, labor availability, and land quality all exhibit a positive association with soil conservation adoption. In contrast, controlling on these and other household characteristics, the probability of adoption falls as consumption risk rises. These results underscore a need for greater sensitivity among policymakers to the role of consumption risk in influencing soil conservation decisions in low-income settings.