Household nutrition and income impacts of using dairy technologies in mixed crop–livestock production systems

Technologies like improved breeds of dairy cows and improved forages have the potential to significantly increase dairy cow productivity and farmers’ profits in developing countries. However, adoption of such technologies has been low in Ethiopia, despite numerous efforts to disseminate the technologies in the past. Some studies argue that adoption of technologies is low because welfare effects of the technologies could be insignificant or negative to certain groups of farmers. This article employed propensity score matching and inverse probability weighting estimator with regression adjustment to examine the difference in household nutrition and income between adopters and nonadopters of dairy technologies in rural Ethiopia. We find that adoption of cross‐bred dairy cows and improved forages increases household nutrition and income. The significant household nutrition and income impact for adopters support the notion that many Ethiopian smallholders have not adopted dairy technologies because adopters and nonadopters of dairy technologies have inherent differences in welfare outcome potentials. The results suggest that interventions that enhance access to farm resources and address barriers to input and output value chains could improve adoption of dairy technologies.     

When knowledgeable neighbors also share seedlings: diffusion of banana cultivation in Tanzania

Networks have been found to increase adoption of technology by providing information about the new technology (e.g., Conley and Udry, 2010). However, little is known about provision of necessary inputs for adoption through networks. Using data from an intervention in Tanzania, I discuss how a farmer's network can also affect the adoption of improved banana cultivation by providing seedlings. A solidarity chain principle obliged project farmers to pass on improved seedlings to other farmers free of charge. I provide a theoretical framework to guide intuition for the empirical results and suggest an empirical distinction between information and input provision through networks. Empirically, I find support for network effects through provision of both information and inputs, jointly boosting the network effect on adoption: a farmer is 39 percentage points more likely to adopt banana cultivation if there is at least one banana grower in the farmer's network compared to none. In this setting, it is not possible to fully disentangle the input and information channel. While the findings suggest that a solidarity chain principle could be an interesting avenue to boost local diffusion of new technologies, more research is needed to unpack the causal impact and the interplay with existing input markets.     

Sustainable recommendation domains for scaling agricultural technologies in Tanzania

Low adoption of sustainable intensification technologies hinders achievement of their potential impacts on increasing agricultural productivity. Proper targeting of locations to scale-out particular technologies is a key determinant of the rate of adoption. Targeting locations with similar biophysical and socio-economic characteristics significantly increases the probability of adoption. Areas with similar biophysical and socio-economic characteristics are referred to as recommendation domains (RDs). This study used geospatial analysis to delineate sustainable recommendation domains (SRDs) for scaling improved crop varieties and good agronomic practices in Tanzania. The study uses K-means clustering to identify relatively similar clusters from grid raster’s representing biophysical and socio-economic environments. Critical ecosystems are masked-out from the clusters to generate the SRDs. The potential impacts of scaling technologies in the generated SRDs were assessed and a spatial targeting index developed. Results identify 20 SRDs and the bio-socio-economic gradients that delineate them. This study proposes an Impact Based Spatial Targeting Index (IBSTI) as an objective tool for priority setting when scaling agricultural technologies. IBSTI identified priority areas within each SRD that should be targeted to maximize potential impacts of a scaling intervention. The data-driven clustering method is recommended for regions with limited technology trials. Results demonstrate the potential of geospatial tools in generating evidence-based policies on scaling of sustainable intensification technologies.     

The Impact of Migration,Remittances and Public Transfers on Technology Adoption: The Case of Cereal Producers in Rural Kenya

Risk and liquidity constraints are major obstacles to the adoption of modern technologies in many rural African countries. Migration and remittances can help rural households overcome these constraints and increase the adoption of modern technologies. We analyse the impact of migration, remittances and government transfers on the adoption of improved seeds in rural Kenya. Using data from the World Bank, two stage least‐squares estimates show that both migration and remittances positively affect the adoption of improved seeds. However, three stage least‐squares estimates reveal that the adoption of new technologies is more related to migration than remittances.     

Smallholder dairy cattle feeding technologies and practices in Tanzania: failures,successes, challenges and prospects for sustainability

In Tanzania, milk production under smallholder farming systems is season sensitive, fluctuations of feeds in both quantity and quality being the major driver. A dry season decline in milk production of over 40% due to feed scarcity is a common phenomenon. Adoption of improved feed production, conservation and utilization technologies and practices in dairy farming communities is poor. This review work was based on a key question which states “Why is adoption of improved dairy nutrition technologies and practices in Tanzania still poor despite being promoted for decades?”. We have shown that major opportunities for curbing dry season animal feed shortage include on-farm optimization of production and use of high yielding pasture varieties including napier grass (Pennisetum purperium Schumach.) and leguminous fodder species. Crop residues in particular maize stover needs to be optimized for effective dry season feeding. The major reasons for low adoption of proven technologies include limited technical knowhow among smallholder dairy farmers augmented by limited extension services and technological costs. For enhancing sustainable uptake; we suggest promotion of on-farm research, public-private partnerships and dairy farmers’ cooperative associations. These are vital for facilitating smooth access to information, investment capital, reliable inputs and markets among the smallholder dairy farmers.     

Gini's coefficient of mean difference as a measure of adoption speed: theoretical issues and empirical evidence from India

This paper represents a variant on the empirical analysis of technological adoption by Griliches. It follows up on previous research regarding the potential of the Gini coefficient of mean difference as a distribution-free measure of adoption speed. A formal proof is supplied for the derivation of an empirically workable definition of the Gini. Data regarding the uptake of high-yielding wheat varieties in India are used to compare the performance of the Gini relative to that of the logistic and Weibull adoption speed coefficients. The results suggest that, for the purpose of ex-post analyses of monotonically increasing adoption processes, use of the Gini in measuring adoption speed can result in a better explanation of aggregate adoption behavior.     

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.     

Enhancing productivity on suburban dairy farms in China

Dairy farms in China's suburban areas have been playing an important role in providing urban markets with fresh milk. With the rising demand for fluid milk and dairy products in the cities, there is a perception that small and scattered dairy farms in China's provinces are gradually disappearing and more concentrated dairy cattle farming is being formed near suburban areas. This article uses farm‐level survey data and stochastic input distance functions to make estimates of total factor productivity (TFP) on suburban dairy farms, as well as for the entire dairy sector. The results show that over the past decade TFP growth has been positive on suburban dairy farms, and this rise in productivity has been driven mostly by technological change. However, at the same time we find that, on average, the same farms have been falling behind the advancing technical frontier. We also find one of the drivers of the suburban dairy sector is the relatively robust rate of technological change of these farms, which has been more rapid than on farms in the dairy sector as a whole. The results suggest that efforts to achieve greater adoption of new technologies and better advice on how to use the technologies and manage production and marketing within the suburban dairy sector will further advance productivity growth in the sector.     

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.     

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.     

Market imperfections,access to information and technology adoption in Uganda: challenges of overcoming multiple constraints

Limited empirical evidence exists on how multiple binding constraints influence the adoption of improved technologies by smallholder farmers. This article uses the case of groundnut variety adoption in Uganda to investigate the role of information, seed supply, and credit constraints in conditioning technology uptake. New data from a household survey in seven groundnut growing districts (n = 945) indicate that 8% of farmers lack information on new varieties, while 18% and 6% of farmers, respectively, cannot adopt mainly due to seed supply and capital constraints. A tobit‐type specification that considers all nonadopters as being uninterested in the technology (i.e., corner solutions) would lead to inconsistent parameter estimates and incorrect conclusions in this context. We therefore estimate a modified multi‐hurdle specification of demand for new varieties, taking into account how information, seed supply, and capital constraints jointly determine adoption probability and intensity. The study reveals new empirical insights on why agricultural technology adoption in Africa has lagged behind: slow uptake is not mainly due to a lack of economic incentives, but rather a reflection of information, seed supply, and credit constraints that prevent farmers from translating their desired demand into adoption of modern varieties. Policy implications are discussed.     

Priority setting for research on aquatic resources: an application of modified economic surplus analysis to natural resource systems

In contrast to research on farming systems, research on natural resource systems seldom applies rigorous priority setting techniques, mainly due to difficulties associated with estimating research impact ex ante for such systems. This article presents a replicable approach to priority setting that addresses these difficulties. Assessment is based on multiple criteria, combining economic surplus analysis with subjective scoring, based on information drawn from a cross‐country survey of expert opinion. The approach is applied to the WorldFish Center, a global agricultural research organization focusing on living aquatic resources, both farmed and wild. The exercise demonstrates the relevance of conventional evaluation techniques to fisheries research based on a practical application of its impact pathways.     

Integrating Technology Traits and Producer Heterogeneity: A Mixed-Multinomial Model of Genetically Modified Corn Adoption

This article proposes a model of technology adoption that integrates demand for individual traits of new technologies with the potential for heterogeneity based on farm and farmer characteristics. The model is applied to recent genetically modified corn adoption data from Minnesota and Wisconsin farmers, using a mixed-multinomial logit (MMNL) model to estimate the effects of traits and farm and farmer characteristics on adoption outcomes. This approach allows explicit recovery of estimates of farmers' shadow prices for individual technology traits. Results show the importance of producer and regional heterogeneity in preferences for seed traits.     

Exploring strategic priorities for regional agricultural R&D investments in East and Central Africa

The 11 countries of East and Central Africa have diverse but overlapping agroclimatic conditions, and could potentially benefit from spillovers of agricultural technology across country borders. This article uses high‐resolution spatial data on actual and potential yields for 15 major products across 12 development domains to estimate the total benefits available from the spread of new agricultural technologies around the region. Market responses and welfare gains are estimated using the Dynamic Research Evaluation for Management model, taking account of current and future projections of local and international demand. Results suggest which crops, countries, and agroclimatic regions offer the largest total benefits. Downloadable data and program files permit different assumptions and additional information to be considered in the ongoing process of strategic priority setting.     

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.     

The Dynamics of Agricultural Biotechnology Adoption: Lessons from series rBST Use in Wisconsin, 1994–2001

This article exploits panel data from Wisconsin dairy farmers to examine the dynamics of recombinant bovine somatotropin (rBST) adoption and to identify the characteristics that distinguish among nonadopters, disadopters, and early and late adopters. Panel methods are used to control for omitted variables and endogenous regressors that call into question coefficient estimates derived from cross-section adoption models. The results, however, confirm previous findings that larger farms with complementary feeding technologies are more likely to adopt rBST, nonadopters appear quite unlikely to become adopters, and rBST adoption will remain at rather moderate levels in Wisconsin.     

Duration Analysis of Technological Adoption in Ethiopian Agriculture

Duration analysis has been used to examine the impact of time‐varying and time‐invariant variables on the speed of adoption of fertiliser and herbicide by smallholder farmers in East and West Shewa in the central highlands of Ethiopia in the 25 years preceding 1996. The estimated models suggest that economic incentives were the most important determinants of the time farmers waited before adopting new technologies; traction power in the form of oxen and infrastructural factors (in particular proximity to markets) also appear to have been important influences, but less so than prices. Other agricultural inputs (area of farmland, labour, credit), extension services and farmers' personal characteristics (education, gender, age) appear to have had little, if any, effect on adoption behaviour. There is also evidence that the speed of adoption of herbicide, on both tef and wheat, was slower than that of fertiliser.     

Welfare effects of maize technologies in marginal and high potential regions of Kenya

This paper describes the findings of a study that used a multi‐market model to assess the potential impact of improved maize technologies on the welfare of various types of rural and urban households in Kenya. The modelling results indicate that technologies developed for high potential regions are likely to have more profound aggregate impacts on maize production and lead to greater reductions in import demand (if prices are controlled) or maize prices (if maize prices are flexible). Technology adoption in high potential regions is likely to have substantially greater positive impacts on aggregate real incomes, but inferior income distributional outcomes compared to technology adoption in marginal regions.     

Exploring Incentives for the Adoption of Food Safety Controls: HACCP Implementation in the U.K. Dairy Sector

This article explores the incentives that motivate the adoption of food safety controls by businesses through a study of HACCP adoption in the U.K. dairy processing sector. The study identifies four key factors that have motivated the adoption of HACCP, namely, internal efficiency, commercial pressure, external requirements, and good practice. Respondents to the survey are clustered according to the relative importance of these factors in their adoption decision. Four clusters are identified and related to the characteristics of firms, including firm size and type of products manufactured. The results indicate that there are systematic differences in the HACCP adoption process between individual firms.