Adoption of agricultural innovations, converging narratives, and the role of Swedish agricultural research for development?

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Adoption of agricultural innovations, converging narratives, and the role of Swedish agricultural research for development?


Discussion paper, version 2011-01-28

Johan Toborn

Table of contents


1. Introduction 1

2. Agricultural Innovations 2

2.1. Adoption and diffusion of agricultural innovations – theories and concepts 3

2.2. Empirical adoption and adoption impact studies 4

3. Adoption of specific innovations illustrated 7

3.1. Illustrative key studies 8

3.1.1. Embodied exogenous innovations 8

High Yielding Varieties 8

GM Crops 10

Fertilizers 14

Pesticides 15

3.1.2. Packages of disembodied agronomic and managerial innovations 16

Conservation Agriculture 16

Integrated Soil Fertility Management 20

Soil and Water Conservation 21

Integrated Pest Management 24

Rain Water Harvesting 27

Agroforestry 28

Low-External Input Technologies 30

Sustainable Agriculture/Integrated Natural Resource Management 31

4. Adoption of agricultural innovations – conclusions 32

5. Perspectives and narratives on agricultural development 33

5.1 Historical development of agriculture 35

5.2 Markets and institutional fixes 35

5.3. Policy fixes 36

5.4 Livelihoods is key 37

5.5 Technology fixes 38

5.5.1. The Green Revolution in retrospect 38

5.5.2 The quest for sustainable and multifunctional agriculture 39

5.6. Emerging, complementary narratives? 41

5.6.1. Convergence on technology, but with multiple pathways to intensification 41

5.6.2. Context matters, generalisation too 42

5.6.3. Towards integrative science 43

5.6.4. Putting knowledge into use 44

6. Concluding discussion – new roles and binding constraints for Swedish
agricultural development research? 46



Adoption of agricultural innovations, converging narratives, and the role of Swedish agricultural research for development?

1. Introduction

This paper discusses what contribution Swedish agricultural science, in particular natural science, could or should make to agricultural development in the south apart from the obvious business as usual, i.e. current research issues where we presumably have competence and comparative advantages. The latter is certainly an important role of Advanced Research Institutions in international agricultural research. Business as usual is in itself a position on agricultural research for development; though not one that has emerged from deliberation, but rather resulted from the interests of individual scientists, existing disciplinary and departmental divisions, academic reward systems, the few financial sources available, and the research approaches they sustain. Research projects making part of the position are usually small, often acquired in competition through a peer review process, or as commissioned research (notably Ph.D. training/research). Research has, understandably, a biophysical focus and usually allows limited opportunities to engage in dissemination and upscaling. Competence can still be put to use in new contexts, formats, and with new foci, however. From different entry points this paper purports to provoke reactions on our position on agricultural research for development. The provocations are subtle, however, and have no simple responses. A marked deviation from business as usual assumes that considerable individual and collective constraints have to be overcome, and that a strong will to change is present. This is a tall order, but reflecting on alternative use of competence, or needs for alternative competence, has an intrinsic value and is seldom done. In the best of worlds, such reflections could result in a position on how we view agricultural research for development, our role in that context, and how we would like to change to fill that role. A shared position on agricultural research for development would increase the appreciation of how different disciplines and approaches fit in the overall context and create synergistic value added rather than existing in a state of internal competition.

Efforts to develop agriculture are expected to result in improved agricultural production; “improved” obviously having multiple interpretations. Better technologies have to be generated and put into use. Agricultural scientists by training and tradition want to believe that new technologies drive agricultural development. Research findings are passed through transformative and communicative stages and finally result in improved production. This default linear model is valid in some cases, and utterly wrong in others. How we perceive adoption and diffusion of agricultural innovations is therefore a key element in our position of agricultural research for development? Chapter 2 describes alternative theories and concepts of adoption and diffusion of agricultural innovations, and empirical approaches to study adoption and diffusion. For a private company, high adoption rates and diffusion of its innovations is a sign of success, presuming sound economics. For public agricultural research, adoption is a necessary precondition for assessing if the benefits generated by the innovation were worth the research investment. Benefits range from outcomes at adopter-level to community-level changes in environmental, economic and social conditions and distributional impacts. This evokes the intricate question how identification, design, implementation and evaluation of agricultural research and innovation diffusion can be improved. For composite natural resource innovations much more sophisticated assessment and learning approaches are needed than represented by the default linear model.

Chapter 3 illustrates actual adoption of agricultural innovations. Innovation is a deceptively simple term, but in reality ranges from embodied external innovations (seed, fertilizers, pesticides, etc.) to systems changes building on agronomic and managerial innovations. This division is admittedly artificial as technology packages in agricultural development often include both categories and simultaneous interventions of a non-technical nature. For selected innovations under the two categories definitions and classification schemes are provided, selected studies of relevance summarised, extent of adoption/diffusion commented on, and illustrative successful cases reported. A diffuse, yet clear pattern emerges. Adoption of agricultural innovations, with exceptions for early Green Revolution (GR) success, has not progressed as fast as desirable and projected, in particular not in Sub-Saharan Africa. What to expect is per se methodologically complex to assess; sometimes based on mathematical models, sometimes rather airing disappointments. How do we explain the lack of progress and what could be done differently?

Chapter 4 draws lessons from adoption studies with implicit implications for Swedish agricultural research for development.

Agricultural development is a study area of increasing complexity. Our knowledge has increased, conditions have changed over time, and additional dimensions are added by growing awareness of e.g. Man’s influence on ecosystems, water scarcity and climate change, and effects of globalisation. To make sense of a messy reality, we often resort to narratives or stories. There are several narratives that from different perspectives are used to explain agricultural development and ultimately adoption of agricultural technologies. Four common narratives are reviewed in Chapter 5: markets and institutional fixes; policy fixes; livelihoods is key; and technology fixes with several subthemes. Each narrative comes in different versions. These narratives are important building blocks of a meta-narrative. Then four possible emerging, complementary narratives seen relevant from a research perspective are discussed: convergence on technology, but with multiple pathways to intensification; context matters, generalisation too; towards integrative science, and putting knowledge into use. These are desirable, not mutually exclusive narratives. Each narrative subtly provokes the business as usual scenario.

Chapter 6 reflects on the implications for Swedish agricultural research for development beyond business as usual.

2. Agricultural innovations

“Many technologists believe that advantageous innovations will sell themselves, that the obvious benefits of a new idea will be widely realized by potential adopters, and that the innovation will therefore diffuse rapidly. Seldom is this the case. Most innovations, in fact, diffuse at a disappointingly slow rate” (Rogers 1995).

An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption. In industrial and agricultural innovation literature, a distinction is made between products, processes, and social/organisational innovations. Agricultural innovations, as traditionally studied, are mainly to categorise as products, but with elements of processes. Technology is used synonymously with innovation.

Agricultural innovations can be classified according to several parameters:

- Genetic, mechanic and chemical innovations (private goods) and agronomic, managerial and animal husbandry innovations (public goods);

- Individual innovations (individual adopter) and collective innovations (group of persons);

- Continuous innovations, semi-continuous innovations, and discontinuous innovations with increasing demands for new skills, knowledge and even investments;

- Labour saving innovations and land saving innovations;

- Process innovations and product innovations;

- Endogenous and exogenous innovations (based on Sonnino 2009).

A slightly different categorization is suggested by Sunding (1999):

- Innovations embodied in capital goods or products (“shielded” and “non-shielded”) and innovations not embodied;

Innovations according to impact:

- New products;

- Yield increasing innovations;

- Cost-reducing innovations;

- Innovations that enhance product quality.

Innovations according to form

- Mechanical, biological, chemical, biotechnical, and informational innovations

For the purpose of this paper a distinction is made between Embodied, exogenous innovations (EEI) and packages of disembodied agronomic and managerial innovations (PDAMI). In practice the two categories are often combined. The first category would mainly qualify as continuous or semi-discontinuous innovations, whereas the PDAMI category leans more to the discontinuous category, i.e. more skill-intensive.

2.1 Adoption and diffusion of agricultural innovations – theories and concepts

Diffusion of innovations has been studied by many disciplines (e.g. anthropology, sociology of various brands, education, medicine, communication studies, marketing, business administration, etc.). From an initial domination of sociology, economics has gradually taken over, possibly because of a stronger emphasis on the theoretical basis for adoption, and its policy relevance.

The sociologist Everett Rogers’ seminal work on diffusion of innovations (1995) is a good starting point into this area of study. An innovation according to Rogers is “an idea, practice or object that is perceived as new by an individual or other unit of adoption”. Diffusion is seen as “the process by which an innovation is communicated through certain channels over time among members of a social system”. A technological innovation usually has two components: a hardware aspect (the tool, product) and a software aspect (how to use the hardware). For good reasons studies of diffusion of innovations have often addressed individual innovations, in practice innovations often come in packages – clusters – and are interrelated and interdependent.

The characteristics of innovations explain their rate of adoption. Five such characteristics of importance are discerned: 1) The relative advantage reflects how the innovation is subjectively perceived superior to the previous idea; 2) Compatibility reflects how the innovation is perceived “consistent with the existing values, past experiences, and needs of potential adopters”; 3) Complexity reflects the perceived difficulty to understand and use the innovation; 4) Trialability is “the degree to which an innovation may be experimented with on a limited basis”; and 5) Observability reflects how the results of an innovation are visible to others. An innovation can further be changed or modified (re-invented) by a user.

Communication, through channels, provides information to a social system with the purpose to influence the knowledge and assessment of the innovation. Mass media is often more effective in creating awareness of an innovation, whereas personal contacts are more effective in forming an opinion about a new idea. Such interpersonal communication is facilitated if conveyors of information are optimally similar to the receiver in certain attributes.

Time is a main factor in the decision-making process, innovativeness and an innovation’s rate of adoption. In the innovation-decision process, an individual passes through the stages: knowledge, persuasion, decision, implementation (adoption) and confirmation (post-adoption assessment). Information is sought at the various stages to reduce uncertainty about the usefulness of the innovation. The decision stages result in adoption or rejection of the idea.

Innovativeness is an expression for how early an individual or other unit of adoption is adopting a new idea compared to other members of the social system. Adopters are divided into five categories, each with its own characteristics: 1) innovators, 2) early adopters, 3) early majority, 4) late majority, and 5) laggards. Finally, rate of adoption is the relative speed with which an innovation is adopted by members of a social system.

The social system with its interrelated units shares an interest in finding solutions to a common goal, i.e. to improve their agricultural system to enhance livelihoods. Such a system has a social and communication structure that facilitates or impedes the diffusion of innovations in the system. Norms, being part of the social system, are the established behaviour patterns for system members. Often opinion leaders play a crucial role in influencing system members. Change agents may have the explicit role to influence members in a certain direction. Both opinion leaders and change agents are central actors in diffusion of innovations.

Three main types of innovation-decisions can be distinguished: independent individual decisions (adopt a HYV), collective decisions (soil conservation on hillsides), and authority imposed decisions.

The accumulated adoption over time, i.e. the diffusion, is frequently found to follow a sigmoid distribution. In marketing applications, this feature has often been used to predict and influence diffusion.

Rogers’ account for innovation adoption and diffusion does not give theoretical explanations to how adoption decisions are actually made. A classic article by Feder (1985) is a frequent departure for theoretical analysis of decision making. This line of studies is mainly pursued by economists. The essence of his article and follow-up renderings on the subject include a number of complicating issues.

Often distinct technological options are present. Several decision processes may then run simultaneously or sequentially. Farmers may therefore rather consider portfolios of innovations. Further, innovations may be divisible or of a lumpy character, presenting a dichotomous choice, which could be a deterrent to those interested in trying on a small scale. Lumpy investments may be only partially recoverable and adoption decisions may at times be close to irreversible. There may be fixed transaction or information costs associated, that may again deter resource-constrained farmers. Innovations may be scale-neutral or contain economies of scale, i.e. the innovation may favour better resourced households. For divisible innovations, the intensity of use is of great interest (e.g. proportion of land allocated, intensity of use per area unit). Technologies may show improved performance over time, or become cheaper due to economies of scale, and therefore gradually become more attractive to farmers, ceteris paribus. Diffusion of technologies is more complex than the spread of influenza.

Potential adopters are uncertain what an innovation may offer. Over time information from different sources and from the farmer’s own experience reduces this uncertainty. A better base is established for adoption/rejection and intensity of use decisions.

The decision maker is assumed to maximise the utility of asset use over time, subject to various resource constraints, usually assuming a concave utility function. This can be expressed by static models, or by dynamic, sequential models that consider changing knowledge and conditions. In a dynamic model, new decisions depend on the results of previous decisions and their effect on wealth and income, and revised subjective knowledge about the utility of the innovation, including production outcomes, expected costs and revenues. Farmers gradually learn how to make better use of the innovation. For management-oriented improvements, a better systems performance may also materialise over time. Hence parameters determining farmers’ choice are continuously updated.

Risk has been included in many models. Production, incomes and costs are not deterministically known. Farmers have their subjective perception of risks involved, and consider not just the expected mean outcome but also the distribution of risks around the mean. The subjective perception of risk may well deviate from the objective reality. It is often assumed that farmers are risk averse with the extent depending on several characteristics. To the farmer, the riskiness of an innovation compared to the old idea then matters; also whether the risk varies together with risks in other parts of the system or moves in the opposite direction. Some models suggest safety-first decision behaviour, implying that farmers have to be assured of a minimum result, and not base their decision on expected results.

Theoretical models of adoption behaviour have looked into variables that may explain the decision to adopt or the intensity of adoption. Such factors include farm size, credit and information access, personal traits of the decision-maker, tenure arrangement, etc.

Theoretical models for the aggregate adoption complement individual adoption models. Alternative assumptions regarding individual adoption behaviour usually result in S-shaped curves. Cochrane’s technological treadmill suggests diminishing gains over time due to price declines following increased production due to adoption.

2.2 Empirical adoption and adoption impact studies

A vast literature of empirical studies has attempted to test the relationship of key variables to adoption behaviour. The theoretical foundation for selection of variables is sometimes weak; which is understandable as theoretical models often point in different directions. Early adoption studies had a heavy emphasis on the GR packages, following the seminal studies of improved varieties in the US.

Understanding past adoption

Empirical studies attempt to understand and explain adoption. It is an ex post perspective. Obviously, technology research has to be guided by early analysis of likely adoption of a technology at some stage of development. Such ex ante analysis may include partial farm budgets to show the economic attractiveness of the technology, constraint and risk analysis. Should the innovation be selected for dissemination, the analysis may be repeated when early signs of adoption are available and the trends extrapolated, constraints focused, etc. The first part of this section deals primarily with ex post studies.

Generalizations (with many exceptions) by Ruttan (in Feder 1985) from early GR technology studies are illustrative of the possible conclusions from such studies:

-The new HYVs were adopted at exceptionally rapid rates in areas where they were technically and economically superior;

- Neither farm size nor tenure has been a serious constraint to the adoption of new HYVs of grain;

- Neither farm size nor tenure has been an important source of differential growth in productivity;

- The introduction of HYVs has resulted in an increase in the demand for labour;

- Landowners have gained relative to tenants.

Feder in his article summarises findings on individual adoption with respect to seven major explanatory variables: farm size, risk and uncertainty, human capital, labour availability, the credit constraint, tenure, and supply constraints. Considered important at the early stages of adoption, they may become less significant in later stages.

His conclusion on the significance of farm size is illustrative and with a bearing on the other factors:

“The wide variety of empirical results, interpreted in the context of the theoretical literature, suggests that size of holding is a surrogate for a large number of potentially important factors such as access to credit, capacity to bear risk..., access to scarce inputs (water, seeds, fertilizers, insecticides), wealth, access to information, and so on”. Since the influence of these factors varies in different areas and over time, so does the relationship between holding size and adoption behaviour. Because the theoretical literature and analytical interpretation of empirical results suggest that several intervening factors lie at the root of observed farm-size/adoption relationships, the remainder of this section turns to consideration of the observed role of such factor”.

On risk, Feder (ibid.) concludes that ...” most of the empirical work on the role of subjective risk is not yet rigorous enough to allow validation or refutation of available theoretical work”.

Adoption research has moved on in the last 25 years. Still, Feder’s concluding comments should be of concern. In the words of Doss (2006):

“... research was needed in five areas: (i) examining the intensity of adoption (not just dichotomous choices); (ii) addressing the simultaneity of adoption of different components of a technology package; (iii) analysing the impact of incomplete markets and policies on adoption decisions; (iv) contextualizing adoption decisions within social, cultural and institutional environments; and (v) paying attention to dynamic patterns of changes in landholdings and wealth accumulation among early and later adopters” (p 208).

Doss argues that progress has been made in the first two fields (e.g. econometric techniques have become increasingly sophisticated to deal with issues of endogeneity and simultaneity of decisions). However, she maintains that “...some of the concerns ... remain unanswered, especially the issues of how institutional and policy environments affect the adoption of new technologies and how the dynamic patterns of adoption affect the distribution of wealth and income”.

To Doss technology adoption research has three current foci: econometric and modelling methodologies to understand adoption decisions, studies of learning and social networks in adoption decisions, and continued local micro-level studies to understand adoption for policy purposes. Generic weaknesses of micro-studies of technology adoption, according to Doss, are the lack of dynamics originating in using cross-sectional data, and the lack of variation within samples. The latter can be rectified by larger (and more expensive) samples or through meta-studies. The latter, in turn, assumes consistent definitions of variables are used.

For policy purposes we may be interested in how incomplete access to credit or cash, information and labour markets may affect the adoption of technologies. Doss illustrates how the measures that have been construed to measure such variables differ markedly between studies, do not necessarily measure the core contents of the variable, make comparisons between studies cumbersome, and interpretation of results less effective for policy design.

Ex post impact assessment

Adoption studies have usually been accompanied by assessments of how adopters benefited. This emphasis has been accentuated over time. Agricultural research has been resource-constrained in recent decades and research investments have to progressively justify their returns. Research outcomes and impacts have to be demonstrated to funders. A second purpose of ex post impact assessment is to provide learning opportunities. These growing demands have gradually extended to consider not only technical and economic production benefits, but also effects on household incomes, other household assets, vulnerability, equity, consumption, nutrition, food security, poverty, and environment, etc. Gone are the also days when agricultural research in practice meant embodied exogenous innovations. A marked shift to NRM and policy research is noted, with telling evidence seen in the CGIAR system (Renkow 2010). Outcomes and impacts of NRM and policy research are exceedingly more complicated to assess. Adoption studies have become part of a much more sophisticated cycle of impact evaluation.

How to go about this challenge has occupied the Standing Panel on Impact Assessment of CGIAR’s Science Council. Its Strategic guidance for ex post impact assessment (epIA) of agricultural research (Walter, T. 2008) is a revealing treatment of how to refine epIA for accountability and learning purposes. The guideline presents a typology of epIA composed of the primary objective to document productivity and profitability, or selective high order impacts and the level of assessment (macro or micro). The principal cases are: 1) aggregate economic rate of return, 2) disaggregate economic rate of return, 3) aggregate multi-dimensional impacts, and 4) disaggregate multi-dimensional impact. Central to CGIAR’s rendering of the topic is the notion of impact pathways. Planning of research projects to be included in CGIAR’s portfolio include describing the most plausible impact pathways from problem identification to intended ultimate goals of the CG. A generic impact pathway includes:

Inputs (research investments);

Outputs (first/immediate results of a research project);

Outcomes (the external use, adoption or influence of a project’s outputs by next or final level users that results in adopter-level changes needed to achieve the intended impact);

Impacts (the ‘big picture’ changes in economic, environmental and social conditions that a project is working toward. Within the CG System, project impacts should be in line with the center’s mission and vision statement, and with the CGIAR goals).

Impact pathway analyses are part of ex ante planning but, when well prepared, also play an important role in epIA assessments. The Guidance explores the accumulated experience and best practice of economic rate of return studies (where obviously adoption is a first step to assess) and multidimensional impact studies. Generally a range of models and analytical tools have to be deployed in a judicious manner. Although there has been methodological progress, in particular multidimensional impact studies still require improvement and additional emphasis.

Livelihood approaches are used with increasing frequency in multi-dimensional impact assessments (e.g. Adatao and Meinzen-Dick 2007). Policy and NRM research epIAs have a less impressive record. They are for good reasons more difficult and resource-demanding to conduct. How to address NRM epIAs has long been a concern with the CGIAR (e.g. Fujisaka and White 2004). Two special difficulties in implementing epIAs relate to attribution of impact and the counterfactual evidence. Attribution is about how benefits and impacts can be casually linked to the research in question, when several stakeholders have been involved in various capacities, and confounding factors may have played a part. The counterfactual evidence asks what would have happened in the absence of the research project. Counterfactuals are cumbersome to construe, and seldom look at the merits of alternative research investments, as would be standard operating procedure in investment analysis. Moisture stress, for instance, can be addressed through short maturing and water efficient varieties, but also through rainwater harvesting or combinations of both. Such counterfactuals may fall under different research budgets and/or institutional domains, and are therefore, disregarding methodological problems, in practice precluded. Both these difficulties are more cumbersome in policy and NRM research. As a guideline, up to 3% of a research institute’s budget should be set aside for impact assessment with epIAs constituting a sizable share of that amount. It is underlined that epIAs should be considered part of science, not tack-ons to fulfil donor requirements.

Ex ante impact assessment

Just as epIAs have become more important, ex ante studies are increasingly recognised as vital to improve allocation of scarce resources to activities that contribute to the development objectives of the research organisation. A blend of models and tools are required to secure data answering four basic questions: 1) where is the impact likely to occur; 2) by whom will the impact be felt; 3) which impacts will be generated; and 4) what is the value of these impacts?” (Thornton et al 2003).

Some 15 approaches to ex ante assessment approaches are illustrated below:

Village workshops/discussions, stakeholder consultations, key informant interviews; community-level formal surveys; community-level formal surveys for looking at adoption and impact; financial and economic analyses of the production effects of new technologies; transect walks, aerial photography; spatial analysis, GIS, satellite imagery; market studies; economic surplus methods; in-depth anthropological/sociological and characterization studies, farmer assessments; participatory nutrient flow diagrams; follow the technology, participatory technology development; hard biophysical simulation models of component processes and interactions; softer biophysical models of component processes and interactions; multiple objective mathematical programming models of the household; rule-based (softer) models of the household (based on Thornton 2003).

The constant assessment – learning cycle

Integrated Natural Resource Management (INRM, see 5.6.3) research is radically different from traditional technology research. INRM tries to build the capacity of natural resource managers to manage change in sustainable ways. This is an inherently indeterminate and complex process. Douthwaite et. al. (2004) describe the central role of monitoring and evaluation (M&E) through the different stages of research planning and implementation. Evaluation would change from a focus on accountability to support learning and adaptive management of stakeholders involved in a project. “… more emphasis should be placed in the use of evaluation to improve, rather than prove, on helping to understand rather than to report, and on creating knowledge rather than taking credit” (ibid). Ex post impact assessments would not quantify impact based on inappropriate economic models but rather use evidence from many sources that the intervention has contributed to impact. Effective M&E, based on a shared impact pathway vision, “will identify and describe incipient processes of knowledge generation and diffusion, the emergence and evolution of innovation networks, and the creation of organizational capabilities” (ibid). Future impact assessment will have to convincingly show how this growth in processes and capabilities contributed to wider-scale development changes. New methods have to be applied to follow the process, including livelihood approaches, simulation modeling and indicator combinations.

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