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Technology in Society 36 (2014) 26–38

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Technology in Society

journal homepage: www.elsevier .com/locate/ techsoc

Rhythms of the herd: Long term dynamics in seed choice byIndian farmers

Glenn Davis Stone a,*, Andrew Flachs a, Christine Diepenbrock b

aDept. of Anthropology, Washington University, St. Louis, MO 63130, USAbDept. of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA

a r t i c l e i n f o

Article history:Received 21 September 2013Received in revised form 11 October 2013Accepted 12 October 2013

Keywords:Technology adoptionAgricultureDecision-makingGenetically modified cropsIndigenous knowledge

* Corresponding author. Tel.: þ1 314 935 5239.E-mail address: stone@wustl.edu (G.D. Stone).

0160-791X/$ – see front matter � 2013 Elsevier Ltdhttp://dx.doi.org/10.1016/j.techsoc.2013.10.003

a b s t r a c t

Scholars in many disciplines have approached the question of how humans combineenvironmental learning (or empirical assessments) and social learning (or emulation) inchoosing technologies. As both a consumer item and the subject of local indigenousknowledge, commercial crop seeds provide a valuable window into these processes. Pre-vious research on seed choices by cotton farmers in Andhra Pradesh, India, uncoveredshort-term seed fads, or herding, indicating agricultural deskilling in which environmentallearning had broken down. Unknown was if the faddism (and the underlying deskilling)would continue or even be exacerbated by the spread of genetically modified seeds. Datacovering 11 years of seed choices in the same sample villages are now available; wecombine analysis of this unusual data set with ethnographic observation. We find thatherding has continued and intensified. We also find an unexpected emergent pattern ofcyclical fads; these resemble classic models of successive innovation adoption whereperiodicity is introduced from outside the system, but we argue that it periodicity isactually generated by an internal dynamic.

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1. Introduction

For farmers, says a recent book on the wisdom ofcrowds, “choosing the right variety. is the most importantdecision they can make, so it’s perhaps not surprising thatthey would make those decisions on their own, rather thansimplymimicking thosewho came before them” [1]. On theimportance of seed choice there can be no argument, butthe relationship between individual decision-making andmimicry is not so simple. Seeds naturally pose specialchallenges to the farmer as experimenter, particularlybecause they have vital qualities that become apparentonly over time or under special circumstances [2].

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India’s cotton farmers provide a particularly interesting– if also troubled – case study in decision making on seeds.India is the world’s biggest cotton planter and has some ofthe world’s lowest yields; debt and crop failure have beenlinked to cotton farmer suicides [3,4]. Although the mostlucrative cash crop for many, cotton is also a notoriouslyunreliable earner because it is input-intensive, vulnerableto numerous pests, and sensitive to market fluctuation.Seed is usually repurchased on the market every year sincemost Indian cotton is hybrid, and the cotton seed market isunreliable, offering hundreds of rapidly-changing and oftendeceptively-labeled seed brands [5]. Seed choice has beenfurther complicated by successive introductions of trans-formative technologies since the 1990’s: the spread ofhybrid seeds, followed by several waves of insecticidetechnologies, followed by genetically modified (GM) Bt

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 27

cotton seeds [6,7].1 The Bt seeds have brought a wave ofcontroversy as well as a quickening of an already dizzyingpace of technological change, from three seed brandscontaining a single transformation event in 2002 to sixdifferent events, sometimes stacked in various combina-tions, in over 1000 seed brands.

Farmers must navigate these kaleidoscopic conditions inmarkets, technologies, and agroecology as they select seedsthat vary in cost, in desired time to harvest (which affectslabor mobilization and credit management strategies), innumber and size of bolls (which impacts harvesting costs), inwater requirements (in a region with fickle monsoons andunreliable irrigation), and in insect resistance (in anareawitha dozen major cotton pests).2 Insight into how farmers goabout choosing seeds under these conditions may be valu-able in building theory on indigenous technical knowledgeand on evaluation and adoption of technology; itmay also beof practical relevance to our understanding of effects of GMtechnologies in developing countries, as the debates on thisin India invoke claims about the farmer’s ability to assess thenew seed technology. GM seed advocates have argued boththat the Indian farmerswho adopt GM seeds are too smart tobe second-guessed, and that GM seeds are suited todeveloping-world farmers who just cannot be taught.3

Technology adoption has been modeled in very differentwaysover theyearsby several disciplines [10,11]. But runningthrough the diverse literatures is the recognition of twofundamental human strategies of choosing technologies. Thefirst involves some formempirical assessment of the benefitsof the technology (often called “payoff information”); thesecond involves emulating others. These basic strategies gobymany names, including environmental and social learning(or cultural transmission) [12], and innovation and imitation[13]; for a review see Conley and Udry [14]. For simplicity wewill term them the better mousetrap and the copycat.

Implicit in the better mousetrap strategy is that a newtechnology (or a new variant of an existing technology)mayoutperform extant technologies, and that its superiority isdiscernible by users: the farmer sees that the seed produces

1 GM plants are made by exposing target cells to a vector containingDNA from disparate sources. After exposure, target cells are screened toisolate the cases in which the DNA has been integrated into the targetcell’s genome. Each case is termed a transformation event, or simply anevent. Each event is unique, sensitive to where and how the introducedDNA is integrated. Once an event has been isolated and evaluated, thecells are cultured and grown into plants so that it can be introduced intovarious crop varieties by conventional breeding. Bt is Bacillus thur-ingiensis, a soil bacterium. Proteins expressed by its “Cry” genes are toxicto some Lepidopterans that are common cotton pests.

2 This list of considerations is far from complete. For instance, someseeds have systemic problems such as failure to germinate, and farmersare sometimes given government-mandated rebates. Some brands turnout to be non-viable “spurious seed,” which can be ruinous, and someseeds are available only through the black market, which leaves thefarmer without a receipt needed for recompensation.

3 Agriculture minister Sharad Pawar asserts that the Indian farmer hasadopted GM seeds and that “The farmer is wiser than me” [8]. On theother hand, biotechnologist Martina McGloughlin claims “for years peo-ple have tried to change cultural practices of these farmers, and it justhasn’t worked. It has been a complete failure, because . you have to re-educate them as to how to modify their farming practices themselves. Butwith biotech, the technology is in a seed. All you have to do is give themthe seed” [9].

more crop, the computer maker sees that the micropro-cessor runs faster, the physician sees that the antibioticcures more bacterial infections. However in practice, tech-nologies varyenormously in trialability–howwell they lendthemselves to empirical evaluation through use. Literalmousetraps are on the “trialable” end of the continuum; onthe other hand, many agricultural technologies are verydifficult to trial under actual farming systems. Breeding ofcrop seeds relies heavily on field trials, but these are con-ducted under highly artificial conditions [15].

The copycat dynamic refers to the common practice ofbasing technologyusedecisionsonothers’decisions. Copycatdecisions are based on who is (or how many are) using thetechnology, rather than how well the technology is workingfor them. Humans copy each other inmatters both grave andfrivolous, oftenpickingourmodels on social criteria thatmayhave nothing to do with how well the technology works.4

Terms for emulation, including copycat and the verb to ape[17], have negative connotations, but emulation is a funda-mental and indispensable practice in a species adapted tosuchawide range of practices andhabitats [14]. Patterning incopycat decisions may lead to the emergent phenomenon ofherding, defined here as widespread conformity that is notobviously adaptive. Wearing warm clothes in winter wouldnot be considered herding because it is clearly adaptive;young people specifically wearing Ugg boots would [cf. 18].

Our long-term investigation of decision-making amongfarmers in Warangal District, Andhra Pradesh, has gener-ated an unusual dataset on seed choices. Previous publica-tions on the early years of the study showed a strikingpattern of herding or seed faddism. Defying social sciencedogma (and biotech industry rhetoric) about farmer tech-nology adoption being based on careful experimentation,Warangal farmerswere found to routinelyplant seeds aboutwhich they knewvery little, often abandoning a seed beforeplanting it long enough to learn much about its perfor-mance. Ethnographic investigations showed the underlyingcause to be hyper-reliance on copycat emulation due to se-vere impediments to better mousetrap trialing. Stone [5,19]used the case to theorize agricultural skilling and to iden-tify the three causes of agricultural deskilling as inconsis-tency, unrecognizability, and accelerated technologicalchange. Genetically modified seeds were adopted duringthe early years of this study, with the promise of alleviatingthe severe attacks of Lepidopteran (caterpillar) pests. Butseeing the pest-induced losses as a symptom of largerproblems, Stone asked if the new round of technologicalchange could alleviate symptoms at the expense of exacer-bating the underlying cause. Since deskilling had earlierbeen revealed in seed choice patterns, further data on seedchoices have been collected as Bt seeds have spread.

We report here on a unique dataset on seed choicesspanning 11 years, along with extensive ethnographicdetail on the dynamics of decision-making. This body of

4 Francis Galton referred to the “mindless gregariousness of herd ani-mals” [16]. But of course herd animals are not at all mindless – they are ofa very firm mind to conform to the behavior surrounding them, which isadaptive, in contrast to “mindful” idiosyncratic behavior which may bemaladaptive.

Fig. 1. Hybrid corn adoption in Iowa, based on Ryan and Gross ([22] Fig. 1). The cumulative percentage curve became the well-known s-curve of adoption.

6 The technology Rogers used as his empirical case study was theherbicide 2,4-D, developed by wartime research on chemical weapons

G.D. Stone et al. / Technology in Society 36 (2014) 26–3828

information allows for an unprecedented view of somelongitudinal dynamics of decision-making and technologyadoption. In this analysis we show that predictions ofexacerbated deskilling are borne out, but in an unex-pected form: seed fads over the past 11 years have peakedwith a remarkably regular periodicity. The patterns ofseed adoption and disadoption bear striking similarities toclassic marketing science models of adoption and substi-tution for successive high-technology products such ascomputer microprocessors. However we offer a differentexplanation of the dynamics of seed herding, includingrecognition that herding may in some cases be intentionalrather than a by-product of the copycat dynamic.

2. Divergent perspectives on technology adoption

Research on technology adoption has a long history inwhich agricultural seeds have played a key role [20,21].Pioneering work by sociologists Bryce Ryan and Neal Grossfocused on the diffusion of hybrid maize seed in Iowa[22,23]. Hired by breeders to investigate farmers’ appar-ently irrational reluctance to adopt the new hybrid seeds,they focused on the social aspects of adoption, such as whoadopted early versus late and how farmers influenced eachother. Iowa seed data provided a classic S-curve of adoptionwhich is the cumulative plot of a normal curve (Fig. 1). Thiswould later be seen in other technologies, from air-conditioners to antibiotics [10].

Sociologist Everett Rogers drew on the hybrid seedresearch to devise a generalized model of the sociology oftechnology adoption [25]. It posited five categories ofadopters, ordered by when they adopt, as depicted in Fig. 2.The first 2.5% to adopt were termed innovators,5 with the

5 Rogers did not specify what led to their adoption, but simply alludedto earlier research on “informal leaders” in farm communities. Interest-ingly, these “informal leaders” (defined by how frequently their advicewas sought) were not early adopters at all, but rather traditionalists; theearly adopters tended to be wealthy large farmers.

subsequent stages forming a normal distribution throughtime.6 Rogers did not explicitly distinguish innovation fromimitation, and he left open the issue of what drove initialadoptions; but to rural sociologists the fundamentals ofadoption seemed to have been largely figured out, and theirinterests tended to move on in the 1960s. The socialapproach to adoptionwas, as Rogers later said [10], a victimof its own success.

But adoption studies were already being led into twoseparate, and more quantitative, directions. One trajectory,which quickly became dominant [21], was led by econo-mists who replaced the social questions with an emphasison profitability of specific technologies. The new directionwas signaled in studies of hybrid corn adoption by ZviGriliches [26,27], which pointed to the agronomic superi-ority of hybrid corn and then modeled adoption not as asocial process but as a function of the spatial aspects ofyield advantage. This was a pure better mousetrap analysisthat essentially ignored social learning, and implicitlyassumed that environmental payoff information droveadoption patterns.

Citing Ryan and Gross’s data, sociologists Havens andRogers retorted that “rate of adoption is not related to prof-itability but is related to cumulative percentage of adoption”[11] – a clear copycat explanation. Yet such utility-basedmousetrap models have dominated adoption studies sincethe 1960s.7 Indeed there has been a surge in such studies asGM crops have worked their way through the agriculturalsystems in many countries (e.g., [31]). These studies have

and introduced to farmers in 1946. It is interesting that the next gener-ation of GM crops is resistant to 2,4-D and dicamba. If and when these areapproved, adoption rates of the same herbicide are likely to be closelyanalyzed again.

7 In recent years, a few economists have returned to consideration onthe role of social interaction in technology adoption; see Bandiera andRasul [28], Brock and Durlauf [29], and Munshi [30].

Fig. 2. Categorization of adopters of agricultural technology by Rogers [25].

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 29

taken on an unmistakable political-economic hue, as theyspeak to the highly charged and polarized debates overwhether GM crops are a “failure” or “success” [32–35].

Our aim is not to privilege mousetrap or copycat models– we are more interested in their interplay – but it isimportant to recognize a pervasive problem with utility-based explanations of seeds. Griliches attributed the mid-century rise in corn productivity entirely to hybrid seed;his data on spatial patterning of hybrid adoption came fromgeographer Andreas Grotewold’s (1955) study of cornproduction. But while claiming to analyze the impact ofhybrid corn, Griliches actually did nothing of the kind, as isclear in Grotewold’s actual study. Grotewold does writethat hybrid seed was an important factor in the rise in cornproductivity between 1935 and 55, but that numerousother key technologies were being adopted simultaneously.This established a pattern that has persisted: attributingadoption and performance advantages to technologies thathave not been truly isolated from the confounding factorsthat are ubiquitous in agriculture [for a recent examplefrom Indian cotton, see [32,34].

The second trajectory was pioneered by marketing re-searchers, led by Frank Bass who translated Rogers’ schemeinto a mathematical model of adoption. The resultingpaper, “A New Product Growth for Model Consumer Du-rables” [36] with a famous typo in its title,8 explicitlyidentified and combined the better mousetrap and copycatdynamics under the terms innovators and imitators. In-novators are implied to be trialing technologies, initiatingan adoption curve determined by a coefficient of imitation.The new technology that instigates an adoption curve isexternal to the model, represented by a coefficient ofinnovation term that does not interact with other terms.

Norton and Bass [13] went a step further in modelingadoption and disadoption of successive generations ofproducts (Fig. 3). In this influentialmodel, the dropoff on theadoption curve represents not saturation (fewer adoptionsbecause more have already adopted) but substitution

8 Bass later wrote [37], “The correct title should be: ‘A New ProductGrowth Model for Consumer Durables.’ I suppose that I was so excitedabout having the paper accepted for publication that I failed to carefullyproofread the galley proofs.”

(fewer buying because more are buying something else).The key driver of the rhythms of adoption is technologicalimprovement external to the dynamics beingmodeled; it isassumed that new technologies are commercialized as theyare invented, and that “each generation is introduced to themarket before its predecessor has been fully diffused to itspotential population” [13]. In otherwords, the periodicity inthe predicted curves is external to the model: consistentwith Bass’s earlier use of an external coefficient of inven-tion, each successive peak occurs when a company inventsand chooses to introduce a new product. This is importantbecause in our analysis of cotton seed choices the period-icity appears to be an internal dynamic. (See also Foster andRosenzweig [38] for amodel of technology substitution thatdepicts changes in market share.)

3. Theorizing environmental and social learning

This selective sketch of technology adoption researchshows that the better mousetrap and copycat dynamicsmay be conceptualized separately, which allows for theconstruction of more elegant models suited to the pre-dilections of different disciplines. Yet in terms of the actualbehavior of decision-makers, the relationship between thetwo dynamics may be more important than either alone. Aclassic early work on social learning that defined matched-dependent behavior as when “the leader is able to read therelevant environmental cue, but the follower is not; thelatter must depend upon the leader for the signal as towhat act is to be performed” [39]. Stone [5] has noted thatfrom an ethnographic standpoint the distinction oftenstrained. “Even a direct environmental observation madeon one’s own crop (“Brahma cotton yielded 6 quintals/acrefor me last year”) is likely to be interpreted or contextual-ized through a form of social learning (“which was muchmore than my neighbor said he got with the same seed”).Even a classic case of conformist adoption (“I am plantingBrahma because my neighbors are”) assumes at least anindirect environmental basis (“and they wouldn’t all beplanting it unless someone had an indication it would dowell”).” The Bass models recognize the importance of thisrelationship but treat it mechanistically. Bass wrote that“Adoptions of the product are made by “innovators” and

Fig. 3. The Norton Bass model [37].

G.D. Stone et al. / Technology in Society 36 (2014) 26–3830

“imitators”, and “the importance of innovators will begreater at first but will diminish monotonically with time,while the imitation effect will increase with time” [13].

Using a different terminology, Henrich [12] shows thatadoptions based purely on environmental learning wouldproduce not the common s-curve but the much rarer r-curves. S-curves must result from environmental learningcombined with social learning, with choices of who to learnfrom being strongly shaped by various intrinsic categoriesof bias. One is prestige bias, in which a farmer chooses amodel to emulate on the basis of prestige –which is not thesame as observing the model’s actual results with thetechnology. Another is conformist bias, in which a farmeradopts a practice when (and because) it has been adoptedby many others.9 Some have also stressed what might becalled social proximity bias, referring to the reliance on in-formation from friends and family [28,38].

Butdespite theconceptual convenienceof thedistinction,in practice environmental learning is entangled with all ofthese cultural biases. Consider prestige bias: there certainlyare cases where exemplars are emulated on behavior clearlyunrelated to what makes them “exemplary.”10 But morecommonly, prestige is equated with general success whichcannot be disconnected from decision-making. When Ifollow a prestigious farmer’s seed choice, I am in partassuming to be harvesting some environmental knowledge,even if he isn’t specifically knownas an astute judge of seeds.(The point becomes important in the following case study, as

9 Both conformist and prestige bias become especially important whenfarmers are operating in the domain of loss: given the inherent uncer-tainty in seed choice, the farmer makes a choice for which he cannot becriticized even if it fails.10 The senior author recalls buying a pair of Dingo Boots as an adoles-cent because they were being hawked by Joe Namath, despite knowingthat Namath’s footwear had nothing to do with his athletic prowess, andthat Namath didn’t even really wear Dingos.

low-caste farmers follow the lead of high-caste farmers eventhough caste is an inherited status that in itself reflectsnothing about insights into seeds.)

Therefore rather than privileging one type of learning itis instructive to view the relative contribution of the typesof learning as situational. We can posit that reliance onsocial learning should increase where environmentallearning is costly and/or inaccurate [14,40,41]. This isparticularly relevant to agriculture because of the greatvariation in the cost/accuracy of environmental payoff in-formation available to farmers. Accurate, locally-generatedinformation on seed performance may be readily availablefor true-breeding (non-hybrid) seeds with an extensivehistory of local use; farmers would have a sound basis forevaluating and comparing a new seed (or input or practice).On the other hand, with deceptively labeled, annually-repurchased, rapidly-changing seed brands containing aprocession of poorly understood genetic technologies,being grown under the challenging conditions of Indiancotton, farmers may have access to very little reliable localpayoff information. We should expect them to rely veryheavily on emulation to choose cotton seeds, although notnecessarily for rice seeds. Such over-reliance on emulationmay lead to maladaptive beliefs and practices, especiallywhen the environment changes very rapidly [41].

Parallel ideas have been developed in economics in themodel of the information cascade [18,42,43]. Similar to theabove concept of conformist bias, these writers point outthat each time an individual weights emulation over theirown signal (i.e., environmental payoff information), theycreate a negative externality by raising the chances the nextindividual will do the same, thus increasingly eroding theweight of payoff information. Eventually even very positivepayoff information may be overridden. When payoff in-formation is expensive, unreliable, and/or overridden bycascades, increasingly emulative decisions may have asnowball effect producing herding. While herding is

Fig. 4. AWarangal farmer shows off the two brands he planned to plant as acontrolled comparison. Unbeknownst to him, the two brands were preciselythe same seed, designated NCS-207 by the company that developed thehybrid. There were two other brands on the market that year that were alsoNCS-207. In some cases there has also been a problem of “spurious seed”, inwhich packages do not even contain the seeds on the label [5].

11 See note 1 for explanation of transformation events.12 Stone also predicted that deskilling would be exacerbated by the“unprecedented wave of wildly contradictory advertising, campaigning,and lobbying aimed at farmers, with false information being spread by itsbackers and detractors alike” (Stone 2011:394).13 In 2013 interviews in Warangal villages, numerous farmers respon-ded that they did not know if they were growing Bt, or that they werenot, although all seed sold in the area are Bt. Flachs attributes some of theconfusion to uncertainty over whether Bt refers to a company or a brand.

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 31

sometimes defined simply as convergence on a similarbehavior, it is used here to refer more specifically toconvergence on a behavior that is less attributable to directutility than to social emulation.

Diverse literatures exist on herd behavior, ranging fromcognitive psychologists and marketing scholars’ work on“heuristics” dealing with social aspects of information–environment interactions (e.g., [44]) to popular writing[1,45]; for a summary of other approaches to herding, seeOrléan [46]. Our case study in Warangal provides aparticularly striking case of herding in seed adoptions and awindow into the intersection of the better mousetrap andcopycat dynamics.

4. Cotton seed herds in Warangal

In the key cotton-growing district of Warangal, AndhraPradesh, Stone [5] investigated farmer decision-makingand the information environment in which it occurs. Hedefined agricultural skilling as the process of farmersdeveloping the ability to perform with a technology undervariable conditions. Skilling had been severely impeded byinconsistency, unrecognizability, and accelerated brandand technology change (especially in seeds but also inpesticides). Of course farmers commonly deal with incon-sistency in weather, pests, and/or market conditions, butWarangal cotton farmers are bedeviled by particularlyrapid, complex and unpredictable variation in insect at-tacks [5]. Unrecognizability plagues farmers in severalways, including the deceptive branding of seeds exempli-fied in Fig. 4. The rapidity of seed change is reflected ininput vendor surveys showing a 69.2% turnover in seedbrands between 2003 and 5 [5].

Stone theorized the phenomenon of agricultural des-killing as the result of impeded flow of environmentalpayoff information, leading here to a crippling of environ-mental learning and thus an overwhelming reliance onsocial learning. This led to a striking pattern of highlylocalized seed fads with little agroecological basis. Forinstance, Kalleda and Ravuru are adjacent villages with thesame growing conditions. In 2004 the top seed in Kalledawas Gemini, which almost no one in Ravuru planted; in2005 the leading seed in Ravuru was Vikas, which virtuallyno one in Kalleda planted. Both seeds disappeared after 2years of sales. In the five growing seasons between 2003and 2007, Ravuru had five different top seeds: Brahma,Bunny, Vikas, RCH-2, and Mallika.

The skilling process was further complicated in 2002 bythe arrival of Bt seeds. Thesewere little planted inWarangaluntil the 2005 season, when they caught on suddenly. Theadoption of Bt seeds coincided with a change in, andintensification of, seed herding: village-specific fads gaveway to a district-wide fad as villages converged on a singleBt seed called RCH-2. Although the Bt version of this seedwas new, its properties were notwell known, and therewasno clear evidence of agronomic superiority (Stone 2007:82–83), it rocketed towild popularity across the district; in onevillage, 96% of all cotton-buying households bought at leastone box of this seed, out of over 60 available seeds.

The interpretation of Indian farmers’ adoption of Btseeds is itself quite politicized [47], but Stone’s argument

is that the spread of Bt seeds would exacerbate agricul-tural deskilling and herding [6]. There are concrete rea-sons to expect the spread of Bt seeds to have such aneffect, and also sound reasons to expect this to result inintensified herding. Bt cotton was predicted to have thiseffect because it brought a new category of acceleratedtechnological change: not only a flood of new seedscontaining the original Bt gene, but then a procession ofnew Bt genes and events.11 Each event behaves differ-ently, and its behavior also varies with the geneticbackground into which it is bred [48]. By 2009, Bt seedshad totally replaced conventional seeds in Warangal, andtechnological change had been further accelerated by theincorporation of six different events in 522 different hy-brids [49]. By 2012, 1128 Bt hybrids had been approvednationwide and a plethora of new constructs were beingtested [50,51]. The profusion of constructs greatly exac-erbated the problem of unrecognizability: the farmershad little inkling of what to expect from the differentcombinations of genes and regulatory sequences, andadvice from input vendors was sketchy and unreliable.12

Indeed, many farmers in Warangal do not even knowthat they are planting Bt seeds.13 Meanwhile the problemof inconsistency has in some ways worsened as thefluctuations in pest populations have been changed, butnot mitigated, by Bt seeds. During the first few years ofwidespread Bt seed use, predation by Lepidopterans did

G.D. Stone et al. / Technology in Society 36 (2014) 26–3832

decrease, but aphid attacks surged [6]; in recent years,despite the procession of Bt constructs, predation byLepidopterans has returned. (In the sample of farmersdescribed in note 13 over half reported Lepidopteranssuch as Helicoverpa sp. and Spodoptera sp. as the mostserious insect pest.) In sum, all three causes of agricul-tural deskilling have worsened.

Since deskilling had been indexed in previousresearch by herding, we set out to collect a longer run ofseed choice data to look at what diachronic patternswould emerge in farmer decision-making, with particularinterest in increased herding. We can now report onlong-term herding and also draw on additional ethno-graphic findings to further illuminate the social dynamicsof emulation.

5. Longitudinal analysis

Data on agricultural decision-making and production inWarangal have been collected from 2002 through 2012.Stone’s initial analyses [5,19] used data from nine villagesbetween 2002 and 2005, and he later [6] used data on asubset of four villages to analyze impacts of Bt seed adop-tion between 2003 and 2007. The villages – Kalleda, Rav-uru, Saireddypally and Gudeppad (Fig. 5) –were selected toprovide a cross-section of growing conditions, ethnicgroups, wealth, caste, and information connectivity [6].14

Farmers were randomly sampled. Surveys obtained seedchoice data from these villages through 2012, with a gap in2009–10 in Saireddypally and Gudeppad. Farmers oftenbuy two or sometimes more brands each year (primarily inan attempt to generate comparative payoff information,although such experiments are often in vain as noted inFig. 4). Each brand bought each year is recorded as a choice.Comprising data on 3162 cotton choices over 11 years, thisdataset provides a picture of the longitudinal dynamics ofdecision-making (see Table 1).

In this analysis the variable % OF CHOICES gives thepercentage of all cotton choices in a given year accountedfor by a given seed. The variable % OF HOUSEHOLDS givesthe percentage of all cotton-buying households that boughtat least one box of a given seed. For example, if everyhousehold bought one box of Brand X and one box of some

14 Gudeppad is a village of around 1100 in an area with a relatively highpercentage of “black cotton soil” that is generally regarded as best suitedto cotton cultivation. This area also has the highest percentage of landplanted to cotton in Warangal; indeed the heavy reliance on cotton hasleft the area vulnerable to downturns in the cotton market, which mayhelp explain why it has been plagued with Warangal’s highest rate offarmer suicide [52]. Gudeppad has a largely indigenous non-tribal pop-ulation with a range of castes represented. In a measure of informationconnectivity, it measured medium–high [6]. Kalleda and Ravuru are in agenerally poorer area, with little black cotton soil and a lower commit-ment to cotton cultivation. Kalleda, with a population of around 3,000, issimilar to Gudeppad in ethnic and caste composition but informationconnectivity is Medium-Low. In Ravuru, with a population around 800,the population is mostly tribal (Lambadi or Ghor), with very low levels ofeducation and low information connectivity. Saireddypally has a popu-lation of around 1500 and a moderate percentage of black cotton soils. Itspopulation is dominated by Andhra (mostly Kamma caste) farmers whoimmigrated from coastal areas several decades ago. These groups tend tobe prosperous, educated, and with high information connectivity.

other seed, % OF CHOICES would be 50% and % OFHOUSEHOLDS would be 100% for Brand X.

5.1. Village-level patterning

Let us first consider patterning at the village level. Fig. 6shows % OF CHOICES for each village over the 11-yearstudy, excluding rarely bought seeds. Four features war-rant attention.

1. The 2005 stampede to RCH-2, assumed to be a short-termfad by Stone (2007),was just that: after accounting for 44%of all seed choices in 2005 (and with % OF HOUSEHOLDSrising to 96% in one village, as discussed below), this seedhad dropped to 12% by 2007 and 0% the next year.15

2. The RCH-2 fad was preceded by a fad for Brahma andwas followed by comparable fads for Mallika and thenfor Neeraja and Dr. Brent. Neeraja and Dr. Brent areseparate brands, but for some purposes they may belumped in the analysis. They are from the same com-pany, released around the same time, contain the sameBollgard II event, and are often treated as interchange-able by local farmers. For instance, when one villagecommissioned a buyer to smuggle in seeds from aneighboring state (described below) many farmers putin an order for “Neeraja or Dr. Brent”.

3. Seed fads have continued to be consolidated since 2005.Between 2002 and 2004 there were many instances oflocalized fads: Gemini in Kalleda, Bunny in Ravuru,Brahma in SRP, and Chitra in Gudeppad [5]. But from2005 on, all villages usually had the same favorite seed.This is one sense in which herding has increased withthe spread of Bt seed.

4. Also striking is the trajectory followed by most fads, withlow adoption being followed by a sharp 1-year peak andthen a drop-off. The shape of fads is discussed furtherbelow.

5.2. District-level patterning

Fig. 7 shows district wide figures (all 4 villages com-bined) for % OF CHOICES for the most popular seeds, andFig. 8 shows % OF HOUSEHOLDS for top fad seeds in spe-cific villages as well as for the entire sample. Four points areof particular interest.

1. Herding has indeed continued and intensified as perpredictions, as seen in fads and measured both by % OFCHOICES and % OF HOUSEHOLDS at the district level.The lack of agroecological rationale for seed popularity,documented in previous studies, has persisted. Askedwhat desirable properties Neeraja or Dr. Brent offered in2012, farmers gave highly contradictory answers (seeStone (2007:81) formore on farmer knowledge of seeds).

2. The increasing consolidation of fads across villages isespecially striking at the district level. This appears toindicate erosion of the local nature of indigenous

15 RCH-2 was first introduced in 1992; its Bt variant was introduced in2004. It was still on the market as of summer 2013.

Table 1Numbers of farmers surveyed and cotton seed choices for four villages, 2002-2012.

Village 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Total

Kalleda Farms 40 29 34 27 11 22 25 12 12 32 24Choices 53 37 50 37 16 37 49 22 23 53 51 428

Ravuru Farms 36 30 63 71 30 62 63 31 32 42 41Choices 39 31 73 95 39 113 125 52 80 92 102 841

SRP Farms 17 20 80 66 36 57 58 0 0 39 39Choices 29 31 140 93 65 132 136 0 0 116 145 887

Gudeppad Farms 67 66 90 68 44 60 61 0 0 31 35Choices 143 150 189 78 59 127 128 0 0 58 74 1006

Fig. 5. Map of Warangal District and study villages.

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 33

knowledge that various writers have identified as key tosmallholder production [53,54]. Word on the hot seedtravels across the district, driving adoptions regardless oflocal conditions. Fad peaks tend to be times of optimism,as the large numbers of farmers using the same seedbuilds confidence that the season will be profitable.When the seed inevitably fails to deliver widespreadprosperity, villages tend to turn inward, often devel-oping local mini-fads (Fig. 6), until a major fad beginsanew.

3. The longitudinal data reveal fads remarkable in theirperiodic regularity, peaking every 3 years. Fig. 7 providesa composite picture of fads by selecting the top 10 seedfads for each village, aligning their peaks, and averagingtheir village-specific market shares. Fads on average riserapidly to a sharp 1-year peak after which they decline atapproximately the same rate. The herd does not return toprevious fed seeds with the notable exception of Brahma,which appears to have a special role as the fallback seedafter its fad had run its course (note 2006 and 2009 inFig. 7).

4. The intensity of the herd effect is better indicated by the% OF HOUSEHOLDS patterns in Fig. 8. Note the severalinstances in which over ¾ of village households bought

the fad seed, and the case of Gudeppad in 2005 when afull 96% of farms bought RCH-2. In many cases thesenumbers would have been even higher had shops notrun out of fad seeds.

6. Adoption and disadoption

One of the most robust findings in innovation adoptionresearch is the s-curve noted above. Mathematically, this isthe cumulative frequency distribution of a normal curve; interms of underlying dynamics it reflects the generalizedsequence described by Rogers, Bass and others in which asmall group of innovators is followed by a rapid accelera-tion of adoptions and then a proportionate dropoff due tosaturation. As noted above, the slope of the adoption curveresults from environmental learning combined with sociallearning [12]. This is a model of actors choosing betweentwo states: old mousetrap vs. new mousetrap, in the termsused above. But when there are successive generations oftechnology, as modeled by Norton and Bass [13], the un-derlying dynamic is assumed to be driven by a processionof better mousetraps. For instance, the pattern of curves inFig. 3 depicts adoption of DRAM chips that are released asthe engineers produce greater power and more speed.

Fig. 6. Village-specific trends in % OF CHOICES. Data are unavailable for 2009–10 in Saireddypally and Gudeppad; the numbers in the light boxes are extrap-olated. Dashed lines before 2006 indicate non-Bt hybrids.

G.D. Stone et al. / Technology in Society 36 (2014) 26–3834

These are highly trialable performance traits; accuratepayoff information is readily obtainable and users have noproblems with inconsistency, unrecognizability, or rate ofchange.

Despite its resemblance to the Norton-Bass adoptioncurves, several lines of evidence indicate that the underlyingdynamic in Warangal seed choices is fundamentallydifferent. The model of successively “better mousetraps,”each with advantages discernible to users, fits cotton seedchoices only weakly. While there may be improvements inseeds that are statistically discernible under controlledconditions, it is virtually never the case that a new seed of-fers a consistent and recognizable benefit to the farmer. Inthe much-discussed case of illegal Navbharat-151 seeds inGujarat [55,56], one seed apparently did offer a readilydiscernible advantage, but its uniqueness makes it anexception that proves the rule. This locally-adapted hybrid,bred by a Gujarat seed company, contained an unapprovedtransgenic Bt trait. In 2001, as a severe outbreak of boll-worms decimatedmany farmers’ fields, the seedwas said tovisibly outperform others; this was probably due to somecombinationof superior germplasmand theBt trait towhichthe pests had not yet developed resistance. But we know ofno other cases where a single seed had given a consistentanddemonstrablygreater yield thanothers.Normally, yieldsandprofits fromanygiven seedarehighly variable, as seen inStone’s ([5] Fig. 9) analysis of yields for 17 top seeds.

Rollouts of new transgenic traits have correspondedpoorly with Warangal seed fads. The 2005 rush to the Bt

version of RCH-2 can be linked to Bt seeds having been onthe market for four years, but the Mallika seed thatreplaced RCH-2 in 2007 contained the same technology(event 531), and moreover Mallika was only one of dozensof seeds in local shops containing event 531. More impor-tantly, 2007 was the same year seeds containing the newevent 15985 appeared in stores. The 2011 fad seeds docontain the 15985 event, but so do the fad seeds that wererising in 2012.

Aggregate trends in seed performance also are inconsis-tent with progressively better mousetraps: trends in cottonyields do not correlate well with the spread of Bt seeds ingeneral or with adoptions of specific Bt events [32,34,57].

Finally, ethnographic evidence shows a strong andconsistent pattern of farmers conforming to seed fads withscant environmental payoff information, as in informationcascades, rather than responding to signals that animproved seed has arrived. Asked what characteristics theyear’s fad seed has, farmers give wildly contradictory an-swers. Even if a seed performs well one year, a farmer mayreadily drop it after hearing about the popularity of anotherseed. (In one extreme case, nine years of success with oneseed were disregarded in favor of untested seeds seen to dowell in other fields.) Farmers’ commentaries on seed trendsalso are inconsistent with the recognition of bettermousetraps; for instance, there was no consensus on RCH-2’s and Mallika’s dramatic falls from grace, with farmersciting a wide range of cases or more commonly saying theyjust felt like trying something new.

Fig. 7. District-wide trends in % OF CHOICES. On the lumping of Neeraja and Dr. Brent, see text.

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 35

In sum, while seed choices are obviously not completelyunrelated to field performance, the assumption of agro-ecologically better mousetraps that recognizably outper-form competitors is unsatisfactory. A seed may becomelocally faddish for reasons that had little to do with payoff

Fig. 8. Village-specific and district-wide trends in % OF HOUSEHOLDS. Each bar indof the top-selling hybrid in their village; black bars depict this information for the toseed (B ¼ Brahma, V ¼ Vishwanath, G ¼ Gemini, U ¼ Bunny, C ¼ Chitra, R ¼ RCH-

information, and it may drop out of favor in the absence ofany consensus on its limitations.

Some surprising social dynamics of seed choice arerevealed by ethnography. Stone reported seed fads beinginstigated by factors such as advertising campaigns or a

icates the number of cotton-farming households that bought at least one boxp seller in the district. Letters above each bar indicate the specific top-selling2, M ¼ Mallika,2 ¼ Mallika and RCH-2 tie, N ¼ Neeraja/Dr. Brent).

G.D. Stone et al. / Technology in Society 36 (2014) 26–3836

new seed being pushed by a local vendor who got a higherprofit margin on it [5]. But there are also cases of overtgroup decision-making that defy classic models ofdecision-making. As Orléan (1995) points out, theassumption of sequential decision-making (as in informa-tion cascade theory) is unrealistic, and this is exemplified inWarangal. OneWarangal thanda (small tribal village) held agroup meeting in 2008 in which the farmers decided as agroup to all plant the same seed (eventually all but a fewdid buy the seed – Mallika). While their own explanationsof the group decision were inconsistent, it seems plausibleas an attempt to improve the quality of local environmentalpayoff information: by holding constant the seed beingplanted, the villagers turned that season’s farming into amore instructive experiment.

A different form of group decision-making was alsoevident in one of our sample villages in 2012. Anticipating ashortage of popular seeds produced by the Mahyco com-pany, district authorities in Warangal issued permits thatguaranteed individual seed packets to specific farmers atspecific shops. Naïvely designed to curb a black market, thepermit system effectively created one, with permits actingas tacit endorsements of the seed’s supposed virtues. The‘prestige’ of the state involvement helped to exacerbate thefad, and seeds sold by brokers and unscrupulous shopowners commonly fetched more than double the guaran-teed state price of Rs 930. Lacking permits or otherwiseunable to purchase their favorite seeds due to the shortage,members of this village pooled resources between relatives,friends, and neighbors to send delegates to the neighboringstate of Maharashtra to buy seeds on the black market andsmuggle them across the border. Lower caste neighborslooked to high caste, larger farmers, usually following theirchoice of Neeraja/Dr. Brent. The community paid for theirsmuggler’s transportation and expenses aswell as amarkupfor the seeds in exchange for a guarantee of their favoritebrands. However, seed brokers were warned that policefrom Andhra Pradesh had established checkpoints on theborder to Maharashtra, leading entrepreneurial smugglersto hide seeds in containers for non-permit brands, bribeborder guards, or even hide seed packets in their head-scarves and turbans. All of this increases uncertainty andrisk, both introducing potentially fake seeds and denyingthe official receipts that insure farmers against widespreadseed failure. Such wild popularity was fleeting: planting ofthe four permitized Mahyco seeds crashed from 61% of all

Fig. 9. Shape of major fads. The top 39 village fads were aligned so that theirpeaks coincided, and percentages were averaged.

seeds planted in 2012 (with Neeraja and Dr. Brent ac-counting for 48% by themselves) to 45% in 2013 (with Dr.Brent and Neeraja accounting for 34%).

Given these underlying dynamics, it is interesting to lookat the generalized pattern of the seed fad. Fig. 9 shows thecurve produced by averaging the top 39 village seed fads;the variable % OF CHOICES within each village was plottedfor each popular seed, the peak years were aligned, and thevalues were averaged for 3 years on either side of the peak.The adoption curve clearly approximates a normal distri-bution, if somewhat more peaked (leptokurtic) than thecurves in the Rogers and Norton-Bass models.

7. Conclusions

Analysis of this unusual dataset has generated three setsof findings that merit highlighting. The first concerns thecontinuing impacts of GM seed technology. Previous studiestheorized and documented evidence for agricultural des-killing in Warangal. While the deskilling preceded thearrival of GM seeds, those seeds were predicted to exacer-bate its underlying causes of unrecognizability, inconsis-tency, and rapid technological change. Our generalexpectation is that farmers should rely increasingly onemulation as environmental payoff information isdecreasingly reliable [40], and our specific expectation wasthat the rapid spread of poorly-understood, rapidly-changing and difficult-to-trial technologies in new geneticbackgrounds would lead to increased herding (Stone 2007;Stone 2011). The intensification of herding as evidenced bythe rising peakedness of fads does not offer conclusive proofof exacerbated deskilling; it is difficult to definitively attri-bute such trends in agriculture to single factors. However itcertainly is consistent with the prediction of increaseddeskilling. The intensified herding helps to confirm theunderlying theory of types of learning, in that an extremelack of reliable payoff information leads to copycat decision-making to overwhelm better mousetrap assessments.

Exacerbated deskilling resulting from quickening tech-nological change in Bt seeds is clearly relevant to thecontentious debates on the use of adoption as an index ofpositive farmer assessment of the new technologies. Thisanalysis points to the conclusion that blanket character-izations – as “smart” evaluators of new technologies or asincapable of learning – are folly. Skilling is situational:rigorous assessment of successive “mousetraps” is virtuallyimpossible in the worsening information environmentdescribed here.

Second is the surprising finding of a strong regularperiodicity in fads. While the periodicity in adoptions bearsa striking resemblance to the Norton-Bass predictions forsuccessive substitutive adoptions, we believe it is beingdriven by a different dynamic. Norton and Bass assume theperiodicity of successive products to be driven by externalinvention and business decisions, but this periodic cyclingappears to be entirely internal to the social process ofemulation and herding. Repeated four times, the cycle ishighly unlikely to be accidental; we can even predict thatJackpot seed will account for at least 50% of district-widecotton choices by 2014, to be followed by a crash in sales.

G.D. Stone et al. / Technology in Society 36 (2014) 26–38 37

The final set of insights concerns disadoption. This isinteresting given that investigation of seed choices wasinitially conceived as an inquiry into adoption – using seedadoption as a window into agricultural decision-making inchallenging environments, in a country where we (andmany others) were keenly interested in adoption of GMseeds in general. In the end it does provide a unique viewof decision-making, but with particularly notable dy-namics pertaining to disadoption. The cycles of dis-adoption that give the curves their distinctive shape differfrom the drop-offs in simple adoption models, whichcome from the rapid shrinking of the pool of those whohave yet to adopt. They also differ from the drop-offs inmodels of successive adoptions, which occur as buyersmove towards a mousetrap shown to be better by payoffinformation. The valleys in the fad curves represent thedissolution of widespread and enthusiastic herds; they areperiods of disparate casting about, indicating that hopes ofstriking gold have again been thwarted.

Funding

Stone’s research in India has been supported by theNational Science Foundation under Grants No. 0314404 and0078396; by the Wenner-Gren Foundation for Anthropo-logical Research; by Washington University; and by theJohn Templeton Foundation. Fieldwork by Flachs was fun-ded by the Lynne Cooper Harvey and Jacob K. Javits Fel-lowships; by Washington University; and by the JohnTempleton Foundation. Fieldwork by Diepenbrock wasfunded by Washington University.

Acknowledgments

Some of the data collection was overseen by A. Sudar-shan Reddy, and some datawere collected by Fiona Sloan asa participant in Washington University’s Village India Pro-gram. For logistical support we are grateful to the RuralDevelopment Foundation.

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Glenn Davis Stone is Professor of Anthropology and EnvironmentalStudies at Washington University in St. Louis. He is an anthropologistwhose research focuses mainly on ecological, political, and cultural as-pects of agriculture; in recent years he has been particularly interested inissues relating to genetically modified crops and to alternative farming. Hehas been conducting fieldwork in India since 2000, as well extensivefieldwork in Nigeria, the Philippines, and rural North America. He ispresident of the Anthropology and Environment Society and author of theblog FieldQuestions.com. Andrew Flachs is an Oberlin graduate, currentlya PhD Candidate at Washington University. His research interests includeindigenous knowledge and agricultural change in both North America andSouth Asia. He conducted ethnographic fieldwork in Warangal District in2012 and 2013. Christine Diepenbrock majored in Biochemistry atWashington University and also studied food security. She conductedseveral research projects under Stone’s supervision, including fieldwork inWarangal in Summer 2012. Now in the PhD program in Plant Breeding andGenetics at Cornell University, her focus is on field-based phenotyping andimproved nutritional quality of food staple crops.