A conceptual model for analysing input±output coe�cients inarable farming systems: from diagnosis towards design

T.J. de Koeijer a,*,y, G.A.A. Wossink b,c, M.K. van Ittersum d, P.C. Struik d,J.A. Renkema c

aWageningen Agricultural University, Department of Plant Sciences, Biological Farming Systems Group, Haarweg 333,

6709 RZ Wageningen, The NetherlandsbNorth Carolina State University, Department of Agricultural & Resource Economics, Raleigh, NC 27695-8109, USA

cWageningenAgriculturalUniversity, Department of Economics andManagement,Hollandseweg 1, 6706KNWageningen, TheNetherlandsdWageningen Agricultural University, Department of Plant Sciences, Bornsesteeg 47, 6708 PD Wageningen, The Netherlands

Received 24 March 1998; accepted 8 April 1999

Abstract

Environmental legislation is forcing a rethink about desirable crop production systems. The development of newproduction systems that meet economic and environmental objectives demands knowledge about which input±outputcombinations are feasible and optimal in practice. A review of concepts in agronomy and in farm and behavioural

economics leads to a conceptual model with a division into production levels and accompanying production restrictingfactors. The highest production level can be de®ned by merely agronomic growth factors; the next production level isrestricted by a mixture of economic and other agronomic factors. The two lowest levels are further restricted by taking

into account the socio-psychological factors.With the production restricting factors of the conceptual model the di�erencesin agronomic e�ciency of actual and theoretical input±output combinations will be analysed in order to ®nd out whichinput±output combinations will be feasible and optimal in practice. # 1999 Elsevier Science Ltd. All rights reserved.

Keywords: Sustainable farming systems; Input±output coe�cients; Agronomical e�ciency; Interdisciplinary analysis

1. Introduction

Many European countries are in the process ofdeveloping legislation that aims at drasticallyreducing environmentally harmful emissions inorder to achieve a more environment friendlyagriculture. This process forces a rethink aboutland use and agricultural production practices.

Farmers will have to adopt production systemsincorporating new and modi®ed techniques.Rossing et al. (1999) distinguish four phaseswithin the development of such production sys-tems: diagnosis, design, testing and improvement,and dissemination. An important part of thediagnosis phase should be devoted to analysis ofinput±output combinations of current productionsystems. By analysing the input±output combina-tions of the actual e�cient production techniquesand the theoretical ones, insight can be gained inwhich production techniques will be feasible andoptimal in practice.

0308-521X/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.

PI I : S0308-521X(99 )00030-X

Agricultural Systems 61 (1999) 33±44

www.elsevier.com/locate/agsy

* Corresponding author. Tel.: +31-317-48-4735; fax: +31-

317-48-4575.y Present address: LEI-DLO Detachment Wageningen, P.O.

Box 35, 6700 AA Wageningen, The Netherlands

E-mail address: t.j.dekoeijer@lei.dlo.nl (T.J. de Koeijer)

In developing new production systems an impor-tant level to consider is the individual farm, becauseit is at this level that the farmers decide what andhow to produce. Their decisions on agriculturalproduction indirectly re¯ect agricultural and envir-onmental policies, by being constrained by the lawsand regulations that embody these policies. Thefarmer bases his decisions on available farmingtechniques, on farm economic considerations andon individual preferences and perceptions. Hence,the farm level is also the level of aggregation atwhich the psycho-sociological, agro-economic andagro-ecological disciplines interact most profoundly.This means that in the analysis of theoreticallye�cient and actual input±output combinationspsycho-sociological, agro-economic and agro-eco-logical insights should be considered explicitly.Before new agricultural production systems that

meet economic and ecological objectives can beintroduced on a large scale, it is important toexplore options in which the often con¯icting eco-nomic and environmental objectives can be recon-ciled. When deciding what to produce, a farmermay adapt his cropping plan by opting for cropsrequiring less external inputs, thus reducing emis-sions. At current product prices, however, thischange in cropping plan may result in income losses.A farmer can also make decisions on how to pro-duce. For this a farmer has the following options:(1) a decrease of inputs; or (2) exchanging currentinputs by more environmentally friendly inputs.These options may also result in lower economicperformances due to yield reductions and higherinput costs, respectively. If, however, the e�ciencyof external inputs such as fertilizers and pesticidesis improved, it may be possible to achieve economicand environmental objectives simultaneously.Against this background, this paper reviews

agronomic, economic and socio-psychologicalconcepts and integrates them into a conceptualinterdisciplinary framework. This model can beused for analysing input±output combinations ofactual and theoretical production technologies aswell as their agronomic, economic and ecologicale�ciency. The analysis identi®es new, alternativeinput±output combinations which are e�cientaccording to a set of well-de®ned objectives andcan be realized in practice. These input±output

combinations are needed in the design process ofnew production systems that meet economic andenvironmental objectives. In this model therestricting e�ects of agronomic and economic fac-tors on production levels should be consideredas well as behaviouristic aspects to account forpreferences and perceptions of the farmer.First, the concept e�ciency will be elaborated.

Next, insights from agronomy, normative farmeconomics and behavioural economics are dis-cussed that are used in the analysis and quanti®ca-tion of input±output combinations. Finally, theseinsights will be integrated into a conceptual model.

2. E�ciency

In economics as well as in production ecology,an `increase in e�ciency' often means that a cer-tain objective can be reached with less or cheaperinputs. The literature on economics distinguishesbetween productivity and e�ciency. Both conceptsare based on the production function. The pro-duction function in economics can be de®ned asthe maximum production attainable with di�eringcombinations of levels of production factors. Inother words, the production function is the set ofall e�cient production techniques (Heertje, 1971).Productivity is the amount of output producedwith a certain amount of input. E�ciency is arelative criterion; it is expressed as the percentageof a desired or attainable productivity that isactually achieved.E�cient use of inputs can be considered from

di�erent points of view: agronomically, ecologi-cally and economically. Agronomic e�ciency canbest be described in terms of resource use e�-ciency, a term derived from De Wit (1992). Agro-nomic e�ciency can be measured by unit outputper unit input. Agronomically e�cient input±output combinations can be determined for eachphysical production level, using the concepts thatwill be presented in Section 3 and the target-oriented approach (Van Ittersum and Rabbinge,1997). Agronomic e�ciency di�ers among pro-duction levels, as discussed by De Wit (1992).The ecological e�ciency of crop production is

based on the resilience of the ecosystem, which

34 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44

is di�cult to measure because it depends on allkinds of agro-ecological processes. An objectivedetermination of threshold values of inputs oremissions that might or might not a�ect resilienceof the ecosystem is therefore di�cult, if notimpossible. Information is often lacking for agood assessment of environmental impact, andrisk perceptions and priorities di�er among stake-holders. It can be argued that it is relevant toconsider several dimensions of ecological e�ciencyin the development and evaluation of sustainableproduction systems: (1) the input (or if availableemissions) per unit area; and (2) input (or emis-sions) per unit product1 (WRR, 1995).Economic e�ciency is based on pro®t max-

imization. Maximum pro®t is realized when mar-ginal costs equal marginal returns and is measuredas pro®t in monetary units per farm. However, foranalysis of e�ciency of the use of environmentallyharmful inputs the relation between individualinputs and outputs should be investigated, whichmeans that an important part of the analysisshould be done at ®eld and crop level. For analysisat farm level the objective of maximum pro®t canbe converted into maximum pro®t for a givencrop. It is then assumed that the optimal amountof `farm-speci®c' or `®xed' inputs like labour andmachinery for the cropping of, for example,sugarbeet is not con¯icting with the optimal dis-tribution of `®xed' inputs for the total croppingplan of the farm.The sustainability of di�erent production sys-

tems will be evaluated in terms of their economicand environmental performance. To standardize,the economic and environmental performance willbe expressed in e�ciency percentages, using ascore from 0 (worst performance) to 100% (bestperformance). The best performance can beexpressed in two ways: (1) as the best performanceof the sample; and (2) as the theoretical maximum.Tyteca (1996) raises this latter question of the`ideal' frontier and suggests two approaches forassessing this frontier: (1) technological de®nitionbased on best existing techniques and experts'opinions on what will become available in the next

few years; and (2) an agronomic approach basedon fundamental principles governing the processesconsidered. In our paper we elaborate on thesetwo approaches. We not only include e�cientlyused current techniques but also more advancedtechniques and future ones which are not yetfound in agricultural practice. We feel thatincluding only best practice technology would beinsu�cient given that we intend to support for-ward-looking analyses, such as exploratory studiesof future2 farming systems and instrumental stu-dies of future environmental policies. Underchanging policy and market conditions, technicalchange can be signi®cant and agricultural econo-mists must take this into account if they are toprovide meaningful conditional forecasts.An example of an evaluation of production sys-

tems based on the ®rst approach of assessing the`ideal' frontier is presented in Table 1. In theexample, three cropping variants for sugarbeetdi�ering in method of weed control are dis-tinguished. Variant 1 is characterized by a currentmethod for weed control using a mixture of over-all spraying and band spraying. In variant 2 onlyband spraying is used, while in variant 3 the num-ber of band spray applications is reduced as well.Cropping variant 3 shows the highest gross mar-gin, so it best serves the economic objective andthus has the best economic performance. There-fore, cropping variant 3 is 100% economicallye�cient. The economic performance of the othercropping variants was then related to variant 3.The environmental e�ciency expressed per ha, ofthe three variants can be calculated in the sameway; in this case environmental damage related tonitrogen and pesticides can be distinguished. Thecropping variant with the lowest nitrogen surplus(kg N per ha) is 100% environmentally e�cientfor nitrogen (all variants equally e�cient) and thecropping variant with the lowest pesticide input(kg a.i. per ha; variant 3 is most e�cient) is 100%e�cient for pesticides.The pursuit of a more sustainable cropping sys-

tem is complicated by the often con¯icting eco-nomic and environmental objectives. However, by

1 This measure corresponds with the measure of agronomic

e�ciency.

2 Future meaning a time period further away than just the

next year(s).

T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44 35

increasing the agronomic e�ciency the environ-mental and economic objective can often bereconciled, at least to a large extent. To calculatethe agronomic e�ciency, the productivity wascalculated for each cropping variant. The variantwith the highest productivity is 100% agronomi-cally e�cient. In the example (Table 1) this is cal-culated for nitrogen and herbicides separately. Theexample shows that an increase in the agronomice�ciency (in this case only for herbicides) resultsin a cropping variant with better environmentaland economic performance. Both the economice�ciency (achieved by saving costs) and the envi-ronmental e�ciency (achieved by using lessenvironmentally harmful inputs) increase in thisexample. So, if the agronomic e�ciency of exter-nal inputs such as fertilizers and pesticides isimproved, it may be possible to achieve the oftencon¯icting economic and environmental objectivessimultaneously. This would improve the prospectsfor introducing sustainable cropping systems inpractice signi®cantly.If the performances of the mentioned variants

are related to the theoretical maximum perfor-mance, by de®nition 100%, as assessed by

eco-physiological and agro-ecological models, thecalculated e�ciencies of the variants in the exam-ple will be lower than in the given example. Agro-nomic concepts, as presented in the next section,enable the identi®cation of maximal agronomice�ciencies.

3. Insights from agronomy

Agronomic knowledge and insights based on acombination of experiments and system analysisshould be used to identify alternative input±output combinations (such as those presented inTable 1), which may contribute to various objec-tives related to sustainable agriculture (Van Itter-sum and Rabbinge, 1997). In agronomy, threeproduction levels are distinguished according tothree groups of growth factors, i.e. the growthde®ning, growth limiting and growth reducingfactors (Rabbinge, 1993; Van Ittersum andRabbinge, 1997). The growth de®ning factors Dare those factors that, at optimum supply of allinputs, determine potential growth and potentialproduction level; these factors consist of the plant

Table 1

The relation between cropping practices and e�ciency criteria a

Sugarbeet Variant 1 Variant 2 Variant 3

Yield (mg/ha) 75.0 75.0 75.0

N (kg/ha) 150 150 150

Herbicides (kg a.i./ha) 2.7 1.6 1.0

Gross margin (D¯/ha) 6784 6958 7061

Economic e�ciency (%) 96 99 100

Environmental damage

N surplus (kg N/ha) 73 73 73

Herbicides (kg a.i./ha) 2.7 1.6 1.0

Environmental e�ciency

Nitrogen (%) 100 100 100

Herbicides (%) 37 63 100

Agronomic productivity (mg output/kg input)

Nitrogen 0.5 0.5 0.5

Herbicides 27.8 46.9 75.0

Agronomic e�ciency

Nitrogen (%) 100 100 100

Herbicides (%) 37 63 100

a Sources: sugarbeet variant 1: PAGV (1994); variant 2 and 3: PAGV (1994), adaptation based on Verschueren (1991).

36 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44

characteristics and the climatic factors such asincoming solar radiation and temperature. Thegrowth limiting factors L comprise the abioticresources water and nutrients. The growth reduc-ing factors R are weeds, pests, diseases and pol-lutants. The potential yield level is determined bygrowth de®ning factors, i.e. incoming solar radia-tion, temperature and the characteristics of thecrop; the crop is optimally supplied with waterand nutrients (none of the growth limiting factorsare actually limiting) and is completely protectedagainst the yield reducing e�ects of growth reduc-ing factors. The attainable or limited yield level islower than the potential yield level, due to a sub-optimal supply of growth limiting factors, e.g.shortage of water and nutrients. The actual pro-duction level is determined by a lack of water andnutrients and by incomplete crop protectionagainst growth reducing factors. Thus, only thepotential yield level is not a�ected by crop man-agement.Input±output combinations are site-speci®c.

The site can be characterized by the physical envi-ronment, i.e. the conditions under which the crop isgrown. The physical environment comprises cli-mate factors, soil characteristics which a�ect theuptake of inputs and other abiotic factors in soiland environment. The physical environment is dif-®cult to manipulate in the short run, and a�ects thepotential production level and the inputs requiredto realize a certain production level. On the otherhand, the crop usually hardly a�ects the physicalenvironment. Di�erences in physical environmentmay a�ect both the potential production level(through climate) and the attainable or actualproduction levels (through climate and soil).In agronomy a distinction is made between

non-substitutable and substitutable inputs. Non-substitutable inputs such as water and nutrientsare often taken up by the plant, or incorporatedinto its biomass, to ful®l speci®c, essential roles ingrowth and metabolism. Substitutable inputs arenot incorporated into the plant, depend on thelevel of the non-substitutable inputs and may bemutually exchangeable up to a certain extent, e.g.labour and mechanization or pesticides and mecha-nization or labour. Since some inputs are sub-stitutable, a certain production level in a particular

physical environment can be achieved with varioussets of production techniques. Because inputsmutually interact and are ®ne-tuned to each other,the technology set consists of ®xed input±outputcombinations (Van Ittersum and Rabbinge, 1997;Wossink and Rossing, 1999).Thus, agronomy provides useful concepts for

the analysis and design of agricultural productionsystems. These concepts allow yield gap analysesand identi®cation of alternative systems to bemade (Van Ittersum and Rabbinge, 1997). Inorder to serve this purpose the concepts abstractcrop management to crop growth factors only;human behaviour in terms of economic and socio-psychological aspects is purposely neglected. Thismeans that agronomic concepts have to be sup-plemented by economic and socio-psychologicalinsights in order to forecast farming practices infuture.

4. Insights from normative farm economics

In economics there is an ongoing debate aboutwhich speci®cations and functional forms to usein econometric studies focused on pesticideuse (Lichtenberg and Zilberman, 1986; Babcocket al., 1992; Carrasco-Tauber and Mo�t, 1992;Chambers and Lichtenberg, 1994; Fox and Weer-sink, 1995). There is a similar disagreement aboutmodelling fertilizer use and crop response (Ackello-Ogutu et al., 1985; Berck and Helfand, 1990;Paris, 1992, 1993). Economists interested in cropresponse focus on variation in just one or twoinputs, assuming that other inputs remain con-stant. Input±output relations are very complexbecause of interactions between the di�erentinputs (De Wit, 1992; De Koning et al., 1995; VanIttersum and Rabbinge, 1997). It is not realistic tostudy the e�ects of a change in only one input,since the e�ciency of this input would be very lowif the levels of the other inputs would not beadapted to the level of the concerning input(Wossink and Rossing, 1998). As a consequence,this approach does not produce input±outputrelationships that facilitate active forward-lookinganalysis in which a comparison is made betweenalternative future practices.

T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44 37

Furthermore, the input±output relations con-sidered by economists are often based on histori-cal data sets. This means that the latest technicaldevelopments and bio-physical insights are notincorporated in such input±output relations (Cha-vas and Cox, 1995), since even in the short timehorizons which are usually covered by economicstudies, the induced technical change is often sig-ni®cant. As agricultural production is highlya�ected by soil, climate and properties of biologi-cal systems, new or alternative production tech-nologies for agricultural production should bebased on bio-physical insights rather than onextrapolation. In addition, the driving force fortechnology change has evolved from mainly pro-duction into a multiple-objective (economic andecological) one. This shift in the direction of tech-nical development is quite di�erent from the earlieraim of primarily increasing production. This meansthat input±output relations and trends based onhistorical data sets will show a distorted view ifthey are used for forward-looking analysis, such as:(1) prediction, for instance the e�ects of possiblepolicy options for reducing pesticide use; and (2)designing new agricultural production systems.So, in many economic models the feasibility of

input substitution is unrealistic or not sustainablein practice and these models cannot cope with newtechnologies. In contrast to this, the generalizedjoint production model (Sudit and Whitcomb,1976; Archibald, 1988) allows the incorporation ofinput±output relations based on agronomicknowledge in economic models. This is in linewith work done by Pandey (1989), Blackwell andPagoutalos (1992), Fox and Weersink (1995) andWossink and Rossing (1998). As well as incorpor-ating agronomically based input±output relations,the framework: (1) allows both good (intended)outputs and bad (unintended) outputs to be pro-duced; (2) allows the proportions of these outputsto be varied; and (3) provides technical solutionsfor rearranging the package of inputs to counterbad outputs. Omitting the time aspect, for a speci®cphysical environment the model can be written as:

Y � F1�L;R;D�; �1�

L � F2�x1;L0�; �2�

R � F3�x2;L;R0�; �3�

Z � F4�x1; x2;L0;D�: �4�

In this model F1 represents the relation betweenagricultural output Y, growth limiting (L) andgrowth reducing (R) factors, given growth de®ningfactors (D). Only the growth factors L and R arevariable; D is given for each physical environment.F2 is the relation between the vector of controlactions x1 (e.g. fertilizing) and the level of L. Theuncontrolled (before treating) level of the yieldlimiting factors represented by the vector L0 (e.g.soil characteristics) is given for each physicalenvironment and is therefore not a variable. F3

stands for the relation between R and the vectorx2, given the uncontrolled (before treating) levelof the growth reducing factors R0 (intensity ofuntreated pests and diseases) and L. The vector x2comprises both conventional damage controlactions and the integrated control options derivedfrom integrated pest management, e.g. band-spraying and mechanical weed control. Theuncontrolled level of R0 depends not only on thephysical environment but also on x1. This meansthat R0, which is not a variable in this function,di�ers for di�ering levels of L. Finally, F4 expressesthe externalities (bad outputs) Z such as emissionsof nutrients and pesticides caused by the use of x1and x2 given L0 and D. Note that x1 and x2 con-tribute to both output Y and externalities Z, thesame quantities appearing in Eqs. (2)±(4). Assum-ing that conventional control options can besubstituted by integrated control options, theexternality output in the system can be reduced byconverting conventional control methods intointegrated control methods. In this framework,externality levels are not proportional to agri-cultural output levels alone but are also a functionof the production technology applied and of thephysical environment.To apply the generalized joint production frame-

work presented above, details of F1±F4 mustbe known, i.e. the relation between crop yield andthe growth factors L and R, crop response relationsF2, population dynamics-based control relations F3

and the relations between cropping system and

38 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44

environmental burden F4. This implies usingmodels/speci®cations incorporating the knowledgeof agronomists, plant pathologists, epidemiolo-gists and pollution experts.A farm economic analysis has to consider

uncertainty, because variability in agro-ecosystemsis very important in practice. The sources of vari-ation a farmer has to deal with in practice arefound in the biotic and abiotic environment and ingenetic resources (Almekinders et al., 1995). Thismeans in fact that the input use should be ®ne-tuned to variation in D, L0 and R0 in space and intime.To use the Eqs. (1)±(4) in normative economic

analyses, they have to be extended to a pro®tfunction. To do this, inputs and outputs are linkedto prices and constraints imposed by the ®xed,allocatable resources B (land, labour, machinery)of the farm:

� � pYF�L0;R0; x1; x2;D;B� ÿ px1x1 ÿ px2x2

ÿ pZZ; �5�

where p is pro®t, pY is the price of the output, px1and px2 are input prices and pZ is a tax on pollu-tion (if applicable). Conditions for pro®t maxi-mization of input use require that the variableinputs are used until their marginal costs equal thevalue of the marginal production:

py��Y=�x1� � px1 � pZ��Z=�x1�; and �6a�

py��Y=�x2� � px2 � pZ��Z=�x2�: �6b�

Eqs. (1)±(6b) are the starting point in the nor-mative analysis of farmers' behaviour. In this typeof analysis it is assumed that farmers have perfectinformation on input/output relations (as expres-sed in the production and pollution functions) andon prices and that their sole aim is pro®t maxi-mization. Furthermore, assumptions have to bemade about the farm-speci®c restrictions a farmerhas to deal with. These include resource endow-ment and regulations. This means that since inpractice farmers have no perfect knowledge, thee�ects of imperfect knowledge on economicbehaviour should be analysed. Furthermore, the

assumption of pro®t maximization as only objec-tive should be veri®ed in relation to the speci®ccharacteristics of the farm.

5. Insights from behavioural economics

Normative farm economics studies the optimalfarm organization by regarding the farm as adynamic system of a technology set, surroundedby constraints and objectives. To explain di�er-ences between observed and optimal farmingpractices, non-optimal economic behaviour has tobe taken into consideration. In contrast to nor-mative farm economics, behavioural farm eco-nomics acknowledges the large variation infarmers' capacity and willingness to reach thepotential level. This behavioural aspect, therefore,determines e�ciency of agricultural productionboth in the short run (deviation from the optimalorganization) and in the long run (by di�erencesin adoption of technical innovation and e�ects onthe natural resource base). Farm economists haveonly recently focused on multiple objectives(beyond pro®t maximization and risk perception)(Sumpsi et al., 1993; Amador et al., 1996;McGregor et al., 1996; Nellinger and Pavlovic,1996) and on limited information/knowledge(Jahae et al., 1996). Behavioural economics focus-es on various objectives in decision making and onlimited information. This is closely related toinsights from social psychology. Katona (1975)was one of the ®rst authors to emphasize the rel-evance of psychological variables for economicbehaviour: motives, perceptions, attitudes andexpectations. In behavioural economics it is notthe objective characteristics of goods/actions thatmatter but the subjective characteristics as per-ceived by the decision maker. With regard to theanalysis of the subjective characteristics of goods/actions two research approaches can be dis-tinguished which are used more frequently for theanalysis of agricultural practices in relation toenvironmental goals. In this section ®rst perceptionanalysis will be discussed and next the concept ofmanagement styles.Perception analysis is a technique which is deriv-

ed from the research ®eld dealing with marketing

T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44 39

and consumer behaviour. In farm economics it isrecently applied on analysis of the adoption ofnew environmentally friendly production techni-ques (Wossink et al., 1996). In this type of analysisa production technique is considered as a collec-tion of attributes which the individual farmer mayappreciate or dislike. Which attributes are con-sidered during the selection depends on the farm-er's preferences and is therefore a person-speci®cmatter, as is the expected score (the relativeimportance of the attribute for the farmer) foreach attribute. This means that even with the sameset of technical options, and given similar naturalconditions D and farm situations B, di�erentfarmers will make di�erent choices:

EfXjg �XI

EifXjj�i�gP��i�; �7�

max Uj � U1�X1j� �U2�X2j� � . . .�Un

� �Xnj�; �8�

where Xj=alternative production technique j( j=1,. . .,J ); E{Xj}=expected result of alternativeproduction technique j; �i=circumstances i, e.g.particularly weather, policy or market conditions(i=1,. . ., I ); Xjj�i=result of alternative produc-tion technique j under state i; P(�i)=probabilityor degree of belief in the occurrence of state �i;Xnj=attribute level n in alternative productiontechnique j; and Uj=utility of alternative produc-tion technique j.In Eq. (7), E{Xj} represents the expectation of

the farmer concerning the result (measured ineconomic or physical terms) for each alternativecropping measure Xj. The farmer bases thisexpectation on the assumption of the probabilitythat particular circumstances �i (such as weather,policy or market conditions) will occur, i.e. P(�i).In Eq. (8), Uj presents the utility of alternative jwhich is based on the perception of the farmerconcerning subjective characteristics of alternativeproduction technique Xj perceived by the farmer.Because human beings are capable of learning,

their response to the same technology set (Fig. 1)may di�er in time. This is an important fact with

respect to farming systems that meet economicand environmental objectives. Farming systemswith multiple objectives require more than justanother selection from the technology set. Agri-cultural development which is no longer onlyaimed at economic but also at environmentalobjectives implies a gradual learning process thatbrings about changes in risk perception, objectivesand starting points (Somers and RoÈ ling, 1993;Somers, 1999). A shift towards such farming sys-tems augments the farmers' need for (new)knowledge and expertise.In this concept the Rational Choice theory

based on the `Homo economicus' maximizes pro®tis still the starting point; it is only adaptable to thereal world by incorporating: (1) the farmer's owngoals; and (2) by making farmer's anticipatedresults decisive, not the objective results. Percep-tion analysis ®ts very well in modern utility theoryin which the Subjective Expected Utility (SEU)hypothesis forms the basis. The SEU hypothesisshows how to integrate the two componentsof utility (preference) and probability (degree ofbelief) to provide a means of ranking risky pro-spects (new alternatives), so enabling risky choiceto be rationalized (Dillon, 1971; Anderson et al.,1977; Hardaker et al., 1997).The concept of management styles is based on

the Agency theory, which has a fundamentallydi�erent starting point. In this theory, decisionmaking is not a separate activity but is closelyrelated to the execution of the daily activities.

Fig. 1. Core model of choice making. Source: Adapted from

Wierenga and van Raaij (1987).

40 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44

Alternatives are not produced by external forcesbut developed by the farmer himself. The farmer'sstrategy to search for information is an importantaspect of the `management style'. According toVan der Ploeg (1994), an individual farmer'smanagement style is the result of: (1) his objec-tives; (2) his ideal for the farm; and (3) his behav-ioural pattern and his information or learningstrategy given the external conditions (Fig. 2). InVan der Ploeg's model, the management style isseen as a basic attitude of the farmer, which (Fig.2) is represented by the arrow pointing down-wards in the centre of the ®gure. The external con-ditions can be divided into technical±economicconditions and social conditions. Both can func-tion as constraints. The Agency theory warnsagainst isolating conditions from behaviour(management). Therefore, in the model there isfeedback from farm organization towards condi-tions (Van der Ploeg, 1994).Since in practice the choice of production tech-

niques not only depends on the objective pro®tmaximization, other behavioural aspects have tobe taken into account too. With perception analy-sis insight can be gained into which aspects of aproduction technique are important to a (group

of) farmer(s) and into their separate relevance.The concept of management styles is a useful toolfor elucidating the personal factors that in¯uencethe choice of production techniques in relationto the organization of the farm. Therefore, theuse of management styles can provide relevantknowledge for the development of new productionsystems.

6. Conceptual model

From the preceding sections it is clear that aconceptual model which combines agronomicpossibilities, normative economic restrictions andhuman behaviour is needed for the analysis ofinput±output combinations being essential pre-requisite in the diagnostic phase of the design ofnew farming systems that meet economic andenvironmental objectives. As stated earlier, the per-formance of crop production systems on environ-mental and economic objectives can be improvedby increasing the agronomic e�ciency. For theanalysis of the potential agronomic e�ciency levelthat satis®es economic and socio-psychologicalobjectives and constraints, a conceptual model

Fig. 2. Management style, external determinants and farm organization. Source: Adapted from Van der Ploeg (1994).

T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44 41

analogous to the well-known representation ofproduction levels and accompanying growthfactors in agronomy is developed (see Section 3).For a given physical environment the highest

production level that can be achieved based onagronomic insights is the `potential' productionlevel. This potential productivity level is de®nedby the production factors (D): CO2, radiation,temperature and crop characteristics under theassumption that the growth limiting and growthreducing factors do not occur (see Section 3). Inpractice, when growth limiting and growth reduc-ing factors occur and inputs have to be applied,considerations relating to farm economics play arole. So for the second production level, the agro-nomic growth factors are combined with theseconsiderations (see Section 4). The latter include:(1) pro®t maximization as the main objective, i.e.price ratio of outputs and inputs and marginalreturns are decisive instead of physical input±output ratios; (2) structural restrictions to thefarm (resource endowment and regulations);and (3) variability in the agro-ecocomplex. Theapproach in the assessment of the second produc-tion level is normative: farmers are considered as agroup of similar decision makers and are assumedto have perfect information. The result is the`normative' production level.

In reality, however, farmers do not have perfectinformation and they are also not purely pro®tmaximizers (see Section 5). Therefore, a third levelfocuses on objectives of farmers and di�erencesamong farmers. Starting from insights of behav-ioural economics, the technology set and di�er-ences among farmers as observed in practice areanalysed. This step results in two productionlevels. The highest level is the `best practice' pro-duction level based on the best performancesobserved. Furthermore, an `average' productionlevel can be obtained, based on the averageperformance in practice.The conceptual model with the di�erent pro-

duction levels is represented in Fig. 3. As indi-cated, the representation of the conceptual modelis analogous to the well-known representation ofproduction levels and accompanying growth fac-tors in agronomy, so both divisions are repre-sented in order to show how they are related.Three groups of production factors and threeproduction levels are distinguished in agronomy(see Section 3). As Fig. 3 shows, the `potential'production level and the `actual' or `average' pro-duction level are identical in the agronomic andagro-economic approaches. The `potential' yieldlevel is identical in both frameworks because onlybio-physical factors determine this production

Fig. 3. Production levels in agronomy (Source: Rabbinge, 1993; Van Ittersum and Rabbinge, 1997) and in the combined agronomic

and economic approach.

42 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44

level. The `actual' and `average' production levelscan be assumed to be identical because they arebased on the performance in practice. The water ornutrient `limited' yield level distinguished in agr-onomy is not considered in the agro-economicapproach. In practice, farmers use a mix of inputsto collectively control limiting and reducing growthfactors, so the `limited' yield level is only of analy-tical interest. For the analysis of input±outputcombinations in the agro-economic framework,the notion of the di�erences between the limitingand growth reducing factors is important and willbe used. Going from the `potential' productionlevel downwards to the `average' production levelfor each production level in both the agronomicand the agro-economic division, the number ofgrowth restricting factors increases and thus theyield level falls.The agro-economic division into production

levels and accompanying factors will be used toidentify alternative input±output combinationswhich are e�cient according to either economic orenvironmental objectives. As an increase of theagronomical e�ciency may help to realise both,the conceptual model will be used to address thedi�erences in agronomic e�ciency of the input±output sets associated with the actual (best prac-tice and average) practices and the theoretical(potential and normative) practices. By analysingto what extent di�erences in agronomic e�ciencycan be explained by the production restrictingfactors associated to these practices, insight can begained in the potential improvement of the agro-nomic e�ciency accounting for economic andsocio-psychological constraints observed in prac-tice. The analysis can be done according to twoextreme situations which can be distinguished forthe improvement of agronomic e�ciency of actualinput±output combinations. In the ®rst situationthe realised production level remains the targetbut the levels of the inputs are reduced. In thesecond situation the target production level ishigher than the one realized but can be increasedwithout a required increase of the input level. Theagronomic e�ciency can be termed `input savinge�ciency' in the ®rst situation and `outputincreasing e�ciency' in the second situation(Uhlin, 1985). For each input±output combination

the input saving and the output increasing e�-ciency can be calculated. Of course combinationsof input saving and output increasing e�ciencystrategies are possible. It depends on the ratio ofweights of economic and ecological objectiveswhich option will be chosen. The input±outputcombinations associated with the potential agro-nomic e�ciency level within the boundaries ofeconomic and socio-psychological objectives andconstraints, can be used to design sustainablefarming systems; i.e. farming systems that arepractically feasible, socially acceptable and envi-ronmentally and economically advantageous.

References

Ackello-Ogutu, Ch., Paris, Q., Williams, W.A., 1985. Testing

the von Liebig crop response function against polynomial

speci®cations. American Journal of Agricultural Economics

67, 873±880.

Almekinders, C.J.M., Fresco, L.O., Struik, P.C., 1995. The

need to study and manage variation in agro-ecosystems.

Netherlands Journal of Agricultural Science 43, 127±142.

Amador, F., Sumpsi, J.M., Romero, C., 1996. A Non-inter-

active Methodology to Assess the Farmers' Utility Function:

an Application to Large Farms in Andalusia, Spain. Pro-

ceedings of the VIIIth European Association of Agricultural

Economists Congress, Edinburgh.

Anderson, J.R., Dillon, J.L., Hardaker, B., 1977. Agricultural

Decision Analysis. Iowa State University Press, Ames, IA.

Archibald, S.O., 1988. Incorporating externalities into agri-

cultural productivity analysis. In: Capalbo, S.M., Antle, J.M.

(Eds.), Agricultural Productivity Analysis: Measurement and

Explanation. Resources for the Future, Washington.

Babcock, B.A., Lichtenberg, E., Zilberman, D., 1992. Impact

of damage control and quality of output: estimating pest

control e�ectiveness. American Journal of Agricultural Eco-

nomics 74, 163±172.

Berck, P., Helfand, G., 1990. Reconciling the von Liebig and

di�erentiable crop production functions. American Journal

of Agricultural Economics 72, 985±996.

Blackwell, M., Pagoutalos, A., 1992. The econometrics of

damage control: comment. American Journal of Agricultural

Economics 74, 1040±1044.

Carrasco-Tauber, C., Mo�t, L.J., 1992. Damage control

econometrics: functional speci®cation and pesticide pro-

ductivity. American Journal of Agricultural Economics 74,

159±162.

Chambers, R.G., Lichtenberg, E., 1994. Simple econometrics of

pesticide use. American Journal of Agricultural Economics

76, 406±417.

Chavas, J.P., Cox, Th.L., 1995. On nonparametric supply

response analysis. American Journal of Agricultural Eco-

nomics 77, 80±92.

T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44 43

De Koning, G.H.J., van Keulen, H., Rabbinge, R., Janssen, H.,

1995. Determination of input and output coe�cients of

cropping systems in the European Community. Agricultural

Systems 48 (4), 485±502.

De Wit, C.T., 1992. Resource use e�ciency in agriculture.

Agricultural Systems 40, 125±151.

Dillon, J.L., 1971. An expository review of Bernoullian deci-

sion theory in agriculture: is utility futility? Review of Mar-

keting and Agricultural Economics 39, 3±77.

Fox, G., Weersink, A., 1995. Damage control and increasing

returns. American Journal of Agricultural Economics 77,

33±39.

Hardaker, J.B., Huirne, R.B.M., Anderson, J.R., 1997. Coping

with Risk in Agriculture. CAB International, London.

Heertje, A., 1971. Economie en technische ontwikkeling. Sten-

fert Kroese, Leiden.

Jahae, J.A.M.A., Reinhard, A.J., Rutten, J.M., Somers, B.M.,

Zwambag, E.A.J., 1996. Kennis op bedrijfsniveau, Onder-

zoeksverslag 149. Landbouw Economisch Instituut, Den

Haag.

Katona, G., 1975. Psychological Economics. Elsevier, New

York.

Lichtenberg, E., Zilberman, D., 1986. The econometrics of

damage control, why speci®cation matters. American Jour-

nal of Agricultural Economics 68, 261±273.

McGregor, M., Willock, J., Dent, B., Deary, I., Sutherland, A.,

Gibson, G., Morgan, O., Grieve, B., 1996. Links between

psychological factors and farmer decision making. Farm

Management 9 (5), 228±239.

Nellinger, L., Pavlovic, M., 1996. Family Farms and their

Socio-economic Equilibrium. Proceedings of the VIIIth

European Association of Agricultural Economists Congress,

Edinburgh.

PAGV, 1994. Kwantitatieve Informatie voor de Akkerbouw en

de Groenteteelt in de Vollegrond 1995, Lelystad.

Pandey, S., 1989. The economics of damage control: comment.

American Journal of Agricultural Economics 70, 443±444.

Paris, Q., 1992. The von Liebig Hypothesis. American Journal

of Agricultural Economics 74, 1019±1028.

Paris, Q., 1993. Von Liebig's law of the minimum and low-

input technologies. In: Struik, P.C., et al. (Eds.), Plant Pro-

duction on the Threshold of a New Century. Kluwer Aca-

demic Publishers, Dordrecht.

Rabbinge, R., 1993. The ecological background of food pro-

duction. In: Chadwick, D.J., Marsh, J. (Eds.), Crop Protec-

tion and Sustainable Agriculture, Ciba Foundation

Symposium 177. John Wiley, Chichester.

Rossing, W.A.H., Meynard, J.M., van Ittersum, M.K.,

1997. Model-based explorations to support development

of sustainable farming systems: case studies from France

and the Netherlands. European Journal of Agronomy 7

(in press).

Somers, B.M., 1999. Learning for sustainable agriculture. In:

Van Ittersum, M.K., van de Gein, S.C. (Eds.), Perspectives

for AgronomyÐAdopting Ecological Principles and Mana-

ging Resource Use. Elsevier Science, Amsterdam, pp. 353±

359 (in press).

Somers, B.M., RoÈ ling, N.G., 1993. Kennisontwikkeling voor

duurzame landbouw, rapport nr. 93/12. NRLO, Den Haag.

Sudit, E.F., Whitcomb, D.K., 1976. Externality production

functions. In: Lin, S.A.Y. (Ed.), Theory of Economic

Externalities. Academic Press, New York.

Sumpsi, J.M., Amador, F., Romero, C., 1993. A research on

Andalusian Farmers' Objective: Methodological Aspects and

Policy Implication. VIIth European Association of Agri-

cultural Economists Congress, Stresa.

Tyteca, D., 1996. On the measurement of the environmental

performance of ®rmsÐa literature review and a productive

e�ciency perspective. Journal of Environmental Manage-

ment 46, 281±308.

Uhlin, H.E., 1985. Concept and Measurement of Technical and

Structural Change in Swedish Agriculture. Sveriges Land-

bruksuniversitet, Uppsala.

Van der Ploeg, B., 1994. Ondernemersstijlen en het zoeken naar

nieuwe mogelijkheden; Een verkenning onder landbouwers

in de VeenkolonieÈ n, Onderzoeksverslag 124, LEI-DLO.

Van Ittersum, M.K., Rabbinge, R., 1997. Concepts in produc-

tion ecology for analysis and quanti®cation of agricultural

input±output combinations. Field Crops Research 52, 197±

208.

Verschueren, L.A.L.R., 1991. De e�ecten van een he�ng op

bestrijdingsmiddelen voor de akkerbouw in de NOP. MSc

thesis, Wageningen Agricultural University, Netherlands.

Wierenga, B., van Raaij, W.F., 1987. Consumentengedrag;

theorie, analyse en toepassingen. H.E. Stenfert Kroese B.V,

Leiden.

Wossink, G.A.A., Rossing, W.A.H., 1998. On increasing

returns and discrete choice: integrating production ecological

principles in economic analysis of crop management. Journal

of Environmental Management 54, 233±247.

Wossink, G.A.A., de Buck, A.J., van Niejenhuis, J.H., Haver-

kamp, H.C.M., 1996. Farmer perceptions of weed control

techniques in sugarbeet. Agricultural Systems 55 (3), 409±

423.

WRR, 1995. Sustained Risks, a Lasting Phenomenon (Report

to the Government No. 44). Netherlands Scienti®c Council

for Government Policy, Sdu Uitgeverij, The Hague, Nether-

lands.

44 T.J. de Koeijer et al. / Agricultural Systems 61 (1999) 33±44