multi criteria assessment of zero residue apple production; bewertungskriterien für einen...

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Original article

Abstract During the 2008–2010 growing seasons, an al-ternative ‘zero residue apple production system’ was com-pared with integrated apple production with cvs ‘idared’, ‘golden Delicious’, ‘Jonagold’ and ‘gala’ in commercial orchards at several locations throughout Slovenia, using data envelopment analysis (Dea), and multi criteria as-sessment by an analytical hierarchical process (aHP). the principle of the ‘zero residue apple production’ is a combination of integrated (iP) and organic apple produc-tion. During the first 3 months of the growing season (1 April–30 June), pesticides used in IP with rapid degrada-tion (8–10 applications) were used to control pests and diseases. During the second part of the season from 1 July to harvest, organic products (6–12 applications) were em-ployed compared with 19–25 applications overall in iP. the goal of the alternative system was to reduce the amount of applied conventional pesticides by 40 % and to minimize pesticide residues in fruits to below the limit of 0.5 % of the legal maximum residue level (Mrl) or below the resi-due concentrations of 0.005–0.01 mg/kg and to retain the high long-term level of yield, fruit quality, and net income per hectare. the number of pesticide residues was reduced from 4.2–5.5 in IP to 1.8–3.4 in zero residue cultivation, while 3 year average yields (class 1 fruit) were 4–9 % low-er than in IP. The break even prices ranged from € 0.31 for Idared in IP, € 0.34 for ‘Elstar’ of both production systems to € 0.35/kg for zero residue cultivated ‘Golden Delicious’. Overall, a price increase of just € 0.02/kg for residue free apples would make this new ‘zero residue apple produc-

tion’ profitable then representing a realistic alternative to the standard integrated apple production system.

Keywords apple · Production system · integrated production · Zero residue · economics · Dea analysis · aHP assessment · Sustainability

Bewertungskriterien für einen rückstandsfreien Apfelanbau

Zusammenfassung Über 3 Jahre (2008–2010) wurde der rückstandsfreie apfelanbau bei den Sorten ‘idared’, ‘gol-den Delicious’, ‘Jonagold’, ‘gala’ mit dem iP- anbau auf Obstbaubetrieben in Slovenien mit Hilfe von statistischer und Dea-analyse (data envelopment analysis) und multi-kriteriellen analytischen Hierarchieprozessen(aHP) unter-sucht. Das Prinzip des rückstandsfreien apfelanbaus ist die Kombination des intergrierten und ökologischen anbaus. in den ersten 3 Monaten der Vegetationsperiode (1. April–30. Juni) wurden Pflanzenschutzmittel mit schneller Abbau-rate aus intergriertem anbau mit 8–10 Maßnahmen und in der zweiten Vegetationshälfte vom 1. Juli bis zur Ernte Pflanzenschutzmittel (6–12 Maßnahmen) aus dem ökolo-gischen anbau eingesetzt. Das Ziel des rückstandsfreien Anbausystems ist es, erstens den konventionellen Pflanzen-schutzmitteleinsatz unter 40 % zu reduzieren und zweitens die Pflanzenschutzmittelrückstände unter den zulässigen höchsten rückstandswert (Mrl-Maximum residue le-vel) von 0,5 % bzw. unter die rückstandskonzentration von 0,005–0,01 mg/kg zu senken. Die Zahl der Pflanzenschutz-rückstände in den Äpfeln sanken von 4,2–5,5 im IP auf 1,8–3,4 im rückstandsfreien Apfelanbau. Die Vorausset-zung für die Wirtschaftlichkeit des neuen rückstandsfreien apfelanbaus ist ein dem iP-anbau vergleichbarer ertrag an

Erwerbs-Obstbau (2013) 55:51–62DOI 10.1007/s10341-013-0186-y

Multi Criteria Assessment of Zero Residue Apple Production

Črtomir Rozman · Tatjana Unuk · Karmen Pažek · Mario Lešnik · Jernej Prišenk · Andrej Vogrin · Stanislav Tojnko

Č. Rozman () · T. Unuk · K. Pažek · M. Lešnik · J. Prišenk · A. Vogrin · S. TojnkoFaculty of agriculture and life Sciences, University of Maribor, Pivola 10, 2311 Hoče, Sloveniae-mail: [email protected]

Received: 8 April 2013 / Accepted: 15 April 2013 / Published online: 9 May 2013© Springer-Verlag Berlin Heidelberg 2013

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52 Č. Rozman et al.

qualitativ hochwertigen Äpfeln (Fruchtqualität und Netto-einkommen). Die Kosten deckenden erzeugerpreise reich-ten von € 0,31 für ‘Idared’ im IP, über € 0,34 für ‘Elstar’ beider Anbausysteme bis zu € 0,35/kg für rückstandsfreie ‘Golden Delicious’. Die Rentabilität des rückstandsfreien Anbaus war, aufgrund besserer Vermarktunschancen mit weniger Rückständen, trotz 4–9 % geringerer Erträge (HKl 1) als im IP mit dem Integrierten vergleichbar. Sie wäre bereits ab einem um € 0,02/kg höheren Preis für die rück-standsfreie Ware profitabel und könnte dann in Zukunft eine wirtschaftlich realistische alternative zum integrierten anbau werden.

Schlüsselwörter Apfel · AHP-Beurteilung · anbausystem · Dea-analyse · integrierter anbau · rückstandsfreier apfelanbau · nachhaltigkeit · Wirtschaftlichkeit

Introduction

growing consumer demands with respect to environment conservation and food safety issues are resulting in a con-tinuous adaptation of agricultural production systems. in europe, a huge market demand now exists for apples free of (< 0.005–0.01 mg/kg) pesticide residues (Berrie and Cross 2012). Farming systems that can produce these types of fruits are under development in many countries around the world and are considered as the intermediate step in trans-formation of standard integrated systems to more advanced production systems of the future (anonymus 2012). the result is presented as zero residue produce in the market. this type of production system is an important alternative to integrated systems for growers who are unable to convert to organic for various reasons.

the underlying idea of the zero residue system is the sep-aration of the apple growing season into two parts (cross and Berrie 2008). Disease and pests are largely controlled during the first part of the season using selected pesticides that are designated as environmentally friendly and that have very fast degradation rates. Harmful organisms are controlled during the second part of the season using plant protection products declared as suitable for organic produc-tion. Using this two-phase approach, we can achieve high rates of pest control and still significantly reduce pesticide residues in crop.

First approaches for the zero residue production were tested in 1990–1995 (Jones et al. 1993). intensive research has continued since 2000 and the first real development of a zero residue production system for apples was presented by english researchers at east Malling research Station who tested the system in commercial orchards (Berrie and Cross 2005; Cross and Berrie 2001). During the last decade, many

researchers started to conduct trials similar to those of Ber-rie and cross at east Malling and started to evaluate the zero residue approach e.g. in austria, germany and Switzerland.

Organic apple production systems are strongly supported all over europe. at present, many problems still prevent the producers of organic apples from covering the entire market demand for apples; in germany every second organic apple has to be imported. consumers expect that apples originating from organic orchards will not have been treated frequently with any kind of plant protection products. Unfortunately, this is not true. in many regions, organic fruit producers must spray apple plantations more frequently, and use more kilograms of plant protection product (e.g., copper, sulfur, etc.) per hectare per year e.g. due to their lower efficacy than do producers who use integrated production systems. in addition, the organic systems often achieve a lower mar-ketable yield (Delate and McKern 2008; Weibel et al. 2007).

This conflict between the potential for improved environ-mental sustainability of organic systems and their insuffi-ciencies in maintaining economic sustainability has limited the expansion of organic fruit production in certain regions. One possible answer is the development of a hybrid system that would incorporate the best practices and solutions of both systems. the zero residue system, on the one hand, successfully addresses the concern for the environment and about pesticide residues in fruits, while on the other hand, it quells the doubts about economic feasibility. However, before implementation, the new zero residue system must be tested and assessed from technological, economic, and environmental points of view.

according to Misra et al. (1991), most consumers favor testing and certification of fruits, but they oppose large price markups for certified pesticide-free fresh produce. Roosen et al. (1998) showed that consumer perceptions of product attributes change, if pesticides are removed from the apple production system and this is reflected in a willingness to accept higher prices for apples, which is also shown to be income elastic. alternative production systems must be eco-nomically feasible to be adopted by growers and they must also be accepted by consumers and society.

Different approaches are available to compare standard and alternative production. the technical and economic efficiency that considers all inputs and outputs used in production is usually assessed with a Data envelopment analysis (‘Dea’) as developed by charnes et al. (1978). Many studies have measured efficiency in agricultural systems using Dea (andre 2009; lilienfeld and asmild 2007; Mao and Koo 1997; nassiri and Singh 2009; Picazo-tadeo et al. 2011; Reig-Martınez and Picazo-tadeo 2004; Sueyoshi 1999). For a comparison of systems in terms of both consumer and society acceptance, we need to apply modern tools for production system comparisons that incor-porate pure economic criteria and also criteria that are not

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53Multi Criteria Assessment of Zero Residue Apple Production

directly related to the basic efficiency analysis. For these cases, multi criteria decision analysis can be applied (tiwari et al. 1999), because the many different aspects can behave in conflicting ways; for instance, economically and techni-cally efficient systems can be damaging to the environment and vice versa. Multi criteria analysis (Mca) has also been applied for assessment of many cropping systems (Bohanec et al. 2007; Bohanec et al. 2008; carpani et al. 2012; Dantsis et al. 2010; girardin et al. 2000; Mazzetto and Bonera 2003; Pavlovič et al. 2011; Pelzer et al. 2012; Žnidaršič et al. 2008). the most common method of Mca is the analytical hierarchical process (aHP) developed by Saaty (1980). the aHP has been used to solve numerous agricultural decision and assessment problems (Jiang and Wan 2009; Montazar and Benbahani 2007; tiwari et al. 1999; Wan et al. 2009; Zhang et al. 2004).

the aim of our research was to compare an integrated apple production system with the zero residue apple pro-duction system. We did this by using data from field experi-ments and processing these data using data envelopment analysis for technical efficiency assessment and the analyti-cal hierarchical process for the multi criteria assessment.

Materials and Methods

Field experiments- Plant Material

commercial apple plantations of cvs ‘idared’, ‘golden Deli-cious’, ‘Jonagold’ and ‘gala’ on M9 rootstock with slender spindle training were used for 12 trials to compare standard integrated (iP) and alternative zero residue apple produc-tion. The experimental design was a field trial with plots in four repetitions. Standard ANOVA analysis was performed and the t-test (P < 0.05) was used for comparisons of means (yield, yield loss, disease attack rate, etc.). Plots of ca. 0.10–0.15 ha each were managed according to the specific system to be compared and were randomly arranged inside larger plantation areas. Plots with the integrated production system were treated with 30 different preparations based on active substances of standard pesticides, for a total of 19–25 applications per season during the period from 1st april to 15th September. Plots with the zero residue production were treated 8–10 times with the same standard pesticides used in the integrated system during the period from 1st april to 1st July; afterwards (during the period from 1st July to 15th September), these plots were treated 6–12 times with prod-ucts registered for organic pest control, based on carbon-ates, mineral clays, plant oils and extracts, bacteria, viruses, yeasts, insect pheromones and similar products.

One row in the middle of each plot was chosen for yield assessments, for picking fruits for storage experiments, and for taking fruit samples for analysis of pesticide residues.

Pesticide residues were determined in an officially certified laboratory (institut Wagner, lebring, austria) using several validated methods (i.e., gc-MSD, gc-µecD, lc-MS/MS, etc.). These methods allowed quantification of residues of 200 different pesticide active substances at ppb level of 0.005–0.01 mg/kg (± 40 %).

Yield was determined on 5 trees from each plot. Picked fruits were placed into plastic storage boxes and stored in professional commercial storage facilities (UlO regime, 1 °c; r.H. 97 %, 1 % O2; 1.2 % cO2; for 4–6 months). losses due to fungal and other diseases were determined after storage. all costs of production were noted, analyzed, and compared between both production systems by per-forming a standard cost–benefit analysis and technical effi-ciency analysis.

Methodology: Enterprise Budgets

the enterprise budgets were used for estimation of costs and return of the analyzed apple cropping systems. enter-prise budgets represent estimates of receipts (income), costs, and profits associated with the production of agricul-tural products. The input data were acquired form 12 field experiments. Based on the enterprise budgets, the following indicators were calculated: (1)

(2)

Where:FR net return (€/ha)TR total revenue (€/ha)TC total costs (€/ha)Y1 first class yield (kg/ha)Y2 second class yield (kg/ha)Y3 third class yield (processing fruit) (kg/ha)yi price of individual yield class

Efficiency analysis of apple production systems with data envelopment analysis

The technical efficiency value obtained from DEA indi-cates the efficiency of an assessed unit (in this case, an indi-vidual apple cropping system) at transforming inputs into outputs (in our case, the inputs and outputs used in apple production (table 1). Technical efficiency in DEA is defined as the ratio between virtual output and virtual input, where virtual outputs (inputs) are dot products of individual out-puts (inputs) and their weights. the Dea procedure yields a measure of efficiency for each system as well as optimal weights for inputs and outputs. The measure of efficiency is a value between 0 and 1, where the “best” (most efficient) units reach a value of 1 while the “worst” (least efficient) units reach the lowest value, but larger than zero (Đatkov

FR = T R − T C

T R = Y1py1 + Y2py2 + Y3py3

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and effenberger 2010). Dea methodology, originally pro-posed by charnes et al. (1978), is used to assess the relative efficiency of a number of entities, using a common set of incommensurate inputs to generate a common set of incom-mensurate outputs. The problem of assessing efficiency is formulated as a task of fractional programming, but the application procedure for Dea consists of solving linear programming (lP) tasks for each of the units under evalua-tion (Martic et al. 2009).

(3)

Subject to:

Legend:vi the weights to be determined for input i;m the number of inputs;ur the weights to be determined for output r;s the number of outputs;Ek the relative efficiency of DMUk (in our case apple

production system)n the number of entities;ε a small positive value

the problem described in equations 1–4 is nonlinear and non-convex, with a linear and fractional objective function and linear and fractional constraints. charnes and cooper (1962) transformed this ccr model into lP form (the pri-mal ccr model) to allow linear programming methods to be used for solving. the denominator has been set equal to 1 and the numerator is maximized. the transformed model is then as follows (the input oriented CCR primal model):

max Ek =s∑

r=1ur yrk

m∑i=1

vi xik

s∑r=1

ur yi

m∑i=1

vi xj

≤ 1, j = 1, 2, . . . ,jk , . . . , n

ur = ∈, r = 1, 2, . . . , svi = ∈, i = 1, 2, . . . , m

(4)

Subject to:

the assessment with analytical Hierarchical Process

the analytical Hierarchical Process (aHP) was best illus-trated by Saaty (1980). the aHP is a decision support tool that can be used for solving complex decision problems. it uses a multi-level hierarchical structure of objectives, sub-objectives, and alternatives (triantaphyllou and Mann 1994). The variants are decomposed into specific param-eters (criterion, attribute) and evaluated separately for each single parameter. Pros and cons, as well as other influencing factors, can also be included. The final variant evaluation is provided with combine procedure. ratio comparisons are performed on a fixed ratio scale. The goal is defined as a statement of the overall objectives. For the precise accountant who only wishes to deal with finite numbers, aHP allows decision-makers to derive ratio scale priorities as opposed to randomly assigning them. the aHP enables the decision makers to incorporate both the subjective and objective matters into the decision making process. this is done by describing complexity as a hierarchy and ration through comparison of alternatives relative to the objec-tive (called pair-wise comparison). However, at each level of the hierarchy, the relative importance of each compo-nent attribute is assessed by comparing them in pairs. the rankings obtained by pair-wise comparisons between the alternatives are converted to normalized rankings using the eigenvalue method. The pair-wise comparison reflects the estimates made by the decision maker regarding the relative importance of each alternative in terms of a given decision criterion.

a typical problem experienced with aHP consists of a set of alternatives and a set of decision objectives. in appli-cations of the aHP to real decision-making problems, the entries in the above reciprocal matrix are taken from the finite set: {1/9, 1/8,…1, 2,…8, 9} as suggested by Saaty (1980). in practice, the above discrete set is usually used. Saaty (1980) and Saaty and Kearns (1991) developed the following steps for applying the AHP:

max Ek =s∑

r=1uryrk

m∑i=1

vixik = 1

s∑r=1

uryry −m∑

i=1vixik ≤ 0, (j = 1, . . . , n)

ur ≥ ∈, r = 1, 2, . . . , svi ≥∈, i = 1, 2, . . . , m

Table 1 Inputs and outputs used for the efficiency analysis of apple cropping systemsInputi1 Fertilizer costsi2 costs of plant protectionI3 labor costsi4 Machinery costsOutputO1 First class yieldO2 cumulative yield

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1. Define the problem and determine its goal. The goal in the presented case is multi criteria assessment of apple cropping systems (integrated vs. zero-residue).

2. Structure the hierarchy (Fig. 1) from the top (the objec-tives from a decision maker’s viewpoint) through the intermediate levels (objectives on which subsequent lev-els depend) to the lowest level, which usually contains a list of alternatives.

For the assessment of apple cropping systems, the group of experts determined five different objectives/attributes:

− The aggregate attribute ‘Pesticides residues in the fruit’ measures the residues. it is assessed by chemical analysis of fruits at harvest and is divided into param-eters indicating numbers of active substances found in the fruit and their relative shares (% Mrl) in compar-ison to the Mrl (maximum residue level) set by food safety legislation. lists of eU Mrls available at the EFSA Pesticide data base (http://ec.europa.eu/sanco_pesticides/public/index.cfm). Most of the important european market chains (food retailers) have already defined rules about this level (attribute) and use these values to decide whether to accept or reject fruit con-signments from their suppliers. noncompliance with stated % Mrl values will often hinder suppliers from entering into business with market chain companies. Both values (the number of active substances found and the % Mrl) are very important tools for selection and competition among suppliers and also for com-petition among merchants. the merchant, who offers fruits with smaller numbers of residues and with

lower % Mrl, has a competitive advantage. these two values are a part of the information presented in marketing campaigns.

− The number of sprays. although this attribute can be argued to be already incorporated in the previous one, higher numbers of sprayings are regarded as negative by consumers (Weibel et al. 2007). the number of pes-ticide applications per season can be closely related to the pesticide load to which the environment is exposed. Consumers (via the rules of fruit produce certification systems) want to know the information regarding the number of pesticide applications per season.

− Economics. this attribute is divided into yield (% of first class fruit, cumulative yield, and price) that describes the revenue side (yield and price) and variable costs. Fixed costs (depreciation etc.) were assumed to be the same for both production systems. the costs were calculated with the use of the model enterprise budget described in the methodology sec-tion (enterprise budgets).

− Orchard health condition. this attribute describes the incidence of the most important fungal diseases (apple scab—Venturaia inaequalis and powdery mildew—Podosphaera leucotricha) and pests (codling moth—Cydia pomonella). the data are obtained directly from field orchard evaluations carried out according to the standard procedures for plant protection field experiments.

3. Construct a set of pair-wise comparison matrices (size n × n) for each of the lower levels, with one matrix for each element in the level immediately above, using the relative scale measurement. the pair-wise comparisons are done in terms of which element dominates the other (table 2).

the expert group compared the relative importance of each objective in a pair-wise manner on a scale of 1–9 (comparison scale where 1 means that the importance of

Fig. 1 the aHP (analytical Hierarchical Process) hierarchy with de-rived priorities

Table 2 Pair-wise comparisons of the criteria at the highest level of the hierarchy

Pesticide residues in the fruit

number of sprayings

eco-nomics

Orchard “health” conditions

Storage diseases

Pesticide residues in the fruit

5.0 4.0 2.0 2.0

number of sprayings

1/3 1/3 1/3

economics 3.0 2.0Orchard “health” conditions

1.0

Storage diseases

Incon: 0.07

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two objectives is the same, while 9 means that one crite-rion is extremely more important than the other (Fig. 2).

4. Judgments are required to develop the set of matrices in step 3. Reciprocals are automatically assigned in each pair-wise comparison.

5. Hierarchical synthesis is now used to weight the eigen-vectors by the weights of the objectives and the sum is taken over all weighted eigenvector entries correspond-ing to those in the next lower level of the hierarchy.

6. Having made all of the pair-wise comparisons, consis-tency is determined using the eigenvalue, λmax, to calcu-late the consistency index, CI as follows:

(5)

where n represents matrix size. Judgment consistency can be checked by taking the consistency ratio (cr) of ci with the appropriate value. the cr is considered acceptable if it does not exceed 0.10. if it is greater than this value, the judgment matrix is inconsistent. a consis-tent matrix should be obtained by reviewing and improv-ing the judgments.

7. Steps 3–6 are performed for all levels in the hierarchy.

the expert choice software is used to make correspond-ing aHP calculation. it allows us to enter the data for each alternative into the so-called Data grid, where individual

CI =λmax − n

n − 1

objectives can be entered directly. the use of the Data grid combines the power of the hierarchy and the pair-wise com-parison process with the ability to evaluate hundreds or even thousands of alternatives. Pair-wise comparisons are still used to evaluate the elements in the hierarchy itself, but not for evaluating the alternatives. the alternatives’ pri-orities are established relative to each covering objective using ratio scaled rating intensities (scales; see Fig. 2). this procedure can be particularly useful when large numbers of alternatives are to be evaluated, as there is no need to compare alternatives in the pair-wise manner; the values are put directly into the Data grid and priorities are calculated based on pair wise comparison of intensities. the main data source for the AHP assessment, viz the field experiments, is described in the materials section.

Results

Yield comparison

in the 2008 season, apple yields were not statistically differ-ent for three apple varieties ‘elstar’, ‘idared’ and ‘Jonagold’ despite the fact that yields in iP plots were somewhat higher than in the case of the zero plots. Only in case of ‘golden Delicious’ apples was the average yield in iP managed plots

Fig. 2 ratings and pair-wise comparison of scales

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significantly higher than in plots managed according to the zero system. Disease and pest pressures in the 2008 sea-son were moderate. in the 2009 season, differences between total yields of the iP or zero managed plots were not very large. the ‘idared’ and ‘golden Delicious’ yields were sig-nificantly higher in the IP system. The Elstar apple differ-ences were not significant. The total yield of ‘Jonagold’ apples was higher in the zero managed plots, but the differ-ence was not significant. The disease and pest pressure in the 2009 season was high.

results from the 2010 season were similar to the 2009 season. Differences between ‘idared’ and ‘elstar’ varieties were not significant, but significant differences were found for ‘golden Delicious’ and ‘Jonagold’. Disease pressure was very high in the 2010 season. large differences were also noticed also in terms of i class fruits. For the zero sys-tem, we noticed a 5–10 % decrease in i class fruits, mostly due to some small scab lesions or surface damage resulting from insect feeding.

if we compare the averaged data for all three seasons, we can see that the yields between the iP and zero residue systems were significantly different only in the case of ‘golden Delicious’ apples viz in one out of four cultivars investigated. the reason lies in the high susceptibility of cv. ‘golden Delicious’ to apple scab, which can result in huge yield reductions. the three-year average for all four culti-vars together was 46,831 kg/ha for IP plots and 45,653 kg/ha for zero residue plots. the total yield for the zero resi-due system gave a slightly lower result, but the difference

was not statistically significant. This result can be consid-ered as indicating the success of the zero residue system, because we were able to preserve most of the yield despite an approximately 50 % reduction in the amounts of applied conventional pesticides (table 3).

average values (iP vs. zero residue) on total yield for a specific variety and a specific year were compared using a t-test (α = 0.05, n = 4). Data marked with different letters show statistically significant differences.

Comparisons of Net Return and Breakeven Point

the economics of the tested apple production systems was estimated with the use of enterprise budgets. the results (table 4) demonstrate that the zero residue system, in years with suitable weather and moderate pest pressure, can provide similar profitability to IP systems. Note that the breakeven price is calculated for the whole yield and that the calculated market price for both types of apples was the same.

average values (iP vs. zero) on net return and breakeven point for a specific cultivar and a specific year were com-pared using a t-test (α = 0.05, n = 4). Data marked with dif-ferent letters indicate statistically significant differences.

analysis of the net return data calculated on the basis of three-year period (2008–2010) shows us that the net return is higher in iP systems than in zero residue system for all four studied cultivars. the relationship between production costs and yield loss due diseases and pests was different for the iP system than for the zero residue system.

Table 3 average yield (kg/ha) determined on repeated experimental plots for individual varieties during the 2008–2010 (08, 09, 10) seasonsintegrated production system Zero residue production systemParameter elstar idared gD Jonagold elstar idared gD Jonagold1.cl 08 22,722 40,258 44,684 35,069 21,502 30,805 41,767 32,0762.cl 08 11,398 7,204 8,981 6,926 10,034 13,393 8,407 6,9243.cl 08 2,294 3,627 2,120 6,780 4,300 5,407 3,033 8,101cY 08 36,414a 51,089a 55,785b 48,774a 35,837a 49,605a 53,206a 47,101a

1.cl 09 33,531 29,984 26,639 41,412 30,237 23,680 22,012 40,0052.cl 09 11,941 8,105 9,836 4,075 13,396 11,244 7,723 4,3263.cl 09 3,265 6,200 4,508 1,895 4,210 7,666 8,882 4,278cY 09 48,737a 44,289b 40,983b 47,382a 47,843a 42,590a 38,617a 48,609a

1.cl 10 36,024 49,273 33,600 35,454 36,096 46,828 32,040 33,2342.cl 10 6,163 3,855 6,982 4,650 5,248 6,132 7,170 4,6423.cl 10 4,350 2,737 785 4,650 4,290 2,787 504 5,467cY 10 46,536a 55,865a 41,367b 44,754b 45,633a 55,748a 39,714a 43,343a

average seasons 2008–20101.cl 30,759 39,838 34,974 37,312 29,278 33,771 31,939 35,1052.cl 9,834 6,388 8,600 5,217 9,559 10,256 7,767 5,2973.cl 3,303 4,188 2,471 4,442 4,267 5,287 4,140 5,949cY 43,896a 50,414a 46,045b 46,970a 43,104a 49,314a 43,846a 46,351a1.cl first-class fruit, 2.cl second-class fruit, 3.cl third-class fruit (industry), CY cumulative hectare yield, GD golden Delicious

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the differences in breakeven point between the iP and zero residue system were not particularly great when com-pared to differences in net return and they were statistically different only in the case of golden Delicious apples. the reason is connected to the high yield loss for this cultivar because of its susceptibility to apple scab. Differences in breakeven point are not great; therefore, we believe that

even a slight increase in marketing price of zero residue apples can compensate for the financial loss due to lower net return. if we sell zero residue apples for a price that is just 2 € cents/kg higher than the iP apples, we can cover the lower net return under conditions of standard expected yields (at least 45 t/ha) and stable apple market conditions.

Table 4 economic evaluation of studied apple production systemsintegrated production system Zero residue production systemParameter elstar idared gD Jonagold elstar idared gD JonagoldSeason 2008netr − 1.926.88a 2.172.55b 4.015.91b 664.69a − 2.872.28b 293.67a 2.501.76a − 499.88aBreEpo 0.387a 0.315a 0.292a 0.322a 0.403a 0.320a 0.311a 0.338a

Season 2009netr 1.110.19b − 600.89a − 763.43a 1.460.48a 272.15a − 1.764.18a − 3555.00b 1.027.47aBreEpo 0.321a 0.333a 0.368a 0.319a 0.326b 0.371b 0.389b 0.331b

Season 2010netr 1.288.17a 3.604.01b 288.32a 259.26a 1.390.47a 2.446.31a 558.24a − 937.74aBreEpo 0.335b 0.291a 0.349a 0.348a 0.307a 0.292a 0.360a 0.368a

average seasons 2008–2010netr 157.26a 1.725.22b 1.180.27b 794.81b − 416.88a 425.26a − 98.33a − 136.71aBreEpo 0.348a 0.313a 0.336a 0.330a 0.345a 0.328a 0.354b 0.345aNetR net return (€/ha), BreEpo breakeven point (€/kg), GD golden DeliciousaValues of NetR and BreEpo for a specific apple cultivar when comparing integrated and zero residues production system for individual years marked with the same letter do not differ statistically significantly according to the t-test (P<0.05, n=4)bAverage values (integrated versus zero residues system) on NetR and BreEpo for a specific cultivar and a specific year were compared using a t-test (P<0.05, n=4). Data marked with different letters indicate statistically significant differences

Table 5 average values for numbers of active pesticide substances found per fruit sample (no. as.) and for relative concentration of residues in relation to the legally stated Mrl valuesintegrated production system Zero residue production systemParameter elstar idared gD Jonagold elstar idared gD JonagoldSeason 2008no. as. 5.7b 6.3b 6.0b 5.3a 3.7a 3.7a 1.7a 4.3a% Mrl 2.1b 3.0b 3.2b 4.5a 0.5a 0.6a 0.9a 4.0a

Season 2009no. as. 3.0b 2.1a 2.0b 3.0b 0.0a 1.5a 0.5a 0.0a% Mrl 1.5b 3.3b 1.9a 1.7b 0.4a 1.7a 1.5a 0.6a

Season 2010no. as. 4.1b 8.0b 8.5b 4.4b 2.0a 5.0a 5.0a 1.0a% Mrl 1.7b 3.9b 2.7a 1.6a 0.3a 0.5a 2.6a 1.3a

average seasons 2008–2010no. as. 4.3b 5.5b 5.5b 4.2b 1.9a 3.4a 2.4a 1.8a% Mrl 1.1b 2.4b 1.5a 1.1b 0.2a 0.7a 1.4a 0.6a% MRL average for all found active substances togetheraValues of No. as. and % MRL for a specific apple cultivar when comparing integrated and zero residues production system for individual years marked with the same letter do not differ statistically significantly according to the t-test (P<0.05, n=4)bAverage values (integrated versus zero residues system) on No. as. And % MRL for a specific cultivar and a specific year were compared using a t-test (P<0.05, n=4). Data marked with different letters indicate statistically significant differences

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the best result was achieved with ‘idared’ apples. this cultivar seems to be suitable for the zero residue production system, according to our results. the other three cultivars appear to be less suitable. the ‘elstar’ cultivar may also be suitable if yields were slightly higher. the prices of ‘elstar’ apples are usually higher and we do not need to apply as much pesticide (shorter growing season, shorter period of storage).

results of analysis of average number of Pesticide Residues Per Fruit Sample and of the % MRL Value

table 5 shows that for all three trial seasons and for most of the plots the numbers of pesticide residues found per fruit and their average concentrations were significantly lower in apples produced in the zero residue system than in the iP system. the difference in numbers of active substances found is particularly important because iP fruits often con-tained more than four active substances (5–8), which would make these fruits unacceptable for many european food retailers (e.g., aldi, rewe, lidl, tesco, etc.). Fruit produced in zero residue system orchards can comply completely with the strictest rules of European food retailers. Based on this parameter, fruits from a zero residue system can outcompete fruits from iP systems.

The differences in % MRL were not as large. Both fruit types (iP and zero) can completely comply with the strictest rules of the market chains. Most retailers have an accept-able concentration of 33 % of MRL. Concentrations of resi-dues found in our apples (either iP or zero) are far from that limit—many times concentration was less than 1 % of Mrl—which makes these fruits almost comparable to fruits originating from fully organic production systems. (table 5)

average values (iP vs. zero) on no. as. and % Mrl for a specific year and cultivar were compared using a t-test (α = 0.05, n = 4). Data marked with different letters showed statistically significant differences.

Comparisons Using DEA Technical Efficiency

The technical efficiency calculated according to the DEA is presented in table 6. The technically most efficient systems have scores close to 1 and less efficient systems have scores far above 1. the achieved score is directly related to costs of inputs and to the amount, quality, and price of the yield. it is also related to the ratio between costs of input and apple prices. A same ranking indicates equally efficient produc-tion approaches. results obtained by Dea are close to the results obtained by analyzing net return data because Dea is based on relationships between costs and product prices (table 6).

in the 2008 season, we obtained clearly higher techni-cal efficiency scores for the integrated system than for the zero residue system for all four varieties. Yields and propor-tions of first class fruits were higher for IP than for zero residue systems. Pest and disease pressures were moderate. ‘Jonagold’ apples showed a slightly higher yield loss due to the coddling moth and ‘golden Delicious’ apples had quite a high proportion of scabbed fruits. Lower efficiency was determined in the case of the zero residue system ‘golden Delicious’ apples, where scab attack caused increased yield losses despite quite frequent fungicide applications. the sit-uation in the 2009 season was somewhat different. the two systems were almost equally efficient despite the fact that pest and disease pressures were higher in 2009 than they had been in 2008. The ‘Idared’ variety was equally efficient in both systems. the ‘elstar’ apples showed effects of alter-nate bearing, which was the main reason for the decrease in yield in both iP and zero plots. Yield reduction therefore was not predominantly caused by diseases and pests.

the relatively low yield in relation to the cost of produc-tion meant that the calculated efficacy was quite low. Losses in yield due to pests and diseases in the case of ‘Jonagold’ apples (iP and zero) were comparable, as was the cost for plant protection products for their control. We can see from the efficacy data that the ‘Idared’ variety can be considered as suitable for a zero residue system, whereas the ‘golden Delicious’ is not suitable.

Results from the 2010 season again show higher efficacy rates for the iP system. this is partly a result of high dis-ease and pest pressures. the ‘elstar’ variety was the excep-tion, as it showed hectare yields that were comparable in both production systems. Only the input costs in the case of iP production were higher than in the zero system, which resulted in a lower efficiency for the IP system.

The technical efficiency assessment shows that zero sys-tem can be technically efficient if high enough yield and fruit quality criteria are met. in years with higher pest and disease pressures, we have to administer many applications of plant protection products. in these years, the costs for biological plant protection during the second part of the sea-

Table 6 The CCR technical efficiency scores of apple cropping sys-tems for four apple varietiesccr efficiency scores

Scores 2008

rank-ing

Scores 2009

rank-ing

Scores 2010

rank-ing

elstar iP 1.0000 1 0.8707 6 0.9679 4idared iP 1.0000 1 1.0000 1 1.0000 1golden d. iP 1.0000 1 0.9560 3 1.0000 1Jonagold iP 0.9725 4 0.9366 4 1.0000 1elstar 0.0 0.9900 3 0.8550 7 1.0000 1idared 0.0 0.9927 2 1.0000 1 0.9788 3golden d. 0.0 0.8181 6 0.9655 2 1.0000 1Jonagold 0.0 0.9459 5 0.9194 5 0.9987 2IP integrated production, 0.0 zero

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son can be much higher than the costs of conventional plant protection in an iP system. this contributes additionally to the lower efficacy of a zero system in these types of sea-sons. the situation is reversed in seasons with low disease and pest pressures. in these cases, the relative costs of bio-pesticides in the zero system can be lower than the relative costs of conventional pesticides for the iP system.

comparisons of iP and Zero residue Systems Using aHP Multi criteria assessment

the multi criteria assessment (table 6) for the four varieties shows better results for the zero system in 2008 for all four varieties. For the 2009 season, we see a lower assessment score for the ‘idared’ apples in the zero system (0.105) com-pared to the ‘idared’ produced in the iP (0.107). this can be explained predominantly by lower yield (see table 2). in contrast, the assessment score for ‘golden Delicious’

was the same for both systems. in 2010, all four varieties received better assessment scores in the zero system than in the IP system. The reason was again in the significantly lower number of residues and the lower value of % Mrl. (table 7)

The AHP multi criteria evaluation results confirmed that when all incorporated criteria are considered (Fig. 2), the zero system can obtain better assessment in comparison to the iP system in most cases. this is a direct consequence of better values of the attribute ‘Pesticide residues in the fruit’, which was also given the highest weight in pair-wise com-parisons by the experts.

Conclusion

the zero residue apple production analyzed here generated comparable results to a standard iP system. total yield har-

Table 7 aHP calculation for the highest level of the hierarchyPriority (a)

2008 Weight (W) elstar iP idared iP gD iP Jonagold iP elstar zero idared zero gD zero Jonagold zeroPesticide residues in the fruit

0.441 0.123 0.089 0.070 0.126 0.146 0.145 0.17 0.131

number of sprayings

0.058 0.199 0.081 0.199 0.081 0.081 0.081 0.199 0.081

economics 0.218 0.146 0.13 0.136 0.111 0.149 0.085 0.102 0.140Orchard health conditions

0.138 0.151 0.121 0.121 0.121 0.151 0.121 0.121 0.091

Storage diseases 0.145 0.085 0.085 0.209 0.209 0.085 0.034 0.085 0.209∑Wa 0.131 0.101 0.119 0.131 0.135 0.109 0.138 0.136

2009 elstar iP idared iP gD iP Jonagold iP elstar 0.0 idared 0.0 gD 0.0 Jonagold 0.0Pesticide residues in the fruit

0.441 0.109 0.084 0.109 0.105 0.169 0.114 0.141 0.169

number of sprayings

0.058 0.199 0.081 0.199 0.081 0.081 0.081 0.199 0.081


0.138 0.14 0.14 0.113 0.14 0.113 0.113 0.101 0.140


2010 elstar iP idared iP gD iP Jonagold iP elstar zero idared zero gD zero Jonagold zeroPesticide residues in the fruit

0.441 0.109 0.084 0.109 0.105 0.169 0.114 0.141 0.169

number of sprayings

0.058 0.199 0.081 0.199 0.081 0.081 0.081 0.199 0.081


0.138 0.128 0.128 0.128 0.128 0.128 0.128 0.128 0.128


GD golden Delicious, 0.0 zero

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vested in plots managed according to the zero residue sys-tem was lower in many trial repetitions, but also was often not significantly lower than in the IP plots. Differences in net returns were larger and were statistically significant. this means that a zero residue system cannot provide a level of economic feasibility comparable to the iP production sys-tem under the assumption that the achieved market price for both types of apples would be the same. in our case, the benefit of producing zero residue apples is not realized in terms of achieving a higher market price, but in terms of increasing the market competitiveness of the producer due to the lower numbers of pesticide residues found in fruit and the lower % MRL value. Because introduction of these two parameters into the system of market evaluation of apples and in basic demands of merchants to their suppliers, these two criteria today are just as important as a low price for apples. growers that cannot comply with the demands expressed in terms of numbers of active substances found and % Mrl will be almost totally excluded from business.

Demands for pesticide residue reduction are increasing every day. From point of view of further increases in these demands, the zero residue system represents a reasonable marketing strategy, despite its overall appearance of being economically less feasible at the moment. We had hoped that during the performance of our trials we would be able to achieve further decreases in the numbers of residues found in our fruit. Our check on the eU retailer rules for the accep-tance of fruit made it obvious that the concentration of resi-dues is of minor importance for marketing apples compared to the data on the numbers of active substances found.

consumers do accept the explanation that the theoretical adverse health effect of pesticide residues in fruit is smaller if a smaller number of active substances are found in the fruit, but they do not understand the differences in the extent of adverse effects possible due to different residue concen-tration levels (for example, between 50 % Mrl and 5 % Mrl). the comprehension of many eU consumers seems to be that pesticide residues have adverse health effects at any concentration level; therefore, these consumers always prefer fruits with the smallest possible number of residues. For apple producers, it is easier to decrease the concentra-tion of residues than the number of found residues per fruit sample. the reason also lies in the rapid improvements in residue detection and quantification limits of modern labo-ratory analytical methods (far above 0.01 ppb).

the Dea results showed that the zero residue system cannot be always declared less technically efficient com-pared to the iP system. the aHP assessment also showed that the zero residue system can be judged as better than the iP system when giving higher priorities to the afore-mentioned pesticide residue parameters. We think that a balanced judgment of the zero residue system will required that all available assessment approaches be combined, since

each of these approaches separately does not provide suf-ficient information for producers to make good decisions about acceptance of the zero residue system.

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