No Evidence of Trade-Off between Farm Efficiency and Resilience: Dependence of Resource-Use Efficiency on Land-Use Diversity

Helena Kahiluoto; Janne Kaseva

doi:10.1371/journal.pone.0162736

Abstract

Efficiency in the use of resources stream-lined for expected conditions could lead to reduced system diversity and consequently endanger resilience. We tested the hypothesis of a trade-off between farm resource-use efficiency and land-use diversity. We applied stochastic frontier production models to assess the dependence of resource-use-efficiency on land-use diversity as illustrated by the Shannon-Weaver index. Total revenue in relation to use of capital, land and labour on the farms in Southern Finland with a size exceeding 30 ha was studied. The data were extracted from the Finnish Profitability Bookkeeping data. Our results indicate that there is either no trade-off or a negligible trade-off of no economic importance. The small dependence of resource-use efficiency on land-use diversity can be positive as well as negative. We conclude that diversification as a strategy to enhance farm resilience does not necessarily constrain resource-use efficiency.

Citation: Kahiluoto H, Kaseva J (2016) No Evidence of Trade-Off between Farm Efficiency and Resilience: Dependence of Resource-Use Efficiency on Land-Use Diversity. PLoS ONE 11(9): e0162736. https://doi.org/10.1371/journal.pone.0162736

Editor: Gui-Quan Sun, Shanxi University, CHINA

Received: February 26, 2016; Accepted: August 26, 2016; Published: September 23, 2016

Copyright: © 2016 Kahiluoto, Kaseva. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The third party to hold the data is Natural Resources Institute Finland, and the person to contact in this matter is Arto Latukka (e-mail arto.latukka@luke.fi).

Funding: This work was supported by the Finnish Climate Change Adaptation Research Programme (ISTO) and by the Academy of Finland (http://www.aka.fi), grants 140870 and 255954. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Evidence-based policy may be wishful thinking in times of turbulence and multi-dimensional epistemic and ontological uncertainty. Robust strategies [1], which work well even if the information is imperfect or inputs to the system vary [2], may yield a more favorable cost-benefit ratio for societal investments [3]. Enhancement of system resilience is one such robust strategy [2], and therefore currently an important complementation to add to efficiency, for sustainability of farming.

Resilience is the capacity of a system to tolerate disturbance and reorganize while retaining its function, structure and identity [4–6], and to shape change and learn [7–8]. If a social-ecological system threatens resilience at larger scales, transformational change is required [9]. In the face of increased turbulence in the global climate and markets, the resilience discourse has emerged in international environmental and economic policy since the start of the current decade identity, e.g., [10–12]. Increasing effort has also been addressed to mathematically model various aspects of stability of complex systems, e.g., [13–16]. Diversity generates a variety of possible responses to variability [17–19] and to various threats [20] and material for transformation [21], and as such is a prerequisite for system resilience [20, 22–26]. Diversity also implies the generation of ‘perpetual novelty’ [26], which is critical for reorganizing the system after disturbance [27].

Efficiency is a key economic concept and has made a crucial contribution to sustainability discourses across disciplines and sectors [28]. Eco-efficiency, in terms of the efficient use of resources (‘more from less’), has long dominated the interpretation of sustainability [29–30] and has substantially influenced societal development strategies. Increasing resource-use efficiency, implying a small ratio of resources to products or revenue, e.g., higher yields per unit area of land or other natural or economic resources in agricultural systems, is believed to promote economic performance, food security and environmental protection [31–32]. Korhonen and Seager [33], Ulanowicz et al. [34] and Goerner et al. [35] argued for the complementarity of the two perspectives, i.e., efficiency and resilience, in sustainable development.

In stable times, system efficiency is streamlined for expected conditions, often creating systems with less diversity, as exemplified by the development at various levels of agricultural systems in industrial countries in recent decades [35–42]. Diversity increases stability of production in variable agricultural environments [43], and land-use diversity appears to increase farm resilience [44] also in terms of economic returns [45–46]. Consequently, efficiency may run counter to system resilience [33], especially through loss of response diversity [18,20]. On the other hand, based on ecological models with economic relevance, Tilman et al. [47] concluded that diversity should enhance efficiency in the use of limited resources.

The relationship of economic performance and biodiversity has been assessed [45–46], especially from the viewpoint of ecosystem services [48–50]. Further, eco-efficiency in terms of products relative to emissions has been related to diversity in what-if scenarios of social-ecological systems [51]. However, empirical evidence for the dependence between resource-use or economic efficiency and production diversity is scarce or non-existent. This knowledge gap is also practically important, because the current understanding of a trade-off relation between economic efficiency and diversity in farming informs agricultural policies.

Inspired by the model-based study of Tilman et al. [47], we tested in an empirical case the general belief that diversity reduces efficiency, e.g., [44]. The aim of this study thus was to investigate empirically, whether there is trade-off between diversity (critical for system resilience) and efficiency of resource-use (also required for sustainability) on farms, and that way contribute to bridging the knowledge gap. We tested the hypothesis that land-use diversity is negatively related to farm efficiency in terms of revenue per unit of land, labour and capital. We tested this hypothesis in the context of Finnish farms, which during the two last decades increased rapidly in specialization and size to achieve greater resource-use efficiency through economies of scale [52–53], and for which resilience is of paramount importance, due to the northernmost location in the world and therefore a rapid climate change, as well as tight linkages to volatile global food markets.

Materials and Methods

Farm data

The empirical data analysed here originate from the Finnish profitability bookkeeping data used to compile the Finnish data for the European Farm Accountancy Data Network (FADN), which is maintained by the European Commission. For comparability and access, variable definitions similar to those in FADN were used for the accounting years of 1998–2008 ([54]; http://ec.europa.eu/agriculture/rica/definitions_en.cfm). FADN is used throughout Europe to evaluate income from agricultural holdings and the impacts of the Common Agricultural Policy (CAP). Finland has four FADN regions, but the climatic conditions for agriculture in the north clearly differ from those for the south, and most agricultural production, accounting farms and land-use diversity, is concentrated in southern Finland. Other areas were thus excluded from the analysis to remove the bias that could arise through independent effects of climate on the revenue and on the diversity of agricultural land-use. In addition, available agricultural area restricts land-use diversity. For smaller farms also other reasons of farming (recreation, maintenance of land value, life style, emotional reasons such as heritage etc.) are in a bigger role which could cause bias into the conclusions if such farms would be included in the analysis of the trade-off between resource-use efficiency and diversity (reflecting the relation between economic efficiency and resilience), because such relation has no relevance for the farmers in those cases. Therefore, the utilised agricultural area (UAA) below 30 hectares (ha) was set as the lower limit for farm size in our analysis. After these restrictions were applied to remove obvious sources of bias, we were left with the empirical data for 3 268 farms totally over the years (Table 1).

Download:

Table 1. Farm inputs (labour, capital and land), total revenue and land-use diversity per production line.

https://doi.org/10.1371/journal.pone.0162736.t001

Land-use diversity

The Shannon-Weaver index [henceforth, the Shannon index] [55], the most commonly used diversity index, was used to illustrate farm land-use diversity. Specifically, the Shannon index was used to describe the number and proportional area distribution (richness and evenness) of eight farm land-use types. A Shannon index equal to zero indicates that the farm comprises only one land-use type; the value of the Shannon index increases as the number of different land-use types and/or their evenness increases. The Shannon index gives an equal weight to each observation and is comparable among cases with different compositions [56]. The Shannon index was calculated according to the following eq (1): (1) where k = 1, …, K referring to the number of land-use types; w_ik is the area covered by land-use type k of farm i; W_i represents the total area of farm i; and w_ik/w_i is the proportion of area covered by land-use type k. The Shannon index is expressed in logarithmic form, and to describe the true diversity (‘land-use diversity’), it needs to be converted (exp(H)).

Agricultural specialisation is a categorical variable in the Finnish profitability bookkeeping data (as in FADN); it consists of categories that are both exhaustive and mutually exclusive such that each observation is assigned to one and no more than one category. The following six agricultural land-use types were used as independent classes for calculating the Shannon index: cereals (corresponding to FADN variable SE035), other field crops (SE041), vegetables, berries, flowers and ornamental plants (SE046*-SE046**), perennial crops (SE054-55), fodder crops and fallow (SE071-73), and other. Only on 69 farms the class ‘other’ represented more than 10% of the agricultural land area, while 89% from the total 3 268 farm observations over years did not have the class ‘other’ at all. This indicates that no bias in the analysis was caused by the lacking information of the diversity within the class ‘other’. We then calculated Pearson’s correlation coefficients for the Shannon index and farm input/output variables, such as UAA, labour, farm capital and total revenue.

Resource-use efficiency

Resource-use efficiency was measured as a relation between the use of the major farm resources land, labour and capital, and farm revenue, using Cobb-Douglas regression model. In addition, ‘technical efficiency’ was measured with stochastic frontier models as the ratio between the observed output (here total revenue) to the maximum output under the assumption of fixed inputs [57], i.e., the resource-use efficiency of individual farms relative to the maximum resource-use efficiency of the farms. Use of the major farm resources land, labour and capital was illustrated by the following input resources: the total UAA of holding (ha; SE025), total labour input on holding in hours (h; SE011), and farm capital as the sum of the average of the working capital of livestock, permanent crops, land improvements, buildings, machinery and equipment, and circulating capital (€; SE510). The total revenue was calculated as output from crops and crop products, livestock and livestock products and other output (corresponding to FADN variable SE131), in €. For detailed definitions of the variables, see [54].

The Cobb-Douglas regression model

The Cobb-Douglas production function see [58] is widely used in econometrics to represent the relation between several inputs and production [59]. It allows the quantity of one input to affect the productivity of another input. We included the Shannon index in the model, in analogy to inputs [60]. The Cobb-Douglas model included three inputs (land, capital and labour), the Shannon index, and a single output, total revenue. The model can be expressed as (2) where y_j is the output of farm j and x₁, x₂ and x₃ are the area (ha), labour (h) and farm capital (€) of farm j. The parameter H_j is the Shannon index of farm j, and ε_j is the error term, which was assumed to be independent and normally distributed. The remaining parameters (A, β₁, β₂, β₃ and ω) were unknown and had to be estimated. The equation was modified for estimating the coefficients of the parameters. Taking the natural logarithm of both sides of the equation leads to (3)

By choosing Y = ln(y_j), X = ln(x_ij) and H = H_j, we obtained a linear regression equation (4) where the unknown parameters could be estimated.

Because the production lines differed in terms of diversity and revenue, we included the production lines in the model as dummy variables. Dummy variables are a series of binary variables that identify whether or not each observation is a member of a specific category. The years with a statistically significant (α = 0.1) interaction of Shannon index and production line were not included in further analyses due to technical and interpretational complexities.

The stochastic frontier production models

The Cobb-Douglas regression models used in the analyses above assume that all farms represent equal technical efficiency. Because this assumption may not be valid, we also applied a stochastic frontier production function, which adds to the model a new term, technical inefficiency that after a mathematical transformation represents the technical efficiency of each farm.

We used two most common stochastic production functions: the Cobb-Douglas and the translog production function. The translog production function is more flexible, because all second order cross-terms of inputs and the Shannon index are included in the model, unlike the Cobb-Douglas production function that assumes all cross-terms to be zero. The stochastic frontier production model included three inputs (land, capital and labour), the Shannon index and a single output, i.e., total revenue. We also investigated whether the association of the total revenue and the Shannon index depends on production line (Table 1) by including a separate intercept term for them.

Thus, the Cobb-Douglas production function can be expressed as (5) and the translog production function as (6) where y is the output of farms and n is the number of inputs added with the Shannon index (x). The parameters β₀, β_i and β_ij are the unknown parameters to be estimated. The ε = v-u, where v is the systematic error component, which is assumed to be independently and identically distributed, random error having normal distribution with mean being zero and variance being σ_v². u is a non-negative random variable, which is assumed to account for technical inefficiency in production, having normal distribution with mean being zero and variance being σ_u² (Fig 1).

Download:

Fig 1. The stochastic production frontier [61–62].

Observed productions and frontier productions are indicated with x and o, respectively. The frontier production (FP), consisted of observed production, inefficiency effect and random noise, can lie above or below the frontier prodution function (PF), depending on the noise effect.

https://doi.org/10.1371/journal.pone.0162736.g001

Model comparisons

We determined whether the technical inefficiency term needs to be added, relative to the Cobb-Douglas regression models, i.e., whether stochastic frontier models (Cobb-Douglas or translog stochastic frontier models) would be required instead of Cobb-Douglas regression models (see above). The key parameter to test the need of the technical inefficiency term is γ = σ_u²/(σ_u²+σ_v²), which tells the proportion of variance of the efficiency term from the overall variance. If hypothesis H₀: γ = 0 holds, there is no need for an efficiency term in the model. Since γ ϵ [0,1] and the hypothesis is one-sided, we used critical values from Kodde and Palm (1986) for a likelihood ratio test. The distributions of a technical inefficiency term were compared based on the information criteria (AIC, Bayesian information criterion (BIC)).

The likelihood ratio test and BIC were used to determine whether the translog production function would be more appropriate than the Cobb-Douglas production function in the stochastic frontier models. The likelihood ratio test statistic can be defined by (7) where L(H₀) and L(H₁) are the values of the likelihood function of the null hypothesis (H₀: β_ij = 0) and the alternative hypothesis. Test statistic D is distributed chi-squared with degrees of freedom equal to the number of parameters that are constrained. BIC can be defined by (8) where k is the number of free parameters and N is the sample size.

To facilitate the interpretation of the translog production models, all the variables were divided by their sample means before estimation. Consequently, the first-order coefficients can be interpreted as elasticities of the sample means [63]. The interaction terms show how the estimated elasticities vary with a movement away from the sample means.

The statistical analyses were performed using SAS software (SAS Institute, Inc., Cary, NC, USA) and the REG, GLM, MIXED and QLIM procedures. The R 3.1.1 package ‘frontier’ (version 1.1–0) was also used to test more complex models that could not be applied using SAS (R Development Core Team).

Results

Land-use diversity in relation to farm inputs and total revenue

The Shannon index varied by farm production line (Table 1); the highest diversity indices were found for farms with field crops as the production line, followed by farms with field crops and grazing livestock. The lowest Shannon index was measured for specialist granivore farms. Only five farms had five different land-use types; the greatest proportion of the farms (55%) had three different land-use types, and forty farms had only a single land-use type. The Shannon index for farm land-use diversity was weakly negatively correlated with total revenue, while it was weakly positively correlated with UAA (Table 2). However, in production-line-specific analyses, the Shannon index was correlated with UAA only, i.e., weakly positively correlated with UAA for cereals, oilseeds and protein crops (r = 0.27, P<0.001) and field crops (r = 0.28, P<0.001) farms.

Download:

Table 2. Pearson correlation matrix of land-use diversity, total revenue and inputs (labour, farm capital and land).

https://doi.org/10.1371/journal.pone.0162736.t002

Land-use diversity in relation to farm resource-use efficiency

Cobb-Douglas regression model.

When using the traditional Cobb-Douglas regression models, we found no statistical support for the negative dependence of resource-use efficiency on land-use diversity. There was no statistically significant difference in the dependence of resource-use efficiency on land-use diversity among the production lines; p-values of the Shannon index varied from 0.48 to 0.83 over years. In the fittest model which included the three inputs (land, labour and capital), the Shannon index and the production lines, the estimate of the Shannon index varied from -0.085 to 0.028 with the confidence interval from [-0.323, 0.153] to [-0.206, 0.263], respectively. The models explained 80–86% of the total variance, while the proportion of land-use diversity was less than one percent. In the year 2000, the traditional Cobb-Douglas regression model where the difference among farms in resource-use efficiency is included in the experimental error term, was shown to be adequate, but in other years stochastic frontier production models were preferred (Table 3).

Download:

Table 3. Comparison of the Cobb-Douglas regression (H₀) and stochastic frontier Cobb-Douglas (H₁) and translog (H₂) production models.

https://doi.org/10.1371/journal.pone.0162736.t003

Stochastic frontier Cobb-Douglas production model.

In the stochastic frontier production models, where a new term, the technical inefficiency of farms was included, the null hypothesis, H₀: γ = 0 (the technical inefficiency effect in the model is zero), was rejected (P<0.002) for all the years (apart from 2000) (Table 3). Therefore, we concluded that there was a technical inefficiency effect in the model for all the years (apart from 2000), i.e., the farms differed from each other in terms of resource-use efficiency, and therefore the stochastic frontier models fitted to the data better than the regression models. The exponential distribution for the technical inefficiency term was found to be more appropriate than the half-normal and truncated normal distributions, based on the information criteria. However, the differences among the selected distribution and the other ones considered were minor.

According to the BIC criterion, the Cobb-Douglas stochastic frontier model was adequate for every year. Similarly to the regression model, the fittest stochastic frontier production model showed no statistical support for the negative dependence of resource-use efficiency on diversity: p-values of the Shannon index varied from 0.58 to 0.99 over years (Table 4). The mean technical efficiency score of farms, 0.8, indicated that the average resource-use efficiency of the farms was 80%.

Download:

Table 4. Maximum-likelihood estimates of the land-use diversity of the stochastic frontier Cobb-Douglas production models.

https://doi.org/10.1371/journal.pone.0162736.t004

Stochastic frontier translog production model.

Based on the alternative comparison method, likelihood ratio test, the Cobb-Douglas stochastic frontier production model was an adequate functional form for the years 2004 and 2006 (Table 3). For the years 2001, 2002 and 2005, the more flexible translog production model was more appropriate. In the years 2001, 2002 and 2005, a small positive, statistically non-significant, dependence of resource-use efficiency on land-use diversity was indicated p = 0.406, p = 0.663 and p = 0.560, respectively) (Table 5).

Download:

Table 5. Maximum-likelihood estimates of the stochastic frontier translog production models for years 2001, 2002 and 2005.

https://doi.org/10.1371/journal.pone.0162736.t005

According to the translog production model, the coefficients of the interactions of land-use diversity with UUA and capital indicated that as land-use diversity of a farm increases, the resource-use efficiency of UUA use tends to decrease and the resource-use efficiency of capital use to increase, in terms of revenue [63]. There was no statistically significant interaction of land-use diversity and resource-use efficiency in labour use (Table 5). The elasticities of inputs of both Cobb-Douglas and translog models could be interpreted similarly, because all the variables were divided by their sample means before estimation. The elasticities illustrate the dependence of resource-use efficiency on land-use diversity and inputs. The small negative elasticities of land-use diversity in 2004 and 2006 indicated that a 10% increase in land-use diversity would result in approximately half a percentage decrease in total revenue. The positive elasticities indicated increase in total revenue by increasing inputs (Fig 2). The confidence intervals indicate that total revenue could at maximum increase by 3% (year 2001) or decline by 3% (year 2004) associated to 10% increase in land-use diversity (Fig 2).

Download:

Fig 2. Dependence of resource-use efficiency on land-use diversity.

Estimated elasticities of land-use diversity and inputs in years 2000–2002 and 2004–2006 summarised based on the results of the adequate model for each year. The elasticities illustrate the dependence of resource-use efficiency on land-use diversity and inputs. The small negative elasticities of in 2004 and 2006 indicate that a 10% increase in land-use diversity, with an average input use, would result in approximately half a percentage decrease in total revenue. The positive elasticities indicate increase in total revenue by increasing inputs. UAA = utilised agricultural area.

https://doi.org/10.1371/journal.pone.0162736.g002

The lack of statistical significance in the dependence of resource-use efficiency on land-use diversity, together with the near-zero value of the coefficient for the land-use diversity, indicate that there is either no dependence of resource-use efficiency on land-use diversity or the dependence is very small.

Discussion

Dependence of farm productivity on diversity

According to our analysis, resource-use efficiency, i.e., the revenue per unit of land, labour and capital, had no relation or only a negligible positive or negative relation with land-use diversity. Consequently, the results did not support our hypothesis of an inverse dependence between farm resource-use efficiency (critical for economic revenue as well as for eco-efficiency) and diversity (critical for resilience). The slight relations observed were inconsequent and weak enough to suggest a low economic significance for the relation in practice.

Tilman et al. [63] showed, however, based on theoretical models, that the primary productivity of ecosystems and the efficiency of resource use should be positively associated with diversity. Contrary to the general belief driving the dominant development of agricultural landscapes, the ecosystem should not only become more temporally and spatially stable [63] as diversity increases, but would also give greater yields and represent greater economic value in a resource-limited situation than is possible given lower diversity.

Tilman et al. [63], p. 416 suggest that in a homogeneous and unchanging environment, the value of diversity arises because sampling from more species or industries allows a better fit between the winning species or industry and environmental conditions. In heterogeneous environments, diversity is of value because an array of options increases the goodness of fit between species traits and environmental conditions. Thus greater diversity allows more complete spanning of potential ecosystem or market niches. Their results show that decreased local diversity can lead to lower production (in accordance to [17]) and lower efficiency in use of limiting resources. This relation did not hold for land-use diversity in our empirical case of the farms with an average or larger land area in southern Finland. This was the case neither within a time span of one year only which exemplifies the significance of diversity as a response to spatial but not to temporal variation, nor when a longer period of six years was considered.

Fletcher and Hilbert [64], also through a theoretical modelling exercise, showed that the opportunity cost of resilience in landscape exploitation varied greatly depending on the management strategy. They emphasized that a given stable and sustainable equilibrium point in an exploited system will be more or less resilient depending on the exploiter's strategy, because of multiple steady states in the nonlinear systems. These multiple steady states [4] give rise to the potential for a discontinuous resilience threshold, with resilience and production maintained on one side and greatly degraded on the other. Such variation in management strategies among farms may have contributed to our results of an inconsistent relation between diversity as a key determinant of resilience, and resource-use efficiency.

Our results are in accordance with the theoretical conclusions of Fletcher and Hilbert [64]: adopting, e.g., diversification as a strategy to enhance resilience does not exclude relatively high resource-use efficiency in terms of economic revenue. This insight emphasizes the potential of adaptive management in farming to simultaneously embrace both of the essential dimensions of sustainability, i.e., resilience and effectiveness [65]. Agricultural management practices benefitting multiple goals [66–67], such as resilience and resource-use efficiency, are increasingly needed to sustain food security in the face of resource scarcity and uncertainty caused by climate change and market volatility.

Reliability and generality of the findings

The dominant discourse of sustainable development focuses on eco-efficiency, with resource-use efficiency as an important precondition. This study related another dimension of sustainability with increasing importance, resilience, to efficiency of generating revenue by use of the most important natural and economic agricultural resources: land, labour and capital. The total farm revenue is also closely related to the output of material goods, i.e., the provisioning ecosystem service of food production [68]. As the product prices and subsidy level are practically equal for all studied farms that have a similar location and size class in the years of stable price development, the revenue is also linearly related to food output among farms within a production line. Therefore, also the price level hardly interferes with the relation between diversity and resource use efficiency in terms of farm revenue within production line. Thus, this estimate of resource-use efficiency can be considered a representative indicator, representing the efficiency in use of natural and economic resources, in terms of food supply and economic revenue.

Functional diversity in landscapes contributes to supporting, regulating, provisioning and cultural ecosystem services [47,66,68]. Diversity in agricultural land-use, however, generates not only functional diversity, but may also provide response diversity associated with resilience to turbulence [20,23]. Ecological, institutional, and livelihood diversity may be among the least controversial determinants of resilience, indicating both reduced sensitivity of a system to disturbance (e.g. [69–71]) and enhanced ability to recover from and adapt to abrupt changes that are difficult to predict, i.e., adaptive capacity [72–73]. Specifically, diversity of agricultural land-use reduces vulnerability and has thus been linked to the resilience of social-ecological systems [45,51, 74–75]. Land-use diversity encompasses both the ecological and economic dimensions of farm resilience, along with socio-cultural and other dimensions [76] and is annually managed by every farm.

Farms in southern Finland represent the most northern and thus climatically vulnerable agriculture in the world. Since climate change is especially rapid in the Northern areas, Finnish farms may face the most rapid climate change of any farms in the world, with all attendant direct and indirect impacts and a high degree of uncertainty with various ecological, livelihood and knowledge-related sources [3,76–77]. Finnish farms are also tightly linked with volatile regional and global food markets. The focus on farms representing the main production lines at these northern latitudes and land area exceeding 30 ha, ensures the relevance of the case for land-use and food production. The slight increase in land-use diversity along with farm size is in accordance with the findings of McNamara and Weiss [78]. By restricting the case by region and by size class, we avoided bias from the dependence of diversity and revenue per resource unit on the latitude or size. Such simultaneous dependence could be erroneously interpreted as dependence between diversity and resource use efficiency, while being an artefact.

Conclusions

We conclude that diversification, as a strategy to enhance resilience in agriculture, hardly constrains resource-use efficiency. This conclusion contradicts the assumption of an unavoidable trade-off between efficiency and diversity, which has driven the continuous decline in the diversity of agricultural systems. Consequently, striving for resilience is not necessarily opposed even to short-term economic efficiency.

Acknowledgments

We are grateful to Arto Latukka and his team in MTT Agrifood Research Finland for providing the Finnish Profitability Bookkeeping data.

Author Contributions

Conceptualization: HK.
Data curation: JK.
Formal analysis: JK.
Funding acquisition: HK.
Investigation: HK JK.
Methodology: JK.
Project administration: HK.
Software: JK.
Visualization: JK.
Writing – original draft: HK JK.
Writing – review & editing: HK JK.

References

1. Regan HM, Ben-Haim Y, Langford B, Wilson WG, Lundberg P, Andelman SJ et al. Robust decision-making under severe uncertainty for conservation management. Ecol Applic. 2005;15(4): 1471–77.
- View Article
- Google Scholar
2. Anderies JM, Folke C, Walker B, Ostrom E. Aligning Key Concepts for Global Change Policy: Robustness, Resilience, and Sustainability. Ecol Soc. 2013;18(2): 8.
- View Article
- Google Scholar
3. Dessai S, Hulme M, Lempert R, Pielke R Jr. Climate prediction: a limit to adaptation? In: Adger WN, Lorenzoni I, O’Brien K, editors. Adapting to Climate Change: Thresholds, Values, Governance. Cambridge University Press, Cambridge; 2009. pp. 64–78.
4. Gunderson L, Holling CS, Pritchard L, Peterson GD. Resilience. Vol. 2, The Earth system: biological and ecological dimensions of global environmental change, In: Mooney HA, Canadell JG, editors. Encyclopedia of Global Environmental Change 2002: 530–1.
5. OSU (The Ohio State University). Toward Sustainable Systems. Results of an Interdisciplinary Symposium, organized by the Center for Resilience at the Ohio State University. 2006;13.
6. Walker B, Holling CS, Carpenter SR, Kinzig A. Resilience, adaptability and transformability in social-ecological systems. Ecol Soc. 2004;9(2): 5.
- View Article
- Google Scholar
7. Carpenter SR, Walker BH, Anderies JM, Abel N. From metaphor to measurement: resilience of what to what? Ecosys. 2001;4: 765–781.
- View Article
- Google Scholar
8. Folke C, Carpenter S, Elmqvist T, Gunderson L, Holling CS, Walker B. Resilience and sustainable development: Building adaptive capacity in a world of transformations. Ambio 2002;31: 437–440. pmid:12374053
- View Article
- PubMed/NCBI
- Google Scholar
9. Folke C, Carpenter SR, Walker B, Scheffer M, Chapin T, Rockström. Resilience thinking: integrating resilience, adaptability and transformability. Ecol Soc 2010;15(4): 20. [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art20/
- View Article
- Google Scholar
10. BMVBS. National strategies of European countries for climate change adaptation: A review from a spatial planning and territorial development perspective. Published by Federal Ministry of Transport, Building and Urban Development (BMVBS). Scientific Support Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) within Federal Office for Building and Regional Planning (BBR). Online-Publikation 2010;No. 21.
11. COM. Adapting to climate change: Towards a European framework for action. Brussels, White paper. 2009; 01472009:April 1.
12. OECD. International Futures Project on Future Global Shocks “Four Faces of Tomorrow” Report prepared by: John Casti IIASA, Laxenburg and The Kenos Circle, Vienna IFP/WKP/FGS(2011) 6; 2011.
13. Sun G-Q, Wang S-L, Ren q, Jin Z, Wu Y-P. Effects of time delay and space on herbivore dynamics: linking inducible defenses of plants to herbivore outbreak. Sci. Rep. 2015; 5:11246. pmid:26084812
- View Article
- PubMed/NCBI
- Google Scholar
14. Sun G-Q, Zhang J, Song L-P, Jin Z, Li B-L, Pattern formation of a spatial predator–prey system. Applied Mathematics and Computation 2012; 218:11151–11162.
- View Article
- Google Scholar
15. Sun G-Q. Pattern formation of an epidemic model with diffusion. Nonlinear Dyn 2012; 69:1097–1104.
- View Article
- Google Scholar
16. Li L, Jin Z. Pattern dynamics of a spatial predator–prey model with noise Nonlinear Dyn 2012; 67:1737–1744.
- View Article
- Google Scholar
17. Himanen SJ, Ketoja E, Hakala K, Rötter R, Salo T, Kahiluoto H. Cultivar diversity has great potential to increase yield of feed barley. Agron Sust Dev.2013;33(3): 519–530.
- View Article
- Google Scholar
18. Kahiluoto H, Kaseva J, Hakala K, Himanen SJ, Jauhiainen L, Rötter RP et al. Cultivating resilience by empirically revealing response diversity. Global Environ Change 2014:25: 186–193.
- View Article
- Google Scholar
19. Mäkinen H, Kaseva J, Virkajärvi P, Kahiluoto H. Managing resilience of forage crops to climate change through response diversity. Field Crops Research 2015;183: 23–30.
- View Article
- Google Scholar
20. Nelson DR, Adger WN, Brown K. Adaptation to Environmental Change: Contributions of a Resilience Framework. Ann Rev Environ Resources 2007;32:395–419.
- View Article
- Google Scholar
21. Geels FW. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy 2002; 31:1257–1274.
- View Article
- Google Scholar
22. Chapin FS, Walker BH, Hobbs RJ, Hooper DU, Lawton JH, Sala OE et al. 1997. Biotic control over the functioning of ecosystems. Sci 1997; 277:500–504.
- View Article
- Google Scholar
23. Elmqvist T, Folke C, Nyström M, Peterson G, Bengtsson J, Walker B, Norberg J. Response diversity, ecosystem change, and resilience. Front Ecol Environ. 2003;1(9):488–494
- View Article
- Google Scholar
24. Folke C, Carpenter SR, Walker BH, Scheffer M, Elmqvist T, Gunderson LH et al. Regime shifts, resilience and biodiversity in ecosystem management. Annual Rev Ecol, Evol Systematics 2004;35: 557–581.
- View Article
- Google Scholar
25. Folke C. Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environ Change 2006;16: 253–267.
- View Article
- Google Scholar
26. Levin SA. Ecosystems and the biosphere as complex adaptive systems. Ecosys. 1998;1:431–436.
- View Article
- Google Scholar
27. Folke C, Holling CS, Perrings C. Biological diversity, ecosystems and the human scale. Ecol Appl. 1996;6: 1018–1024.
- View Article
- Google Scholar
28. Lazear E. Economic imperialism. Quart J Econ. 2000;115(1): 99–146.
- View Article
- Google Scholar
29. Hoffren J, Korhonen J. Editorial: Eco-efficiency is important when it is strategic. Progress Industrial Ecol. 2007; 4:1–18.
- View Article
- Google Scholar
30. Robèrt K-H, Schmidt-Bleek B, Aloise de Larderel J, Basile G, Jansen JL, Kuehr R et al. Strategic sustainable development–selection, design and synergies of applied tools. J Cleaner Prod. 2002; 10:197–214.
- View Article
- Google Scholar
31. Lankoski JE, Ollikainen M. Counterfactual approach for assessing agri-environmental policy: The case of the Finnish water protection policy, 122nd Seminar, European Association of Agricultural Economists, 17–18 February 2011, Ancona, Italy; 2011.
32. Tilman D, Fargione J, Wolff B, D'Antonio C, Dobson A, Howarth R et al. Forecasting agriculturally driven global environmental change. Sci. 2001;292: 281–284.
- View Article
- Google Scholar
33. Korhonen J, Seager T. Beyond eco-efficiency: A resilience perspective. Business Strategy and the Environ. 2008;17: 411–419.
- View Article
- Google Scholar
34. Ulanowicz RE, Goerner SJ, Lietaer B, Gomez R. Quantifying sustainability: Resilience, efficiency and the return of information theory. Ecol Complexity 2009;6: 27–36.
- View Article
- Google Scholar
35. Goerner SJ, Lietaer B, Ulanowicz , Robert E. Quantifying economic sustainability: Implications for free-enterprise theory, policy and practice. Ecol Econ 2009;69: 76–81.
- View Article
- Google Scholar
36. Barkema A, Drabentstott M, Novack N. The new U.S. meat industry. Federal Reserve Bank of Kansas City. Econ Rev. 2001(2nd Q); 33–56.
- View Article
- Google Scholar
37. Chavas JP. Structural change in agricultural production. In: editors Gardner B.L. and Rausser G.C., editors. Handbook of agricultural economics 1A; 2001. p. 263–285
38. Dimitri C, Effland A, Conklin N. The 20th century transformation of U.S. agriculture and farm policy. EIB 3. USDA, Washington, DC; 2005.
39. Gressel J. Genetic glass ceilings: Transgenics for crop biodiversity. Johns Hopkins Univ. Press, Baltimore;2007.
40. Hendrickson M, Burt R, Chataway J, Cotter J, Darcy-Vrillon B, Debailleul G et al. Changes in agriculture and food production in NAE since 1945. In: McIntyre BD et al, editors. Agriculture at a crossroads: IAASTD International Assessment of Agricultural Knowledge, Science and Technology for Development, North America and Europe (NAE) report. Washington DC. IAASTD 2009;20–78. http://www.agassessmentwatch.org/report/North%20America%20and%20Europe_Subglobal%20Report%20(English).pdf
41. Medley KE, Okey BW, Barrett GW, Lucas MF, Renwick WH. Landscape change with agricultural intensification in a rural watershed, southwestern Ohio, USA. Landscape Ecol.1995;10: 161–176.
- View Article
- Google Scholar
42. Pirog R, van Pelt T, Enshayan K, Cook E. Food, fuel and freeways. Leopold Center Sustainable Agriculture, Iowa State University, Ames, USA; 2001.
43. Tilman D, Reich PB, Knops JMH. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 2006; 441: 629–632. pmid:16738658
- View Article
- PubMed/NCBI
- Google Scholar
44. Lin BB. Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change. BioSci. 2011; 61(3):183–193.
- View Article
- Google Scholar
45. Abson D J, Fraser EDG, Benton TG. Landscape diversity and the resilience of agricultural returns: a portfolio analysis of landuse patterns and economic returns from lowland agriculture. Agric & Food Sec. 2013;2: 2.
- View Article
- Google Scholar
46. Schippers P, van der Heide CM, Koelewijn HP, Schouten MAH, Smulders RMJM, Cobben MMB. Landscape diversity enhances the resilience of populations, ecosystems and local economy in rural areas. Landscape Ecol;2014.
- View Article
- Google Scholar
47. Tilman D, Polasky S, Lehman C. Diversity, productivity and temporal stability in the economies of humans and nature. J Environ Econ Managem. 2005; 49: 405–426.
- View Article
- Google Scholar
48. Polasky S, Nelson E, Camm J, Csuti B, Fackler P, Lonsdorf E et al. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol Conserv. 2008;141: 1505–1524.
- View Article
- Google Scholar
49. Mouysset L, Doyen L, Jiguet F. How does the economic risk aversion affect biodiversity? Ecological Applic. 2013;23: 96–109. http://dx.doi.org/10.1890/11-1887.1
- View Article
- Google Scholar
50. Bateman IJ, Harwood AR, Mace GM, Watson RT, Abson DJ, Andrews B et al. Bringing Ecosystem Services into Economic Decision-Making: Land Use in the United Kingdom. Sci 2013 Jul 5;341: 45–50.
- View Article
- Google Scholar
51. Korhonen J, Snäkin J.-P. Quantifying the relationship of resilience and eco-efficiency in complex adaptive energy systems. Ecol Econ. 2005;120: 83–92.
- View Article
- Google Scholar
52. Granberg L. Valtio maataloustulojen tasaajana ja takaajana. Bidrag till kännedom av Finlands natur och folk. Societas Scientiarum Fennica, Helsinki;1989: 138.
53. Lehtonen H, Pyykkönen P. Structural Change in Finnish Agriculture up to 2013. MTT:n selvityksiä 100. (In Finnish, Abstract in English). MTT Agrifood Research Finland. 2005; 40 p.
54. COM. Definitions of variables used in FADN standard results. Community committee for the Farm Accountancy Data Network (FADN). Directorate-General for Agriculture and rural development. Directorate G. Economic analyses and evaluation G.3. Analysis of the situation of agricultural holdings. 2007; RI/CC 882 Rev.8.1. http://ec.europa.eu/agriculture/rica/diffusion_en.cfm#publications RI/CC 882
55. Shannon CE, Weaver W. The Mathematical Theory of Communication. University of Illinois, Illinois, USA;1949.
56. Jost L. Partitioning diversity into independent alpha and beta components. Ecol. 2007; 88: 2427–2439.
- View Article
- Google Scholar
57. Aigner DJ, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production functions. J Econometrics 1977;6: 21–37.
- View Article
- Google Scholar
58. Cobb CW, Douglas PH. A Theory of production. Am Econ Rev. 1928:18: 139–65.
- View Article
- Google Scholar
59. Farrell MJ. The measurement of productive efficiency. J Royal Stat Soc Ser A. General. 1957; 120: 253–282.
- View Article
- Google Scholar
60. Kuosmanen N. Assessing the effect of land-use diversity on farms’ productive efficiency. Working Paper. MTT Agrifood Research Finland;2010. http://www.nomepre.net/index.php/papers
61. Neumann K. The yield gap of global grain production: A spatial analysis. Agricultural Systems 2010;103: 316–326.
- View Article
- Google Scholar
62. Coelli T., Rao P., O'Donnell C.J., Battese G.E., 2005. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Norwell, Massachusetts.
63. Ollinger M, MacDonald J, Madison M. Structural changes in U.S. chicken and turkey slaughter. In: Poultry Demand Change, AER/787, Washington, DC; USDA Economic Research Service; 2001.
64. Fletcher C, Hilbert D. Resilience in landscape exploitation systems. Ecol Modelling 2007;201: 440–452.
- View Article
- Google Scholar
65. Lin BB, Petersen B. Resilience, regime shifts, and guided transition under climate change: examining the practical difficulties of managing continually changing systems. Ecol Soc. 2013;18(1): 28. http://dx.doi.org/10.5751/ES-05128-180128
- View Article
- Google Scholar
66. Power AG. Ecosystem services and agriculture: tradeoffs and synergies. Philosophical Transactions of the Royal Society, Biol Sci. 2010;265: 2959–2971.
- View Article
- Google Scholar
67. Tichit M, Puillet L, Sabatier R, Teillard F. Multicriteria performance and sustainability in livestock farming systems: Functional diversity matters. Livestock Sci. 2011;139: 161–171.
- View Article
- Google Scholar
68. MA. Millennium Ecosystem Assessment Synthesis Report. A report of the Millennium Ecosystem Assessment; 2005.
69. Anderies JM, Walker BH, Kinzig AP. Fifteen weddings and a funeral: Case studies and resilience-based management. Ecol Soc. 2006;11(1): 21.
- View Article
- Google Scholar
70. Ellis F. Rural Livelihoods and Diversity in Developing Countries. Oxford University Press, Oxford; 2000.
71. Perrings C. Resilience in the dynamics of economic environmental systems. Environ Resource Econ. 1998;11(3–4): 503–520.
- View Article
- Google Scholar
72. Hakala K, Jauhiainen L, Himanen SJ, Rötter R, Salo T, Kahiluoto H. Sensitivity of barley varieties to weather in Finland. J Agric Sci 2012;150: 145–160. pmid:22505777
- View Article
- PubMed/NCBI
- Google Scholar
73. Reidsma P, Ewert F. Regional farm diversity can reduce vulnerability of food production to climate change. Ecol Soc. 2008;13: 38.
- View Article
- Google Scholar
74. Eakin H. Institutional change, climate risk, and rural vulnerability: cases from Central Mexico. World Development 2005;33: 1923–1938.
- View Article
- Google Scholar
75. Eriksen S, Brown K, Kelly PM. The dynamics of vulnerability: locating coping strategies in Kenya and Tanzania. Geograph J. 2005;171: 287–305.
- View Article
- Google Scholar
76. Korhonen J. Theory of Industrial Ecology—The Case of the Concept of Diversity. Progress in Industrial Ecol. 2005;2(1): 35–72.
- View Article
- Google Scholar
77. Dessai S, O’Brien K, Hulme M. Editorial: On uncertainty and climate change. Global Environ Change 2007;17: 1–3.
- View Article
- Google Scholar
78. McNamara KT, Weiss C. Farm household income and on- and off-farm diversification. J Agric Appl Econ. 2005;37: 37–48.
- View Article
- Google Scholar

[ref1] 1. Regan HM, Ben-Haim Y, Langford B, Wilson WG, Lundberg P, Andelman SJ et al. Robust decision-making under severe uncertainty for conservation management. Ecol Applic. 2005;15(4): 1471–77.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Anderies JM, Folke C, Walker B, Ostrom E. Aligning Key Concepts for Global Change Policy: Robustness, Resilience, and Sustainability. Ecol Soc. 2013;18(2): 8.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Dessai S, Hulme M, Lempert R, Pielke R Jr. Climate prediction: a limit to adaptation? In: Adger WN, Lorenzoni I, O’Brien K, editors. Adapting to Climate Change: Thresholds, Values, Governance. Cambridge University Press, Cambridge; 2009. pp. 64–78.

[ref4] 4. Gunderson L, Holling CS, Pritchard L, Peterson GD. Resilience. Vol. 2, The Earth system: biological and ecological dimensions of global environmental change, In: Mooney HA, Canadell JG, editors. Encyclopedia of Global Environmental Change 2002: 530–1.

[ref5] 5. OSU (The Ohio State University). Toward Sustainable Systems. Results of an Interdisciplinary Symposium, organized by the Center for Resilience at the Ohio State University. 2006;13.

[ref6] 6. Walker B, Holling CS, Carpenter SR, Kinzig A. Resilience, adaptability and transformability in social-ecological systems. Ecol Soc. 2004;9(2): 5.
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref7] 7. Carpenter SR, Walker BH, Anderies JM, Abel N. From metaphor to measurement: resilience of what to what? Ecosys. 2001;4: 765–781.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref8] 8. Folke C, Carpenter S, Elmqvist T, Gunderson L, Holling CS, Walker B. Resilience and sustainable development: Building adaptive capacity in a world of transformations. Ambio 2002;31: 437–440. pmid:12374053
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref9] 9. Folke C, Carpenter SR, Walker B, Scheffer M, Chapin T, Rockström. Resilience thinking: integrating resilience, adaptability and transformability. Ecol Soc 2010;15(4): 20. [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art20/
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref10] 10. BMVBS. National strategies of European countries for climate change adaptation: A review from a spatial planning and territorial development perspective. Published by Federal Ministry of Transport, Building and Urban Development (BMVBS). Scientific Support Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) within Federal Office for Building and Regional Planning (BBR). Online-Publikation 2010;No. 21.

[ref11] 11. COM. Adapting to climate change: Towards a European framework for action. Brussels, White paper. 2009; 01472009:April 1.

[ref12] 12. OECD. International Futures Project on Future Global Shocks “Four Faces of Tomorrow” Report prepared by: John Casti IIASA, Laxenburg and The Kenos Circle, Vienna IFP/WKP/FGS(2011) 6; 2011.

[ref13] 13. Sun G-Q, Wang S-L, Ren q, Jin Z, Wu Y-P. Effects of time delay and space on herbivore dynamics: linking inducible defenses of plants to herbivore outbreak. Sci. Rep. 2015; 5:11246. pmid:26084812
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref14] 14. Sun G-Q, Zhang J, Song L-P, Jin Z, Li B-L, Pattern formation of a spatial predator–prey system. Applied Mathematics and Computation 2012; 218:11151–11162.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref15] 15. Sun G-Q. Pattern formation of an epidemic model with diffusion. Nonlinear Dyn 2012; 69:1097–1104.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref16] 16. Li L, Jin Z. Pattern dynamics of a spatial predator–prey model with noise Nonlinear Dyn 2012; 67:1737–1744.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref17] 17. Himanen SJ, Ketoja E, Hakala K, Rötter R, Salo T, Kahiluoto H. Cultivar diversity has great potential to increase yield of feed barley. Agron Sust Dev.2013;33(3): 519–530.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref18] 18. Kahiluoto H, Kaseva J, Hakala K, Himanen SJ, Jauhiainen L, Rötter RP et al. Cultivating resilience by empirically revealing response diversity. Global Environ Change 2014:25: 186–193.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref19] 19. Mäkinen H, Kaseva J, Virkajärvi P, Kahiluoto H. Managing resilience of forage crops to climate change through response diversity. Field Crops Research 2015;183: 23–30.
View Article
Google Scholar

[46] View Article

[47] Google Scholar

[ref20] 20. Nelson DR, Adger WN, Brown K. Adaptation to Environmental Change: Contributions of a Resilience Framework. Ann Rev Environ Resources 2007;32:395–419.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref21] 21. Geels FW. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy 2002; 31:1257–1274.
View Article
Google Scholar

[52] View Article

[53] Google Scholar

[ref22] 22. Chapin FS, Walker BH, Hobbs RJ, Hooper DU, Lawton JH, Sala OE et al. 1997. Biotic control over the functioning of ecosystems. Sci 1997; 277:500–504.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref23] 23. Elmqvist T, Folke C, Nyström M, Peterson G, Bengtsson J, Walker B, Norberg J. Response diversity, ecosystem change, and resilience. Front Ecol Environ. 2003;1(9):488–494
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref24] 24. Folke C, Carpenter SR, Walker BH, Scheffer M, Elmqvist T, Gunderson LH et al. Regime shifts, resilience and biodiversity in ecosystem management. Annual Rev Ecol, Evol Systematics 2004;35: 557–581.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref25] 25. Folke C. Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environ Change 2006;16: 253–267.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref26] 26. Levin SA. Ecosystems and the biosphere as complex adaptive systems. Ecosys. 1998;1:431–436.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref27] 27. Folke C, Holling CS, Perrings C. Biological diversity, ecosystems and the human scale. Ecol Appl. 1996;6: 1018–1024.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref28] 28. Lazear E. Economic imperialism. Quart J Econ. 2000;115(1): 99–146.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref29] 29. Hoffren J, Korhonen J. Editorial: Eco-efficiency is important when it is strategic. Progress Industrial Ecol. 2007; 4:1–18.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref30] 30. Robèrt K-H, Schmidt-Bleek B, Aloise de Larderel J, Basile G, Jansen JL, Kuehr R et al. Strategic sustainable development–selection, design and synergies of applied tools. J Cleaner Prod. 2002; 10:197–214.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref31] 31. Lankoski JE, Ollikainen M. Counterfactual approach for assessing agri-environmental policy: The case of the Finnish water protection policy, 122nd Seminar, European Association of Agricultural Economists, 17–18 February 2011, Ancona, Italy; 2011.

[ref32] 32. Tilman D, Fargione J, Wolff B, D'Antonio C, Dobson A, Howarth R et al. Forecasting agriculturally driven global environmental change. Sci. 2001;292: 281–284.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref33] 33. Korhonen J, Seager T. Beyond eco-efficiency: A resilience perspective. Business Strategy and the Environ. 2008;17: 411–419.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref34] 34. Ulanowicz RE, Goerner SJ, Lietaer B, Gomez R. Quantifying sustainability: Resilience, efficiency and the return of information theory. Ecol Complexity 2009;6: 27–36.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref35] 35. Goerner SJ, Lietaer B, Ulanowicz , Robert E. Quantifying economic sustainability: Implications for free-enterprise theory, policy and practice. Ecol Econ 2009;69: 76–81.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref36] 36. Barkema A, Drabentstott M, Novack N. The new U.S. meat industry. Federal Reserve Bank of Kansas City. Econ Rev. 2001(2nd Q); 33–56.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref37] 37. Chavas JP. Structural change in agricultural production. In: editors Gardner B.L. and Rausser G.C., editors. Handbook of agricultural economics 1A; 2001. p. 263–285

[ref38] 38. Dimitri C, Effland A, Conklin N. The 20th century transformation of U.S. agriculture and farm policy. EIB 3. USDA, Washington, DC; 2005.

[ref39] 39. Gressel J. Genetic glass ceilings: Transgenics for crop biodiversity. Johns Hopkins Univ. Press, Baltimore;2007.

[ref40] 40. Hendrickson M, Burt R, Chataway J, Cotter J, Darcy-Vrillon B, Debailleul G et al. Changes in agriculture and food production in NAE since 1945. In: McIntyre BD et al, editors. Agriculture at a crossroads: IAASTD International Assessment of Agricultural Knowledge, Science and Technology for Development, North America and Europe (NAE) report. Washington DC. IAASTD 2009;20–78. http://www.agassessmentwatch.org/report/North%20America%20and%20Europe_Subglobal%20Report%20(English).pdf

[ref41] 41. Medley KE, Okey BW, Barrett GW, Lucas MF, Renwick WH. Landscape change with agricultural intensification in a rural watershed, southwestern Ohio, USA. Landscape Ecol.1995;10: 161–176.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref42] 42. Pirog R, van Pelt T, Enshayan K, Cook E. Food, fuel and freeways. Leopold Center Sustainable Agriculture, Iowa State University, Ames, USA; 2001.

[ref43] 43. Tilman D, Reich PB, Knops JMH. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 2006; 441: 629–632. pmid:16738658
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref44] 44. Lin BB. Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change. BioSci. 2011; 61(3):183–193.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref45] 45. Abson D J, Fraser EDG, Benton TG. Landscape diversity and the resilience of agricultural returns: a portfolio analysis of landuse patterns and economic returns from lowland agriculture. Agric & Food Sec. 2013;2: 2.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref46] 46. Schippers P, van der Heide CM, Koelewijn HP, Schouten MAH, Smulders RMJM, Cobben MMB. Landscape diversity enhances the resilience of populations, ecosystems and local economy in rural areas. Landscape Ecol;2014.
View Article
Google Scholar

[116] View Article

[117] Google Scholar

[ref47] 47. Tilman D, Polasky S, Lehman C. Diversity, productivity and temporal stability in the economies of humans and nature. J Environ Econ Managem. 2005; 49: 405–426.
View Article
Google Scholar

[119] View Article

[120] Google Scholar

[ref48] 48. Polasky S, Nelson E, Camm J, Csuti B, Fackler P, Lonsdorf E et al. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol Conserv. 2008;141: 1505–1524.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref49] 49. Mouysset L, Doyen L, Jiguet F. How does the economic risk aversion affect biodiversity? Ecological Applic. 2013;23: 96–109. http://dx.doi.org/10.1890/11-1887.1
View Article
Google Scholar

[125] View Article

[126] Google Scholar

[ref50] 50. Bateman IJ, Harwood AR, Mace GM, Watson RT, Abson DJ, Andrews B et al. Bringing Ecosystem Services into Economic Decision-Making: Land Use in the United Kingdom. Sci 2013 Jul 5;341: 45–50.
View Article
Google Scholar

[128] View Article

[129] Google Scholar

[ref51] 51. Korhonen J, Snäkin J.-P. Quantifying the relationship of resilience and eco-efficiency in complex adaptive energy systems. Ecol Econ. 2005;120: 83–92.
View Article
Google Scholar

[131] View Article

[132] Google Scholar

[ref52] 52. Granberg L. Valtio maataloustulojen tasaajana ja takaajana. Bidrag till kännedom av Finlands natur och folk. Societas Scientiarum Fennica, Helsinki;1989: 138.

[ref53] 53. Lehtonen H, Pyykkönen P. Structural Change in Finnish Agriculture up to 2013. MTT:n selvityksiä 100. (In Finnish, Abstract in English). MTT Agrifood Research Finland. 2005; 40 p.

[ref54] 54. COM. Definitions of variables used in FADN standard results. Community committee for the Farm Accountancy Data Network (FADN). Directorate-General for Agriculture and rural development. Directorate G. Economic analyses and evaluation G.3. Analysis of the situation of agricultural holdings. 2007; RI/CC 882 Rev.8.1. http://ec.europa.eu/agriculture/rica/diffusion_en.cfm#publications RI/CC 882

[ref55] 55. Shannon CE, Weaver W. The Mathematical Theory of Communication. University of Illinois, Illinois, USA;1949.

[ref56] 56. Jost L. Partitioning diversity into independent alpha and beta components. Ecol. 2007; 88: 2427–2439.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref57] 57. Aigner DJ, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production functions. J Econometrics 1977;6: 21–37.
View Article
Google Scholar

[141] View Article

[142] Google Scholar

[ref58] 58. Cobb CW, Douglas PH. A Theory of production. Am Econ Rev. 1928:18: 139–65.
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref59] 59. Farrell MJ. The measurement of productive efficiency. J Royal Stat Soc Ser A. General. 1957; 120: 253–282.
View Article
Google Scholar

[147] View Article

[148] Google Scholar

[ref60] 60. Kuosmanen N. Assessing the effect of land-use diversity on farms’ productive efficiency. Working Paper. MTT Agrifood Research Finland;2010. http://www.nomepre.net/index.php/papers

[ref61] 61. Neumann K. The yield gap of global grain production: A spatial analysis. Agricultural Systems 2010;103: 316–326.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref62] 62. Coelli T., Rao P., O'Donnell C.J., Battese G.E., 2005. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Norwell, Massachusetts.

[ref63] 63. Ollinger M, MacDonald J, Madison M. Structural changes in U.S. chicken and turkey slaughter. In: Poultry Demand Change, AER/787, Washington, DC; USDA Economic Research Service; 2001.

[ref64] 64. Fletcher C, Hilbert D. Resilience in landscape exploitation systems. Ecol Modelling 2007;201: 440–452.
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref65] 65. Lin BB, Petersen B. Resilience, regime shifts, and guided transition under climate change: examining the practical difficulties of managing continually changing systems. Ecol Soc. 2013;18(1): 28. http://dx.doi.org/10.5751/ES-05128-180128
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref66] 66. Power AG. Ecosystem services and agriculture: tradeoffs and synergies. Philosophical Transactions of the Royal Society, Biol Sci. 2010;265: 2959–2971.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref67] 67. Tichit M, Puillet L, Sabatier R, Teillard F. Multicriteria performance and sustainability in livestock farming systems: Functional diversity matters. Livestock Sci. 2011;139: 161–171.
View Article
Google Scholar

[165] View Article

[166] Google Scholar

[ref68] 68. MA. Millennium Ecosystem Assessment Synthesis Report. A report of the Millennium Ecosystem Assessment; 2005.

[ref69] 69. Anderies JM, Walker BH, Kinzig AP. Fifteen weddings and a funeral: Case studies and resilience-based management. Ecol Soc. 2006;11(1): 21.
View Article
Google Scholar

[169] View Article

[170] Google Scholar

[ref70] 70. Ellis F. Rural Livelihoods and Diversity in Developing Countries. Oxford University Press, Oxford; 2000.

[ref71] 71. Perrings C. Resilience in the dynamics of economic environmental systems. Environ Resource Econ. 1998;11(3–4): 503–520.
View Article
Google Scholar

[173] View Article

[174] Google Scholar

[ref72] 72. Hakala K, Jauhiainen L, Himanen SJ, Rötter R, Salo T, Kahiluoto H. Sensitivity of barley varieties to weather in Finland. J Agric Sci 2012;150: 145–160. pmid:22505777
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref73] 73. Reidsma P, Ewert F. Regional farm diversity can reduce vulnerability of food production to climate change. Ecol Soc. 2008;13: 38.
View Article
Google Scholar

[180] View Article

[181] Google Scholar

[ref74] 74. Eakin H. Institutional change, climate risk, and rural vulnerability: cases from Central Mexico. World Development 2005;33: 1923–1938.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref75] 75. Eriksen S, Brown K, Kelly PM. The dynamics of vulnerability: locating coping strategies in Kenya and Tanzania. Geograph J. 2005;171: 287–305.
View Article
Google Scholar

[186] View Article

[187] Google Scholar

[ref76] 76. Korhonen J. Theory of Industrial Ecology—The Case of the Concept of Diversity. Progress in Industrial Ecol. 2005;2(1): 35–72.
View Article
Google Scholar

[189] View Article

[190] Google Scholar

[ref77] 77. Dessai S, O’Brien K, Hulme M. Editorial: On uncertainty and climate change. Global Environ Change 2007;17: 1–3.
View Article
Google Scholar

[192] View Article

[193] Google Scholar

[ref78] 78. McNamara KT, Weiss C. Farm household income and on- and off-farm diversification. J Agric Appl Econ. 2005;37: 37–48.
View Article
Google Scholar

[195] View Article

[196] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Farm data

Land-use diversity

Resource-use efficiency

The Cobb-Douglas regression model

The stochastic frontier production models

Model comparisons

Results

Land-use diversity in relation to farm inputs and total revenue

Land-use diversity in relation to farm resource-use efficiency

Cobb-Douglas regression model.

Stochastic frontier Cobb-Douglas production model.

Stochastic frontier translog production model.

Discussion

Dependence of farm productivity on diversity

Reliability and generality of the findings

Conclusions

Acknowledgments

Author Contributions

References