A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops

Sustainable alternative to irrigated maize monoculture in a maize-dominated cropped area: Lessons learned from a system experiment

Maize is the most-produced food crop in the world and is grown in intensive rotations or in monoculture (continuous maize) systems. As maize production has expanded massively across the world, many concerns have emerged about its local environmental and other global impacts. Agronomists have designed innovative cropping systems and assessed them using system experiments to make arable systems more sustainable. However, knowledge is still lacking on the sustainability of innovative cropping systems compared to highly intensive systems such as irrigated maize-based monoculture. Here, we present the assessment results of a nine-year system experiment in Alsace, France, developed to compare an innovative system based on a diversified rotation and innovative management practices (three-year rotation of maize/soybean/winter wheat (plus a cover crop) combined with reduced tillage) with a continuous maize reference system. The results cover a six-year assessment period following an initial three-year design period. Classic criteria, such as profitability, workload, pesticide use, fossil energy consumption and nitrate leaching, were assessed along with other less studied criteria, such as pesticide leaching risk, soil structure, soil chemical quality and soil biological activity. Sustainability - which includes environmental, social and economic dimensions - was assessed with the MASC 2.0 method. Overall sustainability was substantially enhanced in the innovative system (5 out of 7 sustainability classes) in comparison with the low level of the reference system (2 out of 7). This was due to a clear improvement in the environmental performance (from 2 out of 5 to 5 out of 5) while social performance was high in both systems (4 out of 5) and economic performance was low (2 out of 5) due to very low contribution to economic development. Nevertheless, the innovative system had a major drawback: lower profitability, especially when scenarios included high maize prices. Furthermore, herbicide use on maize was higher in the innovative system than in the reference one. Avenues for progress, such as encouraging stakeholder participation at the assessment stage or additional innovations such as multiple cropping, are suggested.

DEXiAqua, a Model to Assess the Sustainability of Aquaculture Systems: Methodological Development and Application to a French Salmon Farm

Aquaculture is increasingly considered a major contributor to the growing demand for worldwide seafood production. Sustainability is becoming a key issue for aquaculture systems, with the objective to produce seafood with lower environmental impacts and that is economically viable and socially fair. In the context of the SIMTAP project, a multi-attribute model called DEXiAqua was developed. DEXiAqua uses the DEX method to assess the sustainability of aquaculture systems via indicators from technical domains and reference methods (i.e., life cycle assessment, life cycle costing, social life cycle assessment, and emergy accounting) selected and organized by the partners in the SIMTAP project. The DEX method consists of building an attribute tree that is organized to characterize a complex problem. Qualitative or quantitative indicators are measured at the end of each branch of the tree. The value of each indicator is translated into a qualitative scale for the associated attribute via threshold values. Weighted utility functions are used to build attributes from sub-attributes until the attribute of overall sustainability is reached. DEXiAqua was applied to a case study of salmon farming in France, which illustrated its ability to assess overall sustainability and help identify ways to improve the production system by identifying environmental, social, and economic hotspots. More case studies are required to apply DEXiAqua to a variety of systems with technical and contextual differences, which could result in changing attribute weights to adapt it better to different contexts.

Multicriteria Assessment of the Quality of Waste Sorting Centers—A Case Study

In this paper, the quality of the municipal waste sorting process in seven waste management centers in Slovenia was assessed using the qualitative multicriteria analysis (MCA) method DEX (Decision EXpert) implemented in DEXi software, which is based on multicriteria decomposition of the problem and utility functions in the form of “if–then” decision rules. The study was based on eight types of secondary raw materials. The quality of the secondary raw materials, the regularity of the delivery of secondary raw materials to recycling units based on the sorting efficiency, and the loading weight of the individual baled fractions in the transport of secondary raw materials for recycling were the main parameters used in the model. The final assessment shows “good” waste management service in centers A and D. Centers B, C, and F were rated “average”. The “bad” rating was assigned to centers E and G.

Organic Vegetable Crops Managed with Agro-Ecological Practices: Environmental Sustainability Assessment by DEXi-met Decision Support System

In the last decade, there has been an increasing interest in sustainable agricultural techniques and the environmental evaluation of the effects of agricultural practices. In the present study, we evaluated both the production capacity of organic horticultural systems, and the ex-post sustainability through a new multi-attribute decision model named “DEXi-met”. This qualitative model is able to estimate the environmental sustainability of cropping systems managed with different agro-ecological approaches. In particular, we compared the following three horticultural systems: (i) ECO, an organic system with full implementation of agro-ecological strategies (agro-ecological services crops (ASC), strip cultivation, and organic amendment); (ii) GM, an organic system with the introduction of the ASC; (iii) NO ASC, an organic system without ASC. The treatments with ASC presence (ECO and GM) showed similar total energy outputs (substantially higher than the NO ASC), indicating the positive effect of this agro-ecological practice. The findings pointed out that the ECO system, which followed the principles of natural ecosystems, can contribute to building up more complex agro-ecosystems, increasing both resilience and biodiversity. This management strategy reached a good compromise between the production of vegetable cropping systems and environmental sustainability achievement. Then, it is possible to optimize the use of natural resources, support climate adaptation, and reduce greenhouse gas emissions.

Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public repository managed by INRA (https://data.inra.fr/; https://doi.org/10.15454/WB51LL).

Sequential use of the STICS crop model and of the MACRO pesticide fate model to simulate pesticides leaching in cropping systems

The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems.

Monitoring and assessment of anthropogenic activities in mountain lakes: a case of the Fifth Triglav Lake in the Julian Alps

The Fifth Triglav Lake is a remote mountain lake in the Julian Alps. The area of the Julian Alps where the lake is situated is protected by law and lies within the Triglav National Park. Mountain lakes in Slovenia were considered for a long time as pristine, unpolluted lakes, but analyses in the last decade revealed considerable human impact even in such remote places. Eutrophication or excessive accumulation of nutrients is the main problem of most lakes in the temperate climatic zone, also in Slovenia. Since the introduction of fish in 1991, the lake is going through a series of changes for which we do not know exactly where they lead, so the monitoring and assessment of anthropogenic activities are of great importance. For this purpose, a qualitative multiattribute decision model was developed with DEX method to assess ecological effects on the lake. The extent of the ecological effects on the lake is assessed using four main parameters: the trophic state, lake characteristics, environmental parameters, and anthropogenic stressors. Dependence of environmental impact on various external factors beyond human control, such as temperature, precipitation, retention time, and factors on which we have influence, such as the amount of wastewater and the presence of fish in the lake, were also evaluated. The following data were measured: chlorophyll a, nutrients, TP, oxygen, C/N ratio, nutrients in sediment, temperature, precipitation, retention time, and volume. We made assumptions about fish and wastewater, which we could not measure. The main contributions of this work are the designed model and the obtained findings for the Fifth Triglav Lake that can help not only scientists in understanding the complexity of lake-watershed systems and interactions among system components but also local authorities to manage and monitor the lake aquatic environment in an effective and efficient way. The model is flexible and can be also used for other lakes, assuming that the used parameters are measured and anthropogenic stressors are adjusted to a specific situation. The results of assessment are of particular interest for decision makers in protected areas, providing a new approach to the management of the quality of the water environment.

A novel two-stage evaluation system based on a Group-G1 approach to identify appropriate emergency treatment technology schemes in sudden water source pollution accidents

Sudden water source pollution resulting from hazardous materials has gradually become a major threat to the safety of the urban water supply. Over the past years, various treatment techniques have been proposed for the removal of the pollutants to minimize the threat of such pollutions. Given the diversity of techniques available, the current challenge is how to scientifically select the most desirable alternative for different threat degrees. Therefore, a novel two-stage evaluation system was developed based on a circulation-correction improved Group-G1 method to determine the optimal emergency treatment technology scheme, considering the areas of contaminant elimination in both drinking water sources and water treatment plants. In stage 1, the threat degree caused by the pollution was predicted using a threat evaluation index system and was subdivided into four levels. Then, a technique evaluation index system containing four sets of criteria weights was constructed in stage 2 to obtain the optimum treatment schemes corresponding to the different threat levels. The applicability of the established evaluation system was tested by a practical cadmium-contaminated accident that occurred in 2012. The results show this system capable of facilitating scientific analysis in the evaluation and selection of emergency treatment technologies for drinking water source security.

The impact of secondary pests on Bacillus thuringiensis (Bt) crops

The intensification of agriculture and the development of synthetic insecticides enabled worldwide grain production to more than double in the last third of the 20th century. However, the heavy dependence and, in some cases, overuse of insecticides has been responsible for negative environmental and ecological impacts across the globe, such as a reduction in biodiversity, insect resistance to insecticides, negative effects on nontarget species (e.g. natural enemies) and the development of secondary pests. The use of recombinant DNA technology to develop genetically engineered insect-resistant crops could mitigate many of the negative side effects of insecticides. One such genetic alteration enables crops to express toxic crystalline (Cry) proteins from the soil bacteria Bacillus thuringiensis (Bt). Despite the widespread adoption of Bt crops, there are still a range of unanswered questions concerning longer term agro-ecosystem interactions. For instance, insect species that are not susceptible to the expressed toxin can develop into secondary pests and cause significant damage to the crop. Here, we review the main causes surrounding secondary pest dynamics in Bt crops and the impact of such outbreaks. Regardless of the causes, if nonsusceptible secondary pest populations exceed economic thresholds, insecticide spraying could become the immediate solution at farmers' disposal, and the sustainable use of this genetic modification technology may be in jeopardy. Based on the literature, recommendations for future research are outlined that will help to improve the knowledge of the possible long-term ecological trophic interactions of employing this technology.

Construction of a technique plan repository and evaluation system based on AHP group decision-making for emergency treatment and disposal in chemical pollution accidents

The environmental pollution resulting from chemical accidents has caused increasingly serious concerns. Therefore, it is very important to be able to determine in advance the appropriate emergency treatment and disposal technology for different types of chemical accidents. However, the formulation of an emergency plan for chemical pollution accidents is considerably difficult due to the substantial uncertainty and complexity of such accidents. This paper explains how the event tree method was used to create 54 different scenarios for chemical pollution accidents, based on the polluted medium, dangerous characteristics and properties of chemicals involved. For each type of chemical accident, feasible emergency treatment and disposal technology schemes were established, considering the areas of pollution source control, pollutant non-proliferation, contaminant elimination and waste disposal. Meanwhile, in order to obtain the optimum emergency disposal technology schemes as soon as the chemical pollution accident occurs from the plan repository, the technique evaluation index system was developed based on group decision-improved analytical hierarchy process (AHP), and has been tested by using a sudden aniline pollution accident that occurred in a river in December 2012.

Eliciting and Combining Decision Criteria Using a Limited Palette of Utility Functions and Uncertainty Distributions: Illustrated by Application to Pest Risk Analysis

Utility functions in the form of tables or matrices have often been used to combine discretely rated decision-making criteria. Matrix elements are usually specified individually, so no one rule or principle can be easily stated for the utility function as a whole. A series of five matrices are presented that aggregate criteria two at a time using simple rules that express a varying degree of constraint of the lower rating over the higher. A further nine possible matrices were obtained by using a different rule either side of the main axis of the matrix to describe situations where the criteria have a differential influence on the outcome. Uncertainties in the criteria are represented by three alternative frequency distributions from which the assessors select the most appropriate. The output of the utility function is a distribution of rating frequencies that is dependent on the distributions of the input criteria. In pest risk analysis (PRA), seven of these utility functions were required to mimic the logic by which assessors for the European and Mediterranean Plant Protection Organization arrive at an overall rating of pest risk. The framework enables the development of PRAs that are consistent and easy to understand, criticize, compare, and change. When tested in workshops, PRA practitioners thought that the approach accorded with both the logic and the level of resolution that they used in the risk assessments.

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

A Method for Ranking Non-Linear Qualitative Decision Preferences using Copulas

This paper addresses the problem of option ranking in qualitative evaluation models. Current approaches make the assumptions that when qualitative data are suitably mapped into discrete quantitative ones, they form monotone or closely linear tabular value functions. Although the power of using monotone and linear functions to model decision maker’s preferences is impressive, there are many cases when they fail to successfully model non-linear decision preferences. Therefore, the authors propose a new method for ranking discrete non-linear decision maker preferences based on copula functions. Copulas are functions that capture the non-linear dependences among random variables. Hence each attribute is considered as a random variable. The variables are nested into hierarchical copula structures to determine the non-linear dependences among all attributes at hand. The obtained copula structure is used for obtaining regression function and consequently for option ranking. The application of the method is presented on two examples.

Environmental change challenges decision-making during post-market environmental monitoring of transgenic crops

The ability to decide what kind of environmental changes observed during post-market environmental monitoring of genetically modified (GM) crops represent environmental harm is an essential part of most legal frameworks regulating the commercial release of GM crops into the environment. Among others, such decisions are necessary to initiate remedial measures or to sustain claims of redress linked to environmental liability. Given that consensus on criteria to evaluate 'environmental harm' has not yet been found, there are a number of challenges for risk managers when interpreting GM crop monitoring data for environmental decision-making. In the present paper, we argue that the challenges in decision-making have four main causes. The first three causes relate to scientific data collection and analysis, which have methodological limits. The forth cause concerns scientific data evaluation, which is controversial among the different stakeholders involved in the debate on potential impacts of GM crops on the environment. This results in controversy how the effects of GM crops should be valued and what constitutes environmental harm. This controversy may influence decision-making about triggering corrective actions by regulators. We analyse all four challenges and propose potential strategies for addressing them. We conclude that environmental monitoring has its limits in reducing uncertainties remaining from the environmental risk assessment prior to market approval. We argue that remaining uncertainties related to adverse environmental effects of GM crops would probably be assessed in a more efficient and rigorous way during pre-market risk assessment. Risk managers should acknowledge the limits of environmental monitoring programmes as a tool for decision-making.