Criteria and standards for sustainable agriculture

Eckert, Hans; Breitschuh, G.; Sauerbeck, Dieter

doi:10.1002/1522-2624(200008)163:4<337::aid-jpln337>3.0.co;2-3

Cited by 41 publications

(29 citation statements)

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“…In our opinion, it should result from the interaction among scientists, policy-makers, local stakeholders, and communities. A substantial number of researchers already adopted alternative sources of reference values to assess agricultural sustainability specifically, 'author appraisal' (Sands and Podmore 2000); 'expert appraisal' (van Calker et al 2006); 'expert interviews' (Eckert et al 2000); 'recommended values' (Bockstaller et al 1997); 'community averages' (Gomez et al 1996), which is also subjected to more reviews.…”

Section: Indicator Validationmentioning

confidence: 99%

An assessment of agricultural sustainability indicators in Bangladesh: review and synthesis

Roy

Chan

2011

Environmentalist

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The term 'indicator' is often vague and heterogeneous, and its dynamic characteristics make it highly variable over time and space. Based on reviews and synthesis, this study visualizes phenomena and highlights the trend of indicator selection criteria, development methods, validation evaluation strategies for improvement. In contextualization of the intensification of agriculture and climate change, we proposed a set of indicators for assessing agricultural sustainability in Bangladesh based on theoretically proposed and practically applied indicators by researchers. Also, this article raises several issues of indicator system development and presents a summary after due consideration. Finally, we underline multi-stakeholders' participation in agricultural sustainability assessment.

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Section: Indicator Validationmentioning

confidence: 99%

An assessment of agricultural sustainability indicators in Bangladesh: review and synthesis

Roy

Chan

2011

Environmentalist

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“…As long as these impacts are assimilated by the biophysical limits of the environment, the orchard system will function like a natural ecosystem with minimal environmental degradation (Adams 2006;Kim 2004). Hence, the environmental impacts arising from the energy and matter use by an orchard system have to be within the appropriate ranges of tolerability (Eckert et al 2000). Towards this end, four key indicators along with the threshold values are described.…”

Section: Energy Efficiencymentioning

confidence: 99%

Modelling sustainability: what are the factors that influence sustainability of organic fruit production systems in New Zealand?

Page

2011

Org. Agr.

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Although many believe that organic systems are sustainable as compared to conventional systems, there is a need to study the sustainability of organic systems per se. In this paper, sustainability of organic kiwifruit and apple production systems is modelled based on the concept of strong sustainability which gives utmost importance to environmental sustainability. Sustainability assessment is undertaken through analyses of key energy and material flows of the orchard system and their impacts on the environment. The proposed approach is based on two high level criteria for sustainability: energy efficiency and non-degradation of the environment. Five indicators which address the two criteria for sustainability are the energy ratio, carbon ratio, change in soil carbon level, soil nutrient balances and leaching of nitrogen. Sustainability indicators are estimated over one production year using two computer modelling tools, Overseer® and Stella®. Sustainability assessment indicates that the organic kiwifruit and apple systems are efficient in energy use and are a net carbon sink over a typical production year. The apple systems mined potassium from the soil which may be a threat to future yield and sustainability. Transition to sustainable farming systems implies reliance to a lesser extent on non-renewable energies and recognising the environmental bottom line.

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“…It seems that we tested the ability of a knife to cut a steel bar. However, they are used in many multicriteria assessment methods (Eckert et al, 2000;Gomiero and Giampietro, 2001;Häni et al, 2003) and by regulatory authorities to monitor the evolution of pesticide management (e.g. TFI for the Ecophyto plan).…”

Section: Discussionmentioning

confidence: 99%

“…The simplest ones rely on and take into account the amount of quantities supplied, the Quantity of Active Ingredients (QAI) or the Treatment Frequency Index (TFI) calculating the ratio of applied pesticide to the registered rate. Although those indicators have been developed to describe the evolution of pesticide use intensity, they are often used as main indicators to address the environmental effects due to pesticide spraying in environmental assessment method (Eckert et al, 2000;Vilain et al, 2008). Pesticide risk indicators (Levitan, 2000) addressing complementary variables such as active ingredient properties, crop management data and pedoclimatic variables are more elaborate and were reviewed by several authors (Maud et al, 2001;Reus et al, 2002;Feola et al, 2011;Keichinger et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predictive quality of 26 pesticide risk indicators and one flow model: A multisite assessment for water contamination

Pierlot

Marks-Perreau

Real

et al. 2017

Science of The Total Environment

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• Information on predictive quality of pesticide risk indicators is scarce • Outputs of 26 indicators and 1 model were compared to pesticide measurements in water • 3 comparison tests were performed for a dataset of 1040 measurements from 3 sites • Predictive quality was low to medium for the indicators and acceptable for the model • The model and indicators with medium predictive quality can be recommended for use Stakeholders need operational tools to assess crop protection strategies in regard to environmental impact. The need to assess and report on the impacts of pesticide use on the environment has led to the development of numerous indicators. However, only a few studies have addressed the predictive quality of these indicators. This is mainly due to the limited number of datasets adapted to the comparison of indicator outputs with pesticide measurement. To our knowledge, evaluation of the predictive quality of pesticide indicators in comparison to the quality of water as presented in this article is unprecedented in terms of the number of tested indicators (26 indicators and the MACRO model) and in terms of the size of datasets used (data collected for 4 transfer pathways, 20 active ingredients (a.i.) for a total of 1040 comparison points). Results obtained on a.i. measurements were compared to the indicator outputs, measured by: (i) correlation tests to identify linear relationship, (ii) probability tests comparing measurements with indicator outputs, both classified in 5 classes, and assessing the probability i.e. the percentage of correct estimation and overestimation (iii) by ROC tests estimating the predictive ability against a given threshold. Results showed that the correlation between indicator outputs and the observed transfers are low (r b 0.58). Overall, more complex indicators taking into account the soil, the climatic and the G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v environmental aspects yielded comparatively better results. The numerical simulation model MACRO showed much better results than those for indicators. These results will be used to help stakeholders to appropriately select their indicators, and will provide them with advice for possible use and limits in the interpretation of indicator outputs.

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Criteria and standards for sustainable agriculture

Cited by 41 publications

References 0 publications

An assessment of agricultural sustainability indicators in Bangladesh: review and synthesis

An assessment of agricultural sustainability indicators in Bangladesh: review and synthesis

Modelling sustainability: what are the factors that influence sustainability of organic fruit production systems in New Zealand?

Predictive quality of 26 pesticide risk indicators and one flow model: A multisite assessment for water contamination

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