A novel method for mapping agricultural intensity reveals its spatial aggregation: Implications for conservation policies

Teillard, Félix; Allaire, Gilles; Cahuzac, Eric; Léger, François; Maigné, Élise; Tichit, Muriel

doi:10.1016/j.agee.2011.12.018

Cited by 42 publications

(37 citation statements)

References 36 publications

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“…More precisely, our results indicated that, with similar investments, conservation outcomes were higher for an extensively managed farm. Allocating resources where they provide the highest environmental benefit is crucial to improve the costeffectiveness of conservation actions (Teillard et al, 2012). Our results are consistent with other studies suggesting that conservation actions yield higher biodiversity benefits in extensive farms or regions (Kleijn and Sutherland, 2003).…”

Section: Model Objectives and Limitationssupporting

confidence: 90%

Trade-offs between pasture production and farmland bird conservation: exploration of options using a dynamic farm model

Sabatier

Teillard

Rossing

et al. 2015

Animal

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In European grassland landscapes, grazing and mowing play a key role for the maintenance of high-quality habitats that host important bird populations. As grasslands are also key resources for cattle feeding, there is a need to develop management strategies that achieve the double objective of production and biodiversity conservation. The objective of this study was to use a modelling approach to generate recognisable patterns of bird dynamics in farms composed of different land use proportions, and to compare their production and ecological dimensions. We developed a dynamic model, which linked grassland management to bird population dynamics at the field and farm levels. The model was parameterised for two types of suckling farms corresponding to contrasting levels of grassland intensification and for two bird species of high conservation value. A viability algorithm was used to define and assess viable management strategies for production and ecological performance so as to draw the shape of the relationship between both types of performances for the two types of farms. Our results indicated that, at the farm level, there was a farming system effect with a negative and non-linear relationship linking performance. Improving bird population maintenance was less costly in extensive farms compared with intensive farms. At the field level, the model predicted the timing and intensity of land use, maximising either production or ecological performance. The results suggested that multi-objective grassland management would benefit from public policies that consider levels of organisation higher than the field level, such as the farm or the landscape.

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Section: Model Objectives and Limitationssupporting

confidence: 90%

Trade-offs between pasture production and farmland bird conservation: exploration of options using a dynamic farm model

Sabatier

Teillard

Rossing

et al. 2015

Animal

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“…Other European scale nitrogen datasets have been developed using comparable methods (e.g., Leip et al, 2011 for N flux budgets), but these are not publicly available. Alternative continuous mapping approaches, such as Teillard et al (2012) are limited to certain countries and in spatial resolution. In general, the different datasets used are not based on data from the same reference year, which could cause some mismatches in regions with rapid change.…”

Section: Evaluation Of Methodsmentioning

confidence: 99%

Representing composition, spatial structure and management intensity of European agricultural landscapes: A new typology

Zanden

Levers

Verburg

et al. 2016

Landscape and Urban Planning

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h i g h l i g h t s• We present a spatially-explicit typology of European agricultural landscapes.• Datasets representing land cover, landscape structure and land management are used.• An expert-based top-down typology is compared with a data-driven bottom-up approach.• Inclusion of land management differentiates our results from existing typologies.• We find clear overlaps in general landscape patterns with existing typologies. a r t i c l e i n f o b s t r a c tComprehensive maps that characterize the variation in agricultural landscapes across Europe are lacking. In this paper we present a new Europe-wide, spatially-explicit typology and inventory of the diversity in composition, spatial structure and management intensity of European agricultural landscapes. Agricultural landscape types were characterized at a 1 km 2 resolution based on Europe-wide datasets that represent land cover, landscape structure and land management intensity. Two alternative approaches for typology development were used: an expert-based top-down approach, and a bottom-up approach based on automated clustering using Self Organizing Maps (SOMs). Comparison with available national and European landscape typologies showed that our typology deviates from existing biophysical and anthropocentric typologies relevant to agricultural landscapes as result of the inclusion of land management aspects. Concordance occurred between specific European typology classes, while the comparison with national landscape typologies showed a correspondence in agricultural landscape patterns. Our agricultural landscape typology can provide a basis for landscape assessment at a European-scale to help to identify agricultural landscape types prone to change and landscapes that may require policy response.

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“…Farm Quantify agricultural inputs related Herzog et al, 2006;Reidsma et al, 2006;Geiger et al, 2010; to land productivity Armengot et al, 2011;Fumagalli et al, 2011;Nemecek et al, 2011;Dietrich et al, 2012;Teillard et al, 2012;Gaudino et al, 2014;Jepson et al, 2014LUI + multidiversity/biodiversity Flynn et al, 2009Kleijn et al, 2009;Snapp et al, 2010;Allan et al, 2014;Egorov et al, 2014 Global to local Land use changes Foley, 2005;Reidsma et al, 2006;Serra et al, 2008;Chen et al, 2009;Rudel et al, 2009;Yu et al, 2010;Temme and Verbug, 2011;Salvati and Tombolini, 2013;Kandziora et al, 2014 Regional Land use change model: CLUE model Verburg et al, 2002;Lima et al, 2011 Continental Scenarios of future agricultural land use Rounsevell et al, 2003;Rounsevell et al, 2005;Audsley et al, 2006 Global to local Model simulations + Crop yields; Lobell et al, 2009;Ribeiro et al, 2014 Modelling farming system dynamics Global to local Intensity analysis Aldwaik and Pontius, 2012;Huang et al, 2012 Global to local Changes in land use and changes in yields Ewers et al, 2009;Barretto et al, 2013 National Land use change + HANPP Erb, 2012;Erb, 2014 National HANPP Kraussmann et al, 2012 Local Urban expansion/population …”

Section: Scale Methodology Referencementioning

confidence: 99%

“…In general, agricultural intensity is defined as the ratio of inputs and outputs within an agricultural system, i.e., in terms of yield per land area and per input unit (Turner and Doolittle, 1978;Shriar, 2000Shriar, , 2005Herzog et al, 2006) or alternatively as the sum of different categories of input costs and the total usable agricultural area of the farm (Teillard et al, 2012). Therefore either output-oriented (production) or input-oriented (utilisation) measures can be used to describe agricultural intensity.…”

Section: Introductionmentioning

confidence: 99%

Indicators of agricultural intensity and intensification: a review of the literature

Ruiz-Martinez

Marraccini²,

Debolini

et al. 2015

Ital J Agronomy

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Since the 1960s, research has dealt with agricultural intensification (AI) as a solution to ensure global food security. Recently, sustainable intensification (SI) has increasingly been used to describe those agricultural and farming systems that ensure adequate ecosystem service provision. Studies differ in terms of the application scales and methodologies, thus we aim to summarize the main findings from the literature on how AI and SI are assessed, from the farm to global levels. Our literature review is based on 7865 papers selected from the Web of Science database and analysed using CorText software. A further selection of 105 relevant papers was used for an in-depth full-text analysis on: i) farming systems studied; ii) related ecosystem services; iii) indicators of intensity; and iv) temporal and spatial scales of analysis. Through this two-step analysis we were able to highlight three main research gaps in the AI research indicators. Firstly, the farming systems analysed for assessing AI are often quite simplified or monoculture-oriented, and they do not take the diversity and complex organisation of farming systems into account. Secondly, these studies mainly focus on northern countries or developing countries, whereas there is a gap of knowledge in Mediterranean areas, which are the areas with a high complexity of farming systems and diversity in ecosystem services. Finally, AI is mostly assessed through nitrogen inputs and economic yield, which are used the most both at very local and global levels. Intermediate regional or local levels, which are relevant for policy implementation and local planning, are often neglected.

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A novel method for mapping agricultural intensity reveals its spatial aggregation: Implications for conservation policies

Cited by 42 publications

References 36 publications

Trade-offs between pasture production and farmland bird conservation: exploration of options using a dynamic farm model

Trade-offs between pasture production and farmland bird conservation: exploration of options using a dynamic farm model

Representing composition, spatial structure and management intensity of European agricultural landscapes: A new typology

Indicators of agricultural intensity and intensification: a review of the literature

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