Marc Benoit

Landscape agronomy: a new field for addressing agricultural landscape dynamics

Landscape dynamics increasingly challenge agronomists to explain how and why agricultural landscapes are designed and managed by farmers. Nevertheless, agronomy is rarely included in the wide range of disciplines involved in landscape research. In this paper, we describe how landscape agronomy can help explain the relationship between farming systems and agricultural landscape dynamics. For this, we propose a conceptual model of agricultural landscape dynamics that illustrates the specific contribution of agronomy to landscape research. This model describes the relationship between three elements: farming practices, landscape patterns and natural resources. It can stimulate agronomists to deal with research issues in agricultural landscape dynamics and enhance the interdisciplinary integration of farming systems in wider landscape research. On these premises, we discuss the main research issues that will benefit from an active involvement of agronomy, to understand, but also to assess landscape dynamics and to design relevant decision support systems.

Current management of farms and internal parasites by conventional and organic meat sheep French farmers and acceptance of targeted selective treatments.

Sheep meat production in France is characterized by large flocks and a limited supply of labour. Digestive-tract strongyles are considered as one of the main health problems and control relies mostly on the use of anthelminthics, although resistance to at least the benzimidazoles is increasing. We conducted interviews on nine conventional and seven organic farms regarding whether an anthelmintic targeted selective treatment program could fit within the operations of the farms. In addition, necropsies of lambs were performed on three organic farms, and faecal egg counts and small lungworm counts were performed on all farms in autumn in ewes. Each interview consisted of an open discussion on sheep health and was terminated with comments on digestive-tract helminth infection as detected in parallel with the interview. Factors likely to affect the adoption of the targeted selective treatment approach were subjected to cluster analysis. Conventional farms were mostly advised by veterinarians and relied on systematic planning of anthelmintic treatments. The frequency of treatments was up to once a month for lambs and two to three times a year for ewes. The concept of selecting animals to be treated according to a scheme of targeted selective treatments based on phenotypic markers (e.g., anaemia, diarrhoea, weight gains) was not seen as feasible by these farmers. Conversely, organic farmers, with greater use of advisors and a restricted range of anthelmintic treatments were more susceptible to integrating phenotypic markers into their practices for controlling digestive-tract strongyles.

Combining farmers’ decision rules and landscape stochastic regularities for landscape modelling

Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called “landscape agronomy”, aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.

Landscape regularity modelling for environmental challenges in agriculture

In agricultural landscapes, methods to identify and describe meaningful landscape patterns play an important role to understand the interaction between landscape organization and ecological processes. We propose an innovative stochastic modelling method of agricultural landscape organization where the temporal regularities in land-use are first identified through recognized Land-Use Successions before locating these successions in landscapes. These time–space regularities within landscapes are extracted using a new data mining method based on Hidden Markov Models. We applied this methodological proposal to the Niort Plain (West of France). We built a temporo-spatial analysis for this case study through spatially explicit analysis of Land-Use Succession dynamics. Implications and perspectives of such an approach, which links together the temporal and the spatial dimensions of the agricultural organization, are discussed by assessing the relationship between the agricultural landscape patterns defined using this approach and ecological data through an illustrative example of bird nests.

SPOT HRV data to discriminate grassland quality.

Abstract Information on grassland quality is required to assess the impact of agricultural policy in Europe. Field methods are time-consuming and local in scope, but may provide a basis from which to extrapolate using remotely sensed data. For thirty-eight fields in the Lorraine region of France floristic descriptions, agricultural observations, biomass, ground radiometric data and SPOT HRV data were collected during June, July and September 1986 and April 1987. Agronomical and phytosociological classifications of grassland management type and by inference grassland quality, yielded similar results but the phytosociological method was preferred as it could be quantified. Ground radiometric data and SPOT HRV data for all four seasons were used successfully to discriminate grassland quality but stratification by grassland quality did not increase the degree of correlation between remotely sensed data and vegetation amount. This research highlights the potential that exists for using SPOT HRV data, in conjun...

Mitigation of enteric methane for French cattle: potential extent and cost of selected actions

The purpose of this study was (i) to select options for enteric methane abatement in French conditions, while maintaining production and changing practices but not systems, and (ii) to evaluate the extent of abatement and its cost from 2010 to 2030. Two options were selected: supplying unsaturated fats, and using nitrates as a dietary additive in the diet. Unsaturated fats resulted in a higher abatement, because a greater number of animals were concerned by the option, but incurred a higher total cost and a higher cost per ton of CO2-equivalent avoided. The results were sensitive to methodology, and to variations in the choice of fats and in the cost of ingredients, among other factors. Given their cost, evaluated in the frame of a marginal abatement cost curve, these options will require strong public policies for their application.

Farming systems designing landscapes: land management units at the interface between agronomy and geography

Agriculture faces big challenges, such as feeding a growing population and providing an increasing amount of biomass for energy production. Land is, however, a limited resource and intensification of agricultural practices is deprecated because of the negative impacts on natural resources. Effective answers should therefore be fostered by the development of smarter spatial configurations of agricultural activities. The improvement of farming systems therefore requires agronomy to interact with geography and other disciplines that deal with spatially-explicit aspects of agricultural land management. Different research approaches have supported agronomy in the development of a landscape approach and in this paper we focus on the interactions with geography fostering the enhancement of a common language about the way farming practices are observed and understood by the two disciplines. For this purpose, we compare land management units, identified in recent agronomic literature, with the aim to facilitate future synergies of landscape-oriented research about farming system design. We conclude by arguing for the enhancement of the interface between agronomy and geography and discussing some perspectives on the use of the various land management units in the design of future farming systems with a landscape approach.

Do constructed wetlands in grass strips reduce water contamination from drained fields

This study evaluates the efficiency of two small constructed wetlands installed in the regulatory grass strips between a drained plot and a river. The observed nitrate removal efficiencies were independent of the season or type of constructed wetland and ranged from 5.4 to 10.9% of the inlet amounts. The pesticide mass budgets ranged from -618.5 to 100%, depending on the molecule. The negative efficiencies were attributed to runoff and remobilization. In contrast, the highest efficiencies were associated with pesticides with high Koc and low DT50 (half-life) values, suggesting sorption and degradation. However, the effectiveness of these wetlands is limited for pesticides with low Koc or high DT50 values; thus, the use of these molecules must be reduced. Increasing the number of these small, inexpensive and low-maintenance wetlands in the agricultural landscape would reduce the level of water pollution whilst preserving the extent of cultivated land, but their long-term effectiveness should be evaluated.

Time space stochastic modelling of agricultural landscapes for environmental issues

Since the initial point of Langran (1993) saying that Geographic Information Systems (GIS) were poorly equipped to handle temporal data, many researchers have sought to integrate the time dimension into GIS (Roddick et?al., 2001). We present a time space modelling approach - and a generic software named ARPEnTAge - capable of clustering a territory based on its pluri-annual land-use organization. By adding the ability to represent, locate and visualize temporal changes in the territory, ARPEnTAge provides tools to build a Time-Dominant GIS. One main Markovian assumption is stated: the land-use succession in a given place depends only on the land-use successions in neighbouring plots. By means of stochastic models such as a Hierarchical Hidden Markov Model and a Markov random field, ARPEnTAge performs an unsupervised clustering of a territory in order to reveal patches characterized by time space regularities in the land-use successions. Two case studies are developed involving two territories carrying environmental issues. Those territories have various sizes and are parameterized using long term surveys and/or remote sensing data. In both cases, ARPEnTAge detects, locates and displays in a GIS the temporal changes. This gives valuable information on the spatial and time dynamics of the land-use organization of those territories. Provide an unsupervised time space clustering software to mining land use dynamics.Provide a new way of estimating a class assignment in K-color Potts model based on the mean field paradigm.Implement a time dominant approach in a GIS.

Characterizing historical (1992–2010) transitions between grassland and cropland in mainland France through mining land-cover survey data

Grassland, as one of the largest ecosystems on the earth, supports various goods and services to humanity. Historically, humans have increased agricultural output primarily by cropland expansion and agricultural intensification. The cropland area was primarily gained at the expense of grassland and forests. Apart from grassland conversion, increasing consumption of calorie- and meat-intensive diets drives the intensification of livestock systems, which is shifting steadily from grazing to feeding with crops. To cope with the environmental degradation due to agriculture, various forms of ‘green payment’ were implemented to promote the adoption of sustainable farming practices over the last two decades in the European Union. The aim of this study is to monitor the recent transitions (1992–2010) between grassland and cropland during two Common Agricultural Policy (CAP) reforms at the French mainland scale. We proposed an innovative approach to link grassland conversion to agricultural commodities and farming systems practices. We first assessed the grassland-to-cropland conversion and further investigated the crop sequence patterns that were observed to be dominant after the conversion through mining land-cover survey data Teruti and Teruti-Lucas. We found the trends of the transitions between grassland and cropland over the two time intervals: The loss of grassland (1992–2003) and restoration or re-expansion of grassland (2006–2010) in mainland France. Our finding on the crop sequence patterns after the grassland conversion reveals two notable evolutions of agricultural production systems. These evolutions were related to the increase in the proportion of cropland in the total agricultural land use. One evolution was most likely influenced by the demand for fodder: The conversion from grazing livestock to feeding livestock. Another evolution was the conversion from livestock production to field crop production. Our results indicate that the intensification of livestock farming systems continued over the last two decades in France. We conclude that, the approach developed in this study can be considered as a generic method for monitoring the transitions between grassland and cropland and further identifying the crop sequence patterns after the grassland conversion from time-series land cover data.