Next Article in Journal
Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops
Previous Article in Journal
Evolutionary Subdivision of Domestic Chickens: Implications for Local Breeds as Assessed by Phenotype and Genotype in Comparison to Commercial and Fancy Breeds
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 11
Issue 10
10.3390/agriculture11100915
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Generic Relationships between Field Uses and Their Geographical Characteristics in Mountain-Area Dairy Cattle Farms

by
Cécile Sibra
* and
Gilles Brunschwig
INRAE, VetAgro Sup, UMR Herbivores, Université Clermont Auvergne, 63122 Saint-Genès-Champanelle, France
*
Author to whom correspondence should be addressed.
Agriculture 2021, 11(10), 915; https://doi.org/10.3390/agriculture11100915
Submission received: 22 July 2021 / Revised: 6 September 2021 / Accepted: 21 September 2021 / Published: 24 September 2021
(This article belongs to the Section Farm Animal Production)
Browse Figures
Review Reports Versions Notes

Abstract

:
In mountain farms, challenges posed by the degree of land slope, altitude and harsh climate further compound multiple other possible constraints, particularly in relation to the distance of the farm from the farmstead. This study focused on how mountain-area dairy farmers factor the geographical characteristics of their fields into their field-use decisions. To that end, we surveyed 72 farmers who farm the traditional Salers breed of cattle and 28 specialised dairy system farmers in the central Massif region, France. Information was collected on the uses and geographical characteristics of all grassland fields (n = 2341) throughout the entire outdoor grazing season, without identifying farmers’ rationales for their field-use decisions. Field-use classes were constructed for the traditional Salers system per group of fields (grazed-only, cut-only, grazed-and-cut) and then used to classify fields in the specialized dairy system. The geographical characteristics, which were associated afterwards, were significantly different between the field groups and between field-use classes. Grazed-only fields were found to be more sloping and cut-only fields were smaller and further from the farmstead. Distance/area combinations were different according to field use (animal category, earliness of first cut, grazing and cutting sequence) and were decisive for all field-use classes. This study allowed the identification of generic relationships between field uses and their geographical characteristics in mountain-area dairy cattle farms.

1. Introduction

Field geography largely dictates the activity options and organisations adopted by livestock farmers, which means that field geography is key to the business of livestock farming. Different field geographies (which vary due to size, fragmentation, dispersion, altitude, slope, type of soil, and more) offer different sets of perspectives, and possibilities. The that way livestock farmers adapt to the specific features of their fields in order to accommodate their livestock activities has been a focus of research over the last few decades, chiefly in relation to more complex landscape settings, such as hedgerow network zones or mountain uplands [1,2,3]. A majority of papers have focused on mountain-area zones, where challenges posed by the degree of land slope, altitude and harsh climate further compound the many other possible constraints. Much of this research has been impelled by French teams, which can be explained by the large share of mountain-area farms in France (comprising around 17% of farmland in France), particularly in the Massif central (56% mountain-upland farms accounting for 62% of utilised agricultural area) where cattle farming is predominant [4]. Furthermore, challenges related to distance increasingly compound the other farm work constraints. These challenges are largely due to expanding of the field patterns; the average farmland area tripled in size between 1970 and 2010 to reach 57 ha—or 48 ha excluding collective areas—in mountain-area farms in 2010 [4].
Analysis of land field utilisation has often been addressed at the field level, considered to be the analysis mesh or as the elementary mesh [1,3], and sometimes aggregated up to the farm level [2,5]. The term ‘field’ generally equates to the functional field, i.e., a unit of land that livestock farmers use for the same purpose throughout a season, which results from several neighbouring cadastral parcels being collapsed together [6,7,8] or from one cadastral parcel being divided up [2,9]. Direct survey is often the preferred approach for capturing field features and uses and the rationales given by the farmers, but the method is time-intensive, especially if the objective is to exhaustively cover every factor (e.g., all fields in a region and all uses across a whole season) and/or achieve a representative picture of a whole population (e.g., large sample). Few papers have reported surveys undertaken across a large sample of livestock farms. Research teams sometimes move to shorten the time required for data collection by conducting a small number of surveys and supplementing this data with expert experience [10] or data from farm networks [5], or by narrowing the scope in order to cover a subset fraction of the field parcels [11].
Whatever the method implemented or objective pursued, all of the studies on this topic report that livestock farmers use field traits to gauge the most appropriate utilisation of the field given the livestock system chosen and the objectives set. As field patterns are becoming increasingly sprawled and scattered, livestock farmers are moving to optimise how they run their farm and develop purpose-led strategies, where decisions on how best to use fields are pivotal to the process [12]. Their strategies integrate the specific geographic traits of the fields, along with other factors, like herd characteristics, herd management decisions or labour availability [1,6,9,10,13].
These various publications converge to show evidence that livestock farmers develop their own logics for using fields according to their geographical characteristics. It appears that the methods deployed favour an approach based on understanding each system in order to bring out a general principle. However, is it possible to identify a general principle through the statistical analysis of the relationships observed between the use of the fields and their geography? This question prompted us to posit two hypotheses: (I) field-use logics have common foundations, independently of the livestock system or the farmer implementing it, and (II) it is possible to bring out these common foundations via the analysis of a large amount of data, freeing ourselves from the analysis of each system.
Our objective here was to show and characterise the interrelationship of field use and field geography via a farmer-based approach, exhaustive at the level of each farm and representative of the population of dairy cattle farmers in a mountain-upland area. The expected outcome was evidence of a set of logics for field-use according to the geographical characteristics that are generic to mountain-upland dairy cattle systems.

2. Materials and Methods

2.1. Data Collection

The information was collected through a survey of 100 grass-fed dairy-cattle farmers in the mountain uplands of the Cantal and Puy-de-Dôme regions, France. Two survey campaigns were led in 2005 (n = 72 surveys) and 2009 (n = 28 surveys), focused on traditional Salers breed-system farmers and specialised dairy farmers. All surveys were conducted in the farms and included two interviewers, in a single pass, and a questionnaire. The breeders were recruited in 2005 from among the 90 breeders listed by the Tradition Salers Association, with the aim of analysing the relationships between the uses of fields and their geographical characteristics. The breeders were selected in 2009 by the Cantal and Puy-de-Dôme milk control authorities, with the aim of analysing the relationships between calving periods and forage management, using a questionnaire designed to collect the same information on fields and farms as in 2005. General farm and herd-system characteristics are presented in Table 1. The across-farm variability stems partly from specific features of the traditional Salers system, with cows simultaneously producing milk and 10-month weanling calves [14], and cows milked throughout lactation or the onset of suckling after a few months in-milk. Milk output from Salers cows (2223 kg/cow/year [15]) is lower than in dairy breeds, largely because a share of the milk is systematically sucked by the calf, which has to be present alongside the dam for it to let down milk.
Details about the uses made of each field (e.g., dates of cuts, dates of animal lots turned into/out of the field, animal types and herd counts) were recorded using grass-harvest and grazing-period diaries. The geographical characteristics of each field were collected and described by four variables. Surface area (in ha) was recorded from the administrative records filed by the farmers, and the other geographical criteria were detailed based on the farmers’ statements. Distance from the farmstead (in km) corresponds to the journey travelled by road to the field. Slope was qualified by its intensity as perceived by the farmer (shallow, average, or steep) and its proportion across the field. Altitude (in m.a.s.l.) corresponds to the mean altitude of the field.

2.2. Data Analysis

The data were analysed at the grass-field scale, defined as an area of land used for the same purpose throughout the grazing season [2]. The use made of each field was described from turn-out to pasture in spring until return to stall in the autumn, via eight variables. The starting use-date and ending use-date served to capture the time-in-use (in days) and its position in the season. The date of first use (cut or graze) was characterised in relation to a theoretical date of the beginning of ear emergence (TDBE), which was calculated on the premise that the Massif central permanent grassland starts heading (ear emergence) at 120 calendar days at an altitude of 400 m.a.s.l., with a 6-day lag every further 100 m.a.s.l. interval [16]. Details of the date of first cut and number of cuts were collected to complete the descriptive data on grass-resource harvesting. The animal subcategory served to account for the animals that use the field during the grazing season: milked cows, suckler cows (un-milked late lactation cows) or dry cows, and calves (0–1 year), young heifers (1–2 years), and old heifers (2–3 years). The grazing intensity (in LU × day/ha, total or per animal category) was calculated by taking into account the field area and, for each grazing period, the duration (number of days between the entrance and exit dates), herd size and animal category. The forage supply (in kg DM/ha) was calculated as the total forage distributed to animals on the field during the grazing period.
The first-round survey campaign was previously analysed by Garcia-Launay et al. (2012) [9], who used principal component analysis (PCA) and hierarchical agglomerative clustering (HAC) to stratify the fields (n = 1586) into 3 type-groups according to use-type, i.e., a group of grazed-only fields (six classes), a group of grazed-and-cut fields (six classes), and a group of cut-only fields (three classes). Thereafter, we used the aggregation protocol described in Perrot (1990) [17]. For this, the fields from the second-round survey campaign (n = 756) were separated by multi-level sorting (in Excel) according to their use profile, and were then aggregated into the same clusters as determined previously in Garcia-Launay et al. (2012) [9]. Another class was moreover created for the fields grazed by dry cows that had not been described in the first-round survey campaign. The three classes of cut-only fields identified in 2012 were rearranged here to factor in the earlier first grass harvest and the greater number of grass harvests observed in the second-round survey campaign.
Each field class was then related to the corresponding geographical characteristics. ANOVA was performed using XLSTAT software to determine the significant differences between the field groups and between the field clusters. When the result of ANOVA was significant, pairwise means were compared using the Tukey test, with a significance threshold set at 0.05. The proportional slope variable was angular (arcsine square root) and was transformed to check the hypothesis of normal distribution and homogeneity of variances [18]. For the same purpose, distance from the farmstead and surface area were natural log-transformed.

3. Results

The total survey dataset counted 2341 fields: 1148 (49%) were grazed-only, 962 (41%) were grazed-and-cut, and 231 (10%) were cut-only (Table 2). The grazed-only fields were used earlier in the season and for longer periods, whereas the cut-only fields were used later in the season and for shorter periods. Grazed-and-cut fields were intermediate between grazed-only and cut-only on all use-type criteria. Grazed-only fields were the steepest and highest fields, grazed-and-cut fields were the lowest fields, and cut-only fields were the smallest and furthest from the farmstead (Table 2 and Figure 1).

3.1. Grazed-Only Fields

The animal category functioned to structure the seven classes of grazed-only fields (Ca, calf; He1, young heifer 1–2 years; He2, old heifer 2–3 years; MC, milked cow; SC, suckler cow; DC, dry cow; and DivG, diversified grazing) (Table 3). The diversified grazing field class (DivG) and the milked cows field class (MC) had the highest headcount (32% and 25% of the total, respectively) whereas the dry cows class (DC) had the lowest headcount (3% of the total). All of the fields were almost exclusively used by a single category of animal (71–94% of total grazing), except for the DivG fields which were grazed by all categories, but mainly by milked cows and heifers. The calf field class (Ca) were the most intensively grazed and the most abundantly distributed with forage, whereas DivG and DC fields were the least intensively grazed and the least abundantly distributed with forage. Grazing started very early in the season for all field classes (from −20 to −27 days ahead of the TDBE).
All of the grazed-only field classes were at a similar altitude and shared a similar proportion of sloping ground (Table 3). However, the grazed-only field classes were differentiated on various distance–area combinations (Figure 2 and Table 3). Fields allocated for use by young or not-immediately-productive animals (Ca, He1, He2, DC), which do not require large amounts of grazeable grass, were smaller but tended to be closer to the farmstead as the need for supervision was increased. Ca fields were thus closest to the farmstead, while He2 fields were furthest away. Conversely, fields allocated for use by adult or immediately-productive cows (MC, SC), which require sizeable amounts of grass to graze, were bigger but tended to be closer to the farmstead as the need for hands-on intervention was increased. The MC fields were thus kept close to the farmstead to facilitate the twice-daily milking work, whereas SC fields were further away. DivG fields had the same geographical profile as the group of grazed-only fields, although on average they were bigger, more steeply-sloped, and further from the farmstead.

3.2. Cut-Only Fields

The earliness of the first cut structured the three classes of cut-only fields (Table 4). The intermediate-cut fields class (iC) was the biggest of these three classes (57% of the total). Late-cut fields (lC) were used relatively late on and were only cut one time, as opposed to the early-cut fields (eC), which were first cut early in the season and cut several times, and iC fields which logically intermediate between lC and eC. Field area again emerged as a discriminating geographical factor for the use of cut-only fields (Figure 3 and Table 4), whereas these fields had already been identified as smaller than grazed-only or grazed-and-cut fields. The eC fields were found to be the largest, and also presented favourable factors for more intensive use, with an intermediate distance from the farmstead and a slightly lower altitude. The iC fields were furthest from the farmstead but were nevertheless cut several times. The lC fields were closer to the farmstead but were handicapped by a higher altitude.

3.3. Grazed-and-Cut Fields

The use sequence (i.e., the order and number of grazing or cuts and period used in the season in terms of earliness and duration) structured the six classes of grazed-and-cut fields (Table 5). The grazed then cut then grazed sequence (GCG) and the cut then cut then grazed sequence (CCG) classes had the biggest counts (27% and 21% of the total, respectively), and the grazed then cut sequence (GC) class had the lowest count (3% of the total). The fields that started with grazing (GC and GCG) were used from a very early date (37 and 38 days ahead of the TDBE) and had the most extreme (the shortest and longest, respectively) durations of use. Among the fields that started with a cut (early cut then grazed sequence (eCG), late cut then grazed sequence (lCG), and cut then cut then grazed sequence (CCG)), the time of the first use was latest for lCG fields and the number of cuts was highest for CCG fields. All grazed-and-cut fields were mainly grazed by milked cows (36–50%), with GCG fields most intensively grazed and GC fields least intensively grazed. Diversified sequence fields (DivS) were intermediate on all use-type criteria (grazing and cutting). Geographical characteristics differed according to the type of first use (Figure 4 and Table 5). Fields first used for grazing (GC and GCG) were closer to the farmstead and were larger and steeper with greater use for grazing (GCG), and thus shared some overlapping characteristics with MC fields. The fields that were cut first (CCG, eCG, lCG) tended to be smaller, shallower-sloped, and further from the farmstead, and thus shared some overlapping characteristics with cut-only fields. eCG and lCG fields were only significantly different on the altitude factor, with a large enough gap (156 m.a.s.l.) to offset the start of spring-season plant emergence (Table 5). The DivS fields, which were smaller and further from the farmstead and, therefore, posed more constraints for both grazing and mowing, were mostly left for adult cows (suckler and milked) to graze. These fields, which were predominantly used in the spring, corresponded to multi-option fields that could either be mobilised to increase the provision of grass to graze and leave other grazed fields more time for regrowth if weather conditions were not right for grass growth, or were side-lined for cutting if the weather conditions were right for grass growth.
Figure 5 provides a visual summary of where grazed-only field classes and grazed-and-cut field classes are positioned in terms of distance–area combinations and grazing intensity. The left of the figure features small and close-to-farmstead fields used for calves and dry cows, with the closest fields kept for calves and given regular forage supplements and attentive supervision. The middle of the figure features heifer fields that were appropriately average-sized for growing animals, located at a short distance from the farmstead for the small heifers that still need a fair amount of surveillance or at a longer distance for the older heifers that are more low-maintenance. The right of the figure features the big fields allocated to productive animals: very close-by fields were kept for milked cows due to the milking constraint, fields further away were used for the suckler cows, and fields even further away were used for diversified grazing. Grazing intensity was highest on grazed-only fields, particularly those used for animals with growth or production issues fed a high proportion of grazed grass. However, the large diameter of the calf fields illustrates their specific function as ‘parking’ fields, located very close to the cowshed and provisioned with 10 times more feed forage than all other fields. All of the grazed-and-cut fields were at an intermediate distance, with the smallest fields cut first and the biggest fields grazed first.

4. Discussion

The 1990s marked a surge in research to understand the logics employed by livestock farmers to rationalise their field use strategies, especially in ‘unfavourable’ settings, in terms of natural environment and/or structural factors. Here we articulate this same line of research and pursue the novel approach initially developed by Garcia-Launay et al. (2012) [9]. We approached this analysis at the level of the field and its uses throughout the entire grazing season, at the scale of a population of working farm operations, without integrating the individual logic applied by each livestock farmer, before going on to associate the geographical characteristics of the fields. However, we mobilised a large-sized sample (100 farmers, 2341 fields) in order to ascertain a common logic applied by a population of farmers.
Most of the extant researches have addressed field use practices as part of the farmer’s wider livestock system strategy, where field geography is just one of many components of the system. Despite being different to our approach here, and despite the diversity of study locations and objectives targeted, studies on the field use–field geography relationships produced findings that, particularly regarding distance, area and slope characteristics, are consistent and coherent with our findings here. Morlon and Benoit (1990) [13] characterised field uses on farms in north-eastern France (the Lorraine region) via a hierarchical ranking of their geographical constraints, and, in addition, highlighted correlational fits between field uses and sets of field constraints, e.g., distance and area for dairy production, or slope and area for feed crop harvesting. Thénail and Baudry (2004) [2] and Marie and Delahaye (2009) [3] studied hedgerow network areas (bocage) in France, Spain, and the UK, and observed concentric field uses according to distance-to-farmstead thresholds, with the closest fields used for cows in milk, and fields used for feed harvests and grazing other types of animals located further away, which is in line with our results. Likewise, Brunschwig et al. (2006) [6], in relation to Massif central farms, showed field uses patterned by the type of animal (e.g., dairy cow or heifer) in a way that was bounded by distance thresholds and moved with centrifugal or centripetal forces over the course of the grazing season. Our results, which show the importance of closeness or distance of fields according to their use, are consistent with those of these three studies. However, we did not identify any distance thresholds or centrifugal or centripetal trends over the season. Both Brunschwig et al. (2006) and Marie and Delahaye (2009) [3,6] also highlighted the prominent role of various distance–area combinations in the fields’ uses, as we did. Andrieu et al. (2007) [5] identified both the slope and distance to farmstead as determinant factors of field uses in Auvergne-region mountain landscapes (shallower-sloped fields used for cutting, closer-to-cowshed fields used for grazing dairy cows) whereas surface area was only identified as a determinant factor for fields used to harvest grass silage. Our results are in line with these authors’ observations, but allow for a much more precise approach to these elements. In Aubrac-region farms further south, Martin et al. (2009) [10] identified distance (from the barn or the closest fields) as the single biggest determining factor (due to the possibility of returning the herd daily to the barn or moving it to another field, without transport), followed by slope (possibility of mechanisation) and distance–area combination (possibility of feeding 5 LU/ha for 3 days if the field is remote from the farmstead). Our results are consistent with these authors’ analyses, which are based on expert opinion, but our results are however more generic in that they are obtained from direct interviews with many farmers. Various types of grassland-use sequences (harvest(s) and grazing) over the course of a campaign (up to five or six) emerged in work by Dubeuf et al. (1995) [19] and Rapey et al. (2008) [7], which converges with the diverse patterns of field use identified here (six classes of cut and grazed fields). Dubeuf et al. (1995) [19] also reported, as we did, that cut-only fields were cut relatively late, that proximity (a 2 km perimeter) was an important factor for grazed-and-cut fields with an early first use (cutting or early spring grazing), that more distant and sloping fields were used for grazing heifers or dry cows, and that big fields were used for dairy cows. Marie and Delahaye (2009) [3] noted, however, that dairy-cow fields were always nearby, which is in agreement with our results, but these authors found that such fields were not always big, which contrasts with our results.
The geographical criteria employed here at the field level (slope, distance, area, altitude) are the same criteria as employed in studies conducted at wider scales (local community, region, province) to model trajectories of adaptation around territorial issues. Such modelling addressed, for example, agricultural land abandonment and reforestation in Italian mountains areas [20,21], climate and socio-economic scenarios in the French Alps [22], land fragmentation across dairy farms in Spain [23], strategic land-use allocation between livestock zones and urban zones in the Netherlands [24], and on to permanent grassland landscapes in Portugal [25].
Authors often add caveats limiting the generalisability of their results, citing a domain of validity that is tied to a given type of activity and/or a given territorial community [6,11,13], or an underpowered volume of data [10]. Here, our study collected a huge number of field data and built a set of field use classes showing significant differences—including for the associated geographical variables—that underline a robust set of results that are generic to mountain-upland dairy cattle systems in the Massif central. However, this generalisability still cannot extend to uplands regions where grazing is organised around moving the herd up to high summer pastures, like the Alps [19] or the Pyrenees [26]. The results can be extended to mixed-purpose farms (dairy and suckling system), which would encompass 46% of the farmers surveyed, but are not readily extendable to pure suckling system operations. Dairy farming has a narrower set of field-use options than suckling farming, particularly for cows in milk [10], and suckling-herd fields are managed in blocks (sets of several proximate fields that can be rotated around to offer adequate pasturage) so as to economise the frequency and time of herd movements [6].
The relativity inherent to the field pattern, to its localisation, and/or to the livestock farmer is often cited as a limitation to the generalisability of study findings. Priority rules governing the identified geographic constraints can effectively be made to shift due to the emergence of a stronger constraint. For example, a high density of small fields bounded by hedgerows may prompt breeders to consider the linear of hedgerows to be more important than field area and distance to farmstead [2], or a lack of grass on near-to-farmstead fields to agree to make silage on fields that are further away [13], or even a low mechanisable area to push back the grazing-distance thresholds for dairy cows in an effort to preserve cuttable land [5]. Le Ber and Benoit (1998) [27] demonstrated distance relativity in a heavily wooded environment by differentiating the distance to the farmstead (for example for grazing cows in milk) from the distance to the forest, as pastures are best located with forest cover to provide shelter from wind and sun, whereas crop fields should be established further away from forests. Some breeders may also reprioritise geographical criteria in response to challenging land-use constraints, typically to take advantage of the fields’ altitudinal staggering in order to make profit from the staggered grass growth and thus distribute less concentrated feed [8]. However, Marie and Delahaye (2009) [3] concluded that the same set of basic principles of rationality (in field uses according to the distance–area combination) were common to dairy farms in all five hedgerow network areas studied in France, England, and Spain, independently of the size of the farms or their level of intensification or fragmentation. Lastly, collecting information based on expert input or farmers’ statements also introduces relative subjectivities into the findings [7,10]. The assessment of the level of slope and land-use potentials on sloped fields is hugely dependent on the farmer’s own subjectivity, but also on the equipment at their disposal [8]. Here, our design surveying a large number of farms and a large number of fields enabled us to smooth out local or human particularities to objectively capture field use–field geography relationships at the scale of a large population of grass-fed dairy cattle (or mixed dairy–suckler system) farms in a mountain-upland area.
However, our study did not integrate all of the factors and features that can potentially influence livestock-farmer decisions. Other criteria have been identified as likely to influence field-use potentials, such as the presence of obstacles (rocks, trees, streams, etc.) or the absence of a watering point [28,29], their shape [1,2,28], their accessibility and soil water holding capacity [5,6,10,13,26,27]. Work overload, the arduousness of farm work, or the desire to find a better work–life balance call all lead prompt livestock farmers to simplify their herd management practices (in terms of diet, reproduction strategy, milking schedules, etc.) or rationalise their work schedules (to align to more normal working-week hours) [12,30]. Some of these choices could shift the field-use rules, for instance, making it possible for milked cows to graze on more distant fields as part of a system that is less milking-driven (grouped calvings over a short period of time and once-a-day milking in late lactation) [31]. The growing demographic of women heading up livestock operations (in 2016, 27% of farm managers or co-managers and associates were women, against 8% in 1970 [32]) or spouses of farmers working off-farm (50% of cases in 2010 against 40% in 1997 [4,33]) is driving a decisive shift to ‘normalise’ on-farm workhours and simplify task organisation accordingly [30].
Looking out to a wider scope, considering the ecosystem services bundles provisioned by livestock farmers, the plurality of users and uses for pastoral farming spaces, dealing with nuisance to neighbours, adhering to codes of environmental protection practice, accommodating footpaths and right of way, rural tourism (hiking trails, farm stays) and even traditional local practices, can also shape field use choices [34,35,36,37].

5. Conclusions

This study captured objectified field use–field geography relationships based on significant differences between the identified field-use classes (six grazed-only field classes, seven grazed-and-cut field classes, and three cut-only field classes) and between the associated geographical characteristics. We adopted a novel approach based on extensive field-research data (100 farmers, 2341 fields) at the level of the field and its uses throughout the entire grazing season, at the scale of the population of working farm operations, without integrating the individual logic applied by each livestock farmer, and going on to associate the geographic characteristics of the fields. We identified the slope, area and distance to the farmstead as the most determinant geographical factors used by cattle farmers in deciding how to make use of their fields, along with various distance—area combinations associated with specific field uses. Furthermore, we ascertained the logics underpinning the way farmers associate a field use to a set of geographical field characteristics, even though our survey did not explicitly question the farmers on this point. Our approach and design has thus produced robust results that can be considered generic at the scale of dairy or mixed-purpose (dairy and suckling systems) cattle farms in the pasture-based uplands of the Massif central (France), and even in the mid-mountain area.

Author Contributions

Conceptualisation, methodology, validation, writing—original draft preparation: C.S. and G.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to the cow herds of the farmers interviewed were not sampled, measured or manipulated in any way during the two studies covered by this publication.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors warmly thank the farmers surveyed, the ex-ENITA (now VetAgro Sup since 2010) students majoring in ‘Livestock farming and production systems’ in 2005 and 2009 who administered the surveys, the non-profit Association Tradition Salers, Hervé Molénat and Sylvain Bouscayrol for their valuable assistance on data processing, Fabienne Blanc and Claire Agabriel for their expert input and engagement as student supervisors, and METAFORM LANGUES (2 avenue Michel Ange 63000 Clermont-Ferrand) for the translation of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Francart, C.; Pivot, J.M. Incidences de la structure parcellaire sur le fonctionnement des exploitations agricoles en régions de bocage. Ingénieries Eau-Agric.-Territ. 1998, 14, 41–54. [Google Scholar]
  2. Thenail, C.; Baudry, J. Variation of farm spatial land use pattern according to the structure of the hedgerow network (bocage) landscape: A case study in northeast Brittany. Agric. Ecosyst. Environ. 2004, 101, 53–72. [Google Scholar] [CrossRef]
  3. Marie, M.; Delahaye, D. From Plot Shapes to Agricultural Uses. Comparison between the Organization of Agricultural Landscapes in Dairy and Bocage Areas (Galicia, Normandy, Southern England). In Proceedings of the 9th Rencontres de Théo Quant, Besançon, France, 4–6 March 2009. [Google Scholar]
  4. Agreste Les Dossiers. L’Agriculture en Montagne, Evolutions 1988–2010 d’Après les Recensements Agricoles. 2015, n°26, 75p. Available online: https://agreste.agriculture.gouv.fr/agreste-web/disaron/Dos26/detail/ (accessed on 20 September 2021).
  5. Andrieu, N.; Josien, E.; Duru, M. Relationships between diversity of grassland vegetation, field characteristics and land use management practices assessed at the farm level. Agric. Ecosyst. Environ. 2007, 120, 359–369. [Google Scholar] [CrossRef]
  6. Brunschwig, G.; Josien, E.; Bernhard, C. Geographical constraints and ways of utilizing the fields in the management of dairy cattle and suckler cattle. Fourrages 2006, 185, 83–95. [Google Scholar]
  7. Rapey, H.; Gueringer, A.; Gresset, F.; Houdart, M.; Josien, E.; Bigot, G. Diversity and adaptability of the spatio-temporal management of grazing farms: First learnings from case analysis in Cantal (Auvergne-France). Renc. Rech. Rumin. 2008, 15, 155–158. [Google Scholar]
  8. Bathfield, B.; Rapey, H.; Ingrand, S. Complex field patterns: A help or hindrance in reducing concentrate purchases by dairy farms in mountainous regions? Fourrages 2015, 222, 149–156. [Google Scholar]
  9. Garcia-Launay, F.; Sibra, C.; Molénat, H.; Agabriel, C.; Brunschwig, G. Grassland use in mountain bovine systems according to a hierarchy of geographical determinants. J. Agric. Sci. 2012, 150, 203–217. [Google Scholar] [CrossRef]
  10. Martin, G.; Hossard, L.; Theau, J.P.; Therond, O.; Josien, E.; Cruz, P.; Rellier, J.P.; Martin-Clouaire, R.; Duru, M. Characterizing potential flexibility in grassland use. Application to the French Aubrac area. Agron. Sustain. Dev. 2009, 29, 381–389. [Google Scholar] [CrossRef] [Green Version]
  11. Rapey, H. The diversity of land use spatial organization in mountain farms: Farm size and type are not the sole determining factors. Fourrages 2012, 210, 141–149. [Google Scholar]
  12. Hostiou, N.; Fagon, J. Simplification of livestock management: A analysis of simplified pratices developed in herbivore and grain-fed production systems. INRA Prod. Anim. 2012, 25, 127–140. [Google Scholar] [CrossRef] [Green Version]
  13. Morlon, P.; Benoit, M. Methodolgy for farm field pattern study. Agronomie 1990, 6, 499–508. [Google Scholar] [CrossRef]
  14. Agabriel, J.; Faure, B.; Lebreton, F.X.; Lherm, M.; Micol, D.; Garcia-Launay, F.; Pradel, P.; Angeon, V.; Martin, M. La race bovine Salers: Un atout pour le développement de son territoire d’origine par son identité forte et des produits qualifiés. Cah. Agric. 2014, 23, 138–147. [Google Scholar]
  15. Institut de l’Élevage. Résultats de Contrôle Laitier—Espèce Bovine—France 2019; Collection Résultats; Édité par l’Institut de l’Elevage: Paris, France, 2020; p. 115. [Google Scholar]
  16. Réseaux d’Élevage d’Auvergne et Lozère. Référentiel 2006 des Réseaux d’Elevage Auvergne et Lozère, Conjoncture 2005, Qualité de l’Herbe en Fonction de la Date de Récolte et de l’Altitude; Références. 2006, p. 197. Available online: http://www.grands-troupeaux-mag.fr/wp-content/uploads/2020/05/Les-r%C3%A9sultats-2019-du-contr%C3%B4le-laitier-dans-l%E2%80%99esp%C3%A8ce-bovine.pdf (accessed on 20 September 2021).
  17. Perrot, C. Construction of a farm typology by aggregation around poles based on expert knowledge. Methodological proposals and first resulsts achieved in Haute-Marne (France). INRA Prod. Anim. 1990, 3, 51–66. [Google Scholar] [CrossRef]
  18. Sokal, R.R.; Rohlf, F.J. Biometry: The Principles and Practices of Statistics in Biological Research, 3rd ed.; W.H. Freeman & Co Ltd.: New York, NY, USA, 1969; pp. 380–896. [Google Scholar]
  19. Dubeuf, B.; Fleury, P.; Jeannin, B. Diversity of the working of the forage systems and consequences for advisory work. Fourrages 1995, 141, 19–32. [Google Scholar]
  20. Cocca, G.; Sturaro, E.; Gallo, L.; Ramanzin, M. Is the abandonment of traditional livestock farming systems the main driver of mountain landscape change in Alpine areas? Land Use Policy 2012, 29, 878–886. [Google Scholar] [CrossRef]
  21. Sturaro, E.; Marchiori, E.; Cocca, G.; Penasa, M.; Ralanzin, M.; Bittante, G. Dairy systems in mountainous areas: Farm animal biodiversity, milk production and destination, and land use. Livest. Sci. 2013, 158, 157–168. [Google Scholar] [CrossRef]
  22. Lamarque, P.; Artaux, A.; Barnaud, C.; Dobremez, L.; Nettier, B.; Lavorel, S. Taking into account farmers’ decision making to map fine-scale land management adaptation to climate and socio-economic scenarios. Landsc. Urban Plan. 2013, 119, 147–157. [Google Scholar] [CrossRef]
  23. del Corral, J.; Perez, J.A.; Roibas, D. The impact of land fragmentation on milk production. J. Dairy Sci. 2011, 94, 517–525. [Google Scholar] [CrossRef] [Green Version]
  24. Carsjens, G.J.; van der Knaap, W. Strategic land-use allocation: Dealing with spatial relationships and fragmentation of agriculture. Landsc. Urban Plan. 2002, 58, 171–179. [Google Scholar] [CrossRef]
  25. Pereira, A. Landforms and The Shaping of Cultural Landscapes in Mountain Areas: A Methodology for The Analysis of Permanent Pastures Landscape of Alto Barroso Region (Northern Portugal). Landscape and Landscape Ecology. In Proceedings of the 17th International Symposium on Landscape Ecology, Nitra, Slovakia, 27–29 May 2015; pp. 150–161. [Google Scholar]
  26. Gibon, A.; Balent, G.; Alard, D.; Muntané, J.; Raich, Y.; Ladet, S.; Mottet, A.; Julien, M.P. The utilization of space by mountain livestock farms and the management of biodiversity. Fourrages 2004, 178, 245–263. [Google Scholar]
  27. Le Ber, F.; Benoit, M. Modelling the spatial organization of land use in a farming territory. Example of a village in the Plateau Lorrain. Agronomie 1998, 18, 103–115. [Google Scholar] [CrossRef] [Green Version]
  28. Malpel, L. Les Contraintes Relatives du Parcellaire dans le Fonctionnement des Systèmes Fourragers d’Exploitations Laitières du Massif Central. Propositions de Représentation et d’Analyse. Mémoire de fin d’Etudes; Enita Clermont: Clermont-Ferrand, France, 2001; 47p. [Google Scholar]
  29. Gay, J.; Poix, C.; Brunschwig, G.; Reuillon, J.L. Development of a software for assisted forecasting of forage system evolutions in dairy cattle livestock farms: PASSYFOU. Renc. Rech. Rumin. 2012, 9, 73–76. [Google Scholar]
  30. Dufour, A.; Dedieu, B. Rapports au temps de travail et modes d’organisation en élevage laitier. Cah. Agric. 2010, 19, 377–382. [Google Scholar] [CrossRef]
  31. Cournut, S.; Hostiou, N. Adaptations des systèmes bovin laitier pour réduire la contrainte travail: Une étude en Ségala (France). Cah. Agric. 2010, 19, 348–353. [Google Scholar] [CrossRef]
  32. Agreste. Graph’agri 2020, l’Agriculture, la Forêt, la Pêche et les Industries Agroalimentaires; Ministère de l’Agriculture et de l’Alimentation: Paris, France, 2020; 215p.
  33. Delame, N.; Thomas, G. De Plus en Plus de Conjoints d’Agriculteurs Travaillent Hors de L’Exploitation. Les Agriculteurs in L’Agriculture, Nouveaux Défis; INSEE, Institut national de la statistique et des Études Économiques: Montrouge, France, 2007; pp. 231–244. [Google Scholar]
  34. Eychenne, C.; Lazaro, L. Summer pastures: Between “commons” and “public goods”. Representations of pastoral areas and forms of government intervention. J. Alp. Res. 2014, 102, 12. [Google Scholar] [CrossRef] [Green Version]
  35. Delanoue, E.; Roguet, C. Social acceptability of French livestock production: Identification and analysis of the main controversies to the viewpoints of different stakeholders. Inra Prod. Anim. 2015, 28, 39–50. [Google Scholar] [CrossRef]
  36. Nettier, B.; Dobremez, L.; Brunschwig, G. Taking into account the interactions between alpine pastures and farming systems in agro-pastoral systems: A literature review. Inra Prod. Anim. 2015, 28, 329–340. [Google Scholar] [CrossRef]
  37. Ryschawy, J.; Tichit, M.; Bertrand, S.; Allaire, G.; Plantureux, S.; Aznar, O.; Perrot, C.; Guinot, C.; Josien, E.; Lasseur, J.; et al. How to assess the multiple services provided by livestock? A preliminary methodology applied on the French case-study. Inra Prod. Anim. 2015, 28, 23–38. [Google Scholar] [CrossRef] [Green Version]
Agriculture 11 00915 g001 550
Figure 1. Area, distance and slope (share of area) characteristics of all fields and field-groups.
Figure 1. Area, distance and slope (share of area) characteristics of all fields and field-groups.
Agriculture 11 00915 g001
Agriculture 11 00915 g002 550
Figure 2. Area, distance and slope (share of area) characteristics of the grazed-only field classes.
Figure 2. Area, distance and slope (share of area) characteristics of the grazed-only field classes.
Agriculture 11 00915 g002
Agriculture 11 00915 g003 550
Figure 3. Area, distance and slope (share of area) characteristics of the cut-only field classes.
Figure 3. Area, distance and slope (share of area) characteristics of the cut-only field classes.
Agriculture 11 00915 g003
Agriculture 11 00915 g004 550
Figure 4. Area, distance and slope (share of area) characteristics of the grazed-and-cut field classes.
Figure 4. Area, distance and slope (share of area) characteristics of the grazed-and-cut field classes.
Agriculture 11 00915 g004
Agriculture 11 00915 g005 550
Figure 5. Grazed-only field classes and grazed-and-cut field classes plotted according to distance, area and grazing intensity. Ca, calf; He1, young heifer 1–2 years; He2, old heifer 2–3 years; DC, dry cow; MC, milked cow; SC, suckler cow; DivG, diversified grazing; GC, grazed then cut field; GCG, grazed then cut then grazed field; eCG, early cut then grazed field; lCG, late cut then grazed field; CCG, cut then cut then grazed field; DivS, diversified sequences field.
Figure 5. Grazed-only field classes and grazed-and-cut field classes plotted according to distance, area and grazing intensity. Ca, calf; He1, young heifer 1–2 years; He2, old heifer 2–3 years; DC, dry cow; MC, milked cow; SC, suckler cow; DivG, diversified grazing; GC, grazed then cut field; GCG, grazed then cut then grazed field; eCG, early cut then grazed field; lCG, late cut then grazed field; CCG, cut then cut then grazed field; DivS, diversified sequences field.
Agriculture 11 00915 g005
Table
Table 1. Descriptive data on the farms surveyed (n = 100).
Table 1. Descriptive data on the farms surveyed (n = 100).
Mean (SD)Minimum–Maximum
Farmstead altitude (m.a.s.l.)894 (182.1)430–1400
Agricultural area (ha)82 (40.0)27–243
Number of fields24 (9.9)8–54
Permanent grassland area (%)91 (15.5)35–100
Herd size (LU)89.7 (35.03)38.6–216.1
Stocking rate (LU/ha agricultural area)1.17 (0.29)0.53–1.85
Harvested forage 1 (t DM/LU)1.60 (0.65)0.30–4.70
Purchased forage 1 (t DM/LU)0.12 (0.20)0–0.91
Grazing duration (d/year)216 (21.1)140–257
Number of dairy cows47 (20.1)22–130
Dairy output (1000 L/year)132.1 (120.0)15–550
Milking duration (d/year)321 (51.8)135–365
SD, standard deviation; LU, livestock unit; DM, dry matter, 1 Forage = hay, grass silage, wrapped grass, corn silage.
Table
Table 2. Field uses and geographical characteristics (all fields and stratified by field groups (Mean (SD)).
Table 2. Field uses and geographical characteristics (all fields and stratified by field groups (Mean (SD)).
All Fields
(n = 2341)		Grazed-Only Fields
(n = 1148)		Grazed-and-Cut Fields
(n = 962)		Cut-Only Fields
(n = 31)		p
Use characteristics
Starting use date (calendar d)138 (33.9)127 (32.4) c145 (32.4) b167 (21.0) a***
Ending use date (calendar d)285 (46.0)288 (44.4) b297 (33.6) a216 (39.1) c***
Use duration (d)148 (61.4)162 (58.0) a153 (45.0) b50 (47.2) c***
Difference between the 1st use date and the TDBE 1 (d)−10 (33.3)−22 (31.4) c−3 (31.6) b20 (21.4) a***
Number of cuts0.7 (0.83)---1.3 (0.54) b1.8 (0.78) a***
Grazing intensity (LU × day/ha) by
All animals222 (220.4)317 (241.1) a161 (149.9) b---***
Milked cows89 (151.0)111 (169.9) a84 (136.4) b---***
Suckler cows23 (83.2)28 (91.1)32 (96.5)---ns
Dry cows14 (49.4)18 (61.7)13 (36.5)---ns
Young heifers29 (75.0)48 (96.9) a14 (40.4) b---***
Old heifers30 (93.4)47 (124.6) a17 (44.7) b---***
Calves22 (104.2)40 (144.5) a6 (27.7) b---***
Distributed forage (kg DM/ha)258 (792.4)366 (1052.2) a191 (415.1) b---***
Geographical characteristics
Area (ha)3.3 (4.09)3.7 (4.86) a3.1 (3.33) a1.9 (1.78) b***
Distance from farmstead (km)2.5 (6.26)2.9 (8.05) b1.9 (3.46) b3.4 (4.92) a***
Altitude (m.a.s.l.)877 (176.2)886 (180.4) a867 (172.7) b868 (165.9) ab*
Slope (% of area)31 (45.0)46 (48.1) a18 (37.4) b13 (32.8) b***
SD, standard deviation; LU, livestock unit; DM, dry matter; Different letters within a row indicate significant differences at p < 0.05: *** p < 0.001; * p < 0.05; ns, non-significant, 1 theoretical date of the beginning of ear emergence.
Table
Table 3. Field uses and geographical characteristics of the grazed-only field classes (n = 1148) (Mean (SD)).
Table 3. Field uses and geographical characteristics of the grazed-only field classes (n = 1148) (Mean (SD)).
Field Classes 1CaHe1He2MCSCDCDivG
numbers90179992829937362p
Use characteristics
Grazing intensity/animal category (% of total grazing intensity)
Milked cows4.8 (12.32) c0.2 (2.02) c0.9 (5.46) c91.3 (14.85) a5.4 (12.97) c0.4 (2.28) c29.6 (38.18) b***
Suckler cows0.6 (3.50) c0.4 (3.05) c1.7 (6.96) bc1.1 (5.07) c70.7 (34.68) a0.0 (0) c10.0 (24.07) b***
Dry cows0.1 (1.22) cd0.1 (0.80) d1.4 (5.96) cd4.0 (9.32) bc0.8 (3.13) cd90.4 (27.26) a6.2 (15.59) b***
Young heifers0.9 (3.65) c93.9 (12.75) a2.0 (6.94) c0.5 (3.72) c0.6 (2.61) c1.0 (4.18) c18.0 (27.65) b***
Old heifers0.9 (4.18) c4.6 (11.22) c93.9 (13.26) a1.1 (5.11) c5.9 (10.91) c0.0 (0.05) c18.6 (29.20) b***
Calves77.0 (31.48) a0.0 (0.29) c0.0 (0) c1.8 (4.19) bc1.0 (5.54) c0.1 (0.76) c7.4 (20.46) b***
Total grazing intensity
(LU × day/ha)		623 (152.1) a241 (118.3) c336 (243.7) b383 (168.3) b355 (176.4) b274 (152.1) bc216 (155.0) c***
Distributed forage (kg DM/ha)2390 (2712.6) a91 (299.2) b124 (337.0) b373 (592.9) b258 (664.9) b23 (74.6) b124 (254.4) b***
Date of first use (calendar d)123 (23.0)128 (27.4)122 (24.0)124 (32.9)124 (37.2)125 (37.4)132 (35.4)ns
Date of last use (calendar d)303 (39.0) a286 (41.5) bc285 (46.2) bcd297 (35.5) ab304 (35.7) a261 (57.7) d279 (48.9) cd***
Use duration (d)181 (46.0) a157 (53.5) bc163 (53.0) abc174 (50.1) ab180 (57.3) a136 (75.7) c147 (62.6) c***
Difference between date of 1st use and TDBE 2 (d)−27 (19.6)−21 (25.4)−27 (24.2)−22 (32.8)−25 (35.7)−24 (38.2)−20 (34.6)ns
Geographical characteristics
Area (ha)1.2 (1.11) d2.8 (2.74) c2.6 (2.27) bc4.2 (3.97) a5.2 (6.76) a1.5 (1.32) cd4.4 (6.37) ab***
Distance from farmstead (km)0.9 (2.66) d3.5 (4.71) bc7.8 (14.54) a0.7 (2.11) d2.5 (4.62) c4.0 (4.97) ab3.6 (10.62) c***
Slope (% of area)39 (48.2)51 (48.4)44 (48.0)39 (48.0)46 (47.3)43 (46.1)50 (47.8)ns
Altitude (m.a.s.l.)888 (181.0) ab894 (173.0) a878 (146.3) ab840 (174.7) b888 (217.0) ab878 (91.8) ab921 (184.4) a***
Different letters within a row indicate significant differences at p < 0.05: *** p < 0.001; ns, non-significant, SD, standard deviation; LU, livestock unit; DM, dry matter, 1 Ca, calf; He1, young heifer 1–2 years; He2, old heifer 2–3 years; MC, milked cow; SC, suckler cow; DC, dry cow; DivG, diversified grazing; 2 TDBE, theoretical date of the beginning of ear emergence.
Table
Table 4. Field uses and geographical characteristics of the cut-only field classes (n = 231) (Mean (SD)).
Table 4. Field uses and geographical characteristics of the cut-only field classes (n = 231) (Mean (SD)).
Field Classes 1lCiCeCp
Number5013150
Use characteristics
Date of first cut (calendar d)197 (7.6) a166 (11.4) b140 (4.8) c***
Date of last use (calendar d)203 (29.5) a215 (43.2) a231 (30.5) b**
Difference between date of 1st use and TDBE 2 (d)45 (17.5) a20 (12.9) b−7 (6.0) c***
Use duration (d)7 (26.6) a49 (43.4) b91 (32.6) c***
Number of cuts1.0 (0) a1.7 (0.65) b2.7 (0.59) c***
Geographical characteristics
Area (ha)1.5 (1.99) b1.8 (1.75) b2.4 (1.48) a***
Distance from farmstead (km)1.4 (1.52) b4.5 (5.20) a2.6 (5.54) ab***
Slope (% of area)17 (37.2)13 (31.8)11 (30.3)ns
Altitude (m.a.s.l.)966 (199.2) a844 (160.4) b838 (95.0) b***
SD, standard deviation; Different letters within a row indicate significant differences at p < 0.05: *** p ≤ 0.001; ** p ≤ 0.01; ns, non-significant; 1 lC, late-cut field; iC, intermediate-cut field; eC, early-cut field; 2 TDBE, theoretical date of the beginning of ear emergence.
Table
Table 5. Field uses and geographical characteristics of the grazed-and-cut field classes (n = 962) (Mean (SD)).
Table 5. Field uses and geographical characteristics of the grazed-and-cut field classes (n = 962) (Mean (SD)).
Field Classes 1GCGCGeCGlCGCCGDivSp
numbers33260170177206116
Use characteristics
Date of first use (calendar d)113 (21.7) d110 (20.3) d157 (12.4) b185 (12.3) a152 (19.2) b141 (26.9) c***
Date of last use (calendar d)195 (24.8) d309 (28.5) a310 (22.2) a299 (23.1) b301 (23.0) b268 (22.5) c***
Date of 1st cut (calendar d)180 (20.0) ab180 (20.2) a156 (12.2) c184 (12.0) a154 (16.5) c174 (18.2) b***
Difference between date of 1st use and TDBE 2 (d)−38 (20.9) e−37 (19.9) e+13 (14.8) b+32 (14.5) a+4 (17.4) c−7 (28.3) d***
Use duration (d)83 (29.2) e199 (37.3) a153 (20.9) b114 (23.9) d149 (28.5) b127 (35.8) c***
Number of cuts1.2 (0.61) b1.1 (0.37) b1.0 (0) c1.0 (0) c2.1 (0.39) a1.2 (0.43) b***
Grazing intensity/animal category (% of total grazing intensity)
Milked cows45 (49.0) ab50 (42.2) a47 (46.2) ab36 (45.4) b41 (47.0) ab49 (45.7) ab*
Suckler cows6 (24.0) ab14 (27.9) a17 (34.0) a18 (35.8) a3 (16.4) b18 (34.1) a***
Dry cows3 (17.1) b7 (17.5) b9 (23.3) b11 (27.1) b20 (36.3) a4 (14.7) b***
Young heifers12 (32.7)8 (21.3)13 (30.0)13 (30.4)16 (34.0)11 (26.8)ns
Old heifers30 (46.0) a12 (25.4) b8 (23.0) b15 (30.8) ab12 (27.9) b13 (30.7) b**
Calves1 (3.1)4 (14.5)4 (17.2)3 (14.4)4 (17.4)4 (13.4)ns
Total grazing intensity
(LU × day/ha)		98 (101.0) c225 (151.9) a160 (121.2) b153 (192.1) bc110 (131.8) bc139 (87.5) bc***
Distributed forage (kg DM/ha)130 (245.9) a271 (471.5) a223 (358.3) a149 (367.3) a114 (390.1) a181 (468.1) a**
Geographical characteristics
Area (ha)3.2 (2.91) ab4.2 (4.92) a2.6 (2.15) b2.9 (2.91) b2.6 (2.14) b2.5 (1.86) b***
Distance from farmstead (km)1.2 (2.17) b1.1 (2.27) b1.5 (2.66) ab2.5 (4.20) a2.4 (3.34) a2.7 (5.10) ab***
Slope (% of area)18 (38.5) ab26 (42.8) a20 (38.9) ab13 (32.5) b10 (28.3) b21 (38.7) ab***
Altitude (m.a.s.l.)913 (157.2) ab850 (181.3) b797 (158.5) c953 (173.2) a866 (136.8) b867 (176.6) b***
SD, standard deviation; LU, livestock unit; DM, dry matter; Different letters within a row indicate significant differences at p < 0.05: *** p ≤ 0.001; ** p ≤ 0.01; * p < 0.05; ns, non-significant; 1 GC, grazed then cut field; GCG, grazed then cut then grazed field; eCG, early cut then grazed field; lCG, late cut then grazed field; CCG, cut then cut then grazed field; DivS, diversified sequences; 2 TDBE, theoretical date of the beginning of ear emergence.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Sibra, C.; Brunschwig, G. Generic Relationships between Field Uses and Their Geographical Characteristics in Mountain-Area Dairy Cattle Farms. Agriculture 2021, 11, 915. https://doi.org/10.3390/agriculture11100915

AMA Style

Sibra C, Brunschwig G. Generic Relationships between Field Uses and Their Geographical Characteristics in Mountain-Area Dairy Cattle Farms. Agriculture. 2021; 11(10):915. https://doi.org/10.3390/agriculture11100915

Chicago/Turabian Style

Sibra, Cécile, and Gilles Brunschwig. 2021. "Generic Relationships between Field Uses and Their Geographical Characteristics in Mountain-Area Dairy Cattle Farms" Agriculture 11, no. 10: 915. https://doi.org/10.3390/agriculture11100915

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop