Is livestock is an unstable form of wealth limiting social differentiation? This study demonstrates that pastoral wealth follows the same pattern as that of all forms of wealth. Wealth accumulates over time, and while the highest earners can save much of their income (i.e., newborn livestock), low earners cannot. Thus, high earners can accumulate more wealth over time.
Using governmental statistics from 2001 to 2018 on reindeer herding in Norway, this paper investigates long term wealth differentials in the reindeer husbandry in Norway.
Noteworthy, the data do not represent a sample of reindeer herders but encompass the entire population of licensed reindeer herders in Norway
The paper argues against a dominant view that livestock, as the primary source of wealth, limits the development of inequalities, making pastoralism unable to support complex or hierarchical organisations (see also this page).
Main results
From 2001 to 2018, livestock inequality, measured by the Gini coefficient, decreased nationally and regionally.
Cumulative wealth distribution follows a similar pattern: the proportion owned by the wealthy decreased from 2001 to 2018, whereas the proportion owned by the poor increased.
Rank differences persist with minor changes, especially among the poor; around 50% of households ranked as poor in 2001 continued to be so in 2018.
Being wealthy or poor differed between regions. For example, households classified as poor in the South had, on average, larger herds than those in the North. Furthermore, compared to the South, wealthy households in the North had, on average, substantially more reindeer while at the same time experiencing much more variation in reindeer numbers
In sum, there is nothing apparent in pastoral adaptation, with livestock as the main base of wealth that levels wealth inequalities and limits social differentiation.
Annoyingly, the journal cropped one of the figures in the pdf version of the paper so that relevant information is missing. So, Fig.7 should be viewed online until this is corrected (https://www.nature.com/articles/s41599-023-02316-3/figures/7).
But, while we looked at the wealthy and poor, another way to look at wealth differentials is to look at how the poor are doing relative to the middle class, i.e. the 50/10 ratio.
In effect, we can compar the median reindeer number (the number of a herder right in the middle of the distribution) with the tenth-percentile reindeer number (the number of someone near the bottom of the income distribution).
If the 50/10 ratio shrinks it means that poorer households are catching up with the ‘middle-class’ off.
While not addressed in the paper, here is a figure showing that the poor are catching up to the middle-class, especially in recent years.
Temporal trends in median and 10th percentile reindeer numbers for both regions. See the full paper here.
Join us as we explore the world of nomadic herding, from the traditional knowledge and skills used by nomadic herders to the economic and environmental benefits of this unique way of life. Discover how nomadic herding can be a sustainable and valuable part of the modern world.
Like so many else, I’ve been experimenting with ChatbotGPT from openai.com. I’m completely blown away by the possibilities and the level of content that it manages to create.
To test it, I asked it to give me some catchy title suggestions for a blog post about nomadic pastoralism. One of the suggestions was “Nomadic Herding: A Sustainable Way of Life”!
So here it goes; I think it was pretty good but see for yourself:
write a 800 words blog post titled “Nomadic Herding: A Sustainable Way of Life”
Nomadic herding is a traditional way of raising livestock that involves moving from one place to another in search of pasture and water. This way of life has been practised for thousands of years and continues to thrive in many parts of the world. As a result, nomadic herders have a deep connection to the land and a wealth of traditional knowledge and skills to manage their animals and sustain their way of life.
But what makes nomadic herding a sustainable way of life? To answer this question, it’s essential to understand the unique characteristics of nomadic herding and how it can benefit the environment and local communities.
One of the critical characteristics of nomadic herding is the use of large, mobile herds of animals. This allows nomadic herders to move their animals to different areas in search of fresh pasture and water, and it will enable the land to recover after the animals have grazed. In addition, this type of mobility helps to prevent overgrazing and soil erosion and helps maintain the land’s health.
In addition, nomadic herders have a deep understanding of the land and its resources, using this knowledge to manage their animals and grazing patterns. This allows them to avoid damaging sensitive areas, such as wetlands and riverbanks, and to maximize the productivity of the land. Nomadic herders also use various traditional herding techniques, such as rotational grazing, to manage their animals and maximize the benefits of grazing.
Another critical aspect of nomadic herding is its role in conserving biodiversity. Nomadic herders often move their animals to different areas in search of pasture and water, which can help preserve their animals’ genetic diversity. In addition, nomadic herding can help to maintain the diversity of plant and animal species in a given area. This is because nomadic herders often graze their animals on land unsuitable for agriculture, which can help preserve natural habitats and ecosystems.
Nomadic herding also provides significant economic and social benefits to local communities. Nomadic herders provide food, fibre, and other products essential to many rural areas’ economies. In addition, nomadic herding can help create jobs and support local businesses. Nomadic herders also play a vital role in maintaining cultural traditions and identity, and they can help preserve a given area’s cultural heritage.
Despite the many benefits of nomadic herding, some challenges must be addressed to ensure its sustainability. One of the biggest challenges is the loss of pasture and water due to the expansion of agriculture, mining, and other forms of development. This can make it difficult for nomadic herders to find suitable grazing land for their animals, leading to conflict with other land users. In addition, nomadic herders must deal with the challenges of climate change, which can lead to changes in precipitation patterns and other impacts on the land.
To address these challenges, it is essential to support nomadic herding and to recognize its value as a sustainable way of life. This can be done through various measures, such as providing access to education, healthcare, and other services for nomadic herders.
This took me 5 minutes (or rather, it took 5 minutes to come up with the query to ChatbotGTP)… After that, I used Grammarly to freshen up some of the writing!
In the last couple of years, there has been a debate, especially in Khrono, concerning how the Research Council of Norway evaluates and funds scientific proposals.
There have been several stories whereby submissions to the Research Council of Norway (RCN) have been unfavourably assessed but then secured funding from the European Research Council (ERC).
ERC funds basic science, same as the RCN’s FRIPRO, or “Ground-breaking research”, programme.
For example, this story shows that two rejections from the Research Council of Norway resulted in a Starting Grant from ERC.
Or this story, where the RCN assigned very low grades on a proposal but the outcome in ERC was securing an ERC Consolidator grant.
This story is illuminating because the proposal submitted to RCN was just a shortened version of the one submitted to ERC. In effect, the main underlying idea and approach were the same.
Nevertheless, the average grade in the Research Council of Norway was 4 and represented the lowest grade the PI had ever received before!
In contrast, to receive funding from ERC, you need grade A (fully meets the ERC’s excellence criterion and is recommended for funding if sufficient funds are available) during two evaluation steps.
This is, of course, true but begs the question of whether there are some fundamental flaws in the evaluation process in the Research Council of Norway.
For example, I now find myself in the same situation: I’ve submitted more or less the same proposal 3 different times to the RCN and have received the average grade of 4 with no funding (see a news story here).
Last year I submitted an expanded version to ERC and was notified on the 9th of March that the project was approved for funding (ERC Consolidator Grant, see here for details about the project).
While the evaluations are subjective, especially concerning scientific excellence, some criteria are less so.
For example, PIs and scientific group evaluations are somewhat objective as they are based on track records documented by publications, previous grants, etc.
Compare, for example, assessments I’ve received in 2012 vis-vis assessments I’ve received in the last 3 submissions:
In 2012 I submitted a proposal where ‘The project manager and project group’ assessment got a 6 – Excellent. The meaning of this grade is:
“The project leader and/or research/project group is qualified at a high international level, has contacts within the foremost national and international research environments and will be able to play an important role in ensuring the success of the project.”
10 years on, with more leadership experience, loads more publications ++ and the evaluation has fallen to 4 – Good on all 3 submissions!
Granted, there are differences in how these grades are assigned: PI and group are now evaluated under ‘Implementation’, which also includes ‘feasibility’. Previously, ‘feasibility’ was a separate category (by the way, I received a 6 on this in 2012 also!).
But this only goes to show that the evaluation criteria used by the RCN have changed.
A change that apparently opens for more vague and less concrete evaluations: the fact is that before 2019 all the proposals I submitted scored on overall 6 (or, in one case, 7 whereby I got a Young Research Talent grant), but after 2018 I’ve scored on overall 4.
Compare this with the assessment from ERC, where 7 different reviewers scored my capabilities as a PI as ‘Exceptional’, ‘Excellent’ or ‘Very Good’.
If one subscribes to an idea that the evaluation process in ERC is better suited to pinpoint ground-breaking research—whatever that might be—the inescapable conclusion is that the RCN has designed an evaluation system that is missing the most innovative (a buzzword that RCN is mightily fond off) projects, at least in terms scientific excellence.
In fact, looking at the level of written evaluations from the RCN, it would be better to randomly select people in the Norwegian population and ask them what they think.
That would at least provide an unbiased evaluation process.
Last week I was informed that my project proposal “From small-scale cooperative herding groups to nomadic empires – a cross-cultural approach (COMPLEXITY)” was funded through the ERC Consolidator Grant scheme.
Had to wait to announce it since it was not official until 12:00 17.03.2022 when ERC announces their press release with all the successful grants. The press release can be found here.
The overall aim of the Consolidator Grant is to “… support mid-career researchers and will help them consolidate their teams and conduct pioneering research on topics and with methods of their choosing” (from press release).
Central thesis
COMPLEXITY is situated at the intersection of anthropology and ecology and deals with the evolution of political complexity.
The prevalent view of the evolution of complex societies favours agriculture as the main factor.
How do we then explain the rise of nomadic empires?
One common explanation refers to conflict, and large-scale conflict with China has been presented as the central element in the rise of, for example, the Mongol Empire.
In 1242, Europe stood on the precipice of destruction. Based in Hungary and Serbia, the Mongol armies were poised for conquering the rest of Europe. Only the death of the Great Khan halted the Mongol advance, sparing Europe from the fate of an inevitable conquest. Twenty-five years after the withdrawal, the Mongol Empire reached its peak with the establishment of the Yuan dynasty – making it the largest land empire in history, stretching from the Sea of Japan to the Mediterranean Sea and the Carpathian Mountains.
Thus, pastoralists could only develop complex levels of organisation when facing strong agricultural neighbours.
But this cannot explain how pastoralists transitioned from small, kin-based groups to complex stratified societies.
COMPLEXITY’s central thesis is that before large-scale conflict is even possible, a level of within-group cooperation must be present.
Noteworthy, it is almost impossible for pastoralists to survive without cooperative labour investment and help from other households
By viewing cooperative herding groups as the building blocks of nomadic societies, COMPLEXITY aims to increase our understanding of the evolution of political complexity based on a new theoretical explanation of pastoral political organisation.
Structure
COMPLEXITY adds to state of the art through three steps.
While cooperative herding has been documented, previous studies have been based on single case studies.
The preliminary extent of cooperative herding groups.
Evidence is also fragmented, and little systematic attempts have been made to understand general patterns of pastoral cooperation.
The first step of COMPLEXITY is thus to cross-culturally analyse and document the prevalence of cooperative herding groups by using the existing ethnographic literature and a cross-cultural database
This will be used to select four field sites in Africa and Inner Asia:
Study design where the overall starting point is to select two communities at two sites within each region. Arrows indicate levels of comparison undertaken in the project: between regions; between sites; and between communities. Coloured areas on the map indicate the already documented presence of herding groups while animal figures indicate the traditional Old World nomadic pastoral zones defined by the key cultural animal
Cooperation, performance and the rise of pastoral inequality
Understanding cross-cultural diversity and patterns in behaviour is a central goal of human behavioural ecology.
Nevertheless, the predominant view of cooperation is shaped by studies focusing on food sharing among foragers.
A conceptual overview of the domain, focus, problem, mechanisms and research load in evolutionary aspects of cooperation in anthropology. Food sharing has been a focus because it carries a cost for the giver: the giver must share parts of their food without knowing if the action will be reciprocated. Thus, sharing is a collective action dilemma, i.e., a situation where free-riders can thwart cooperation. Sharing labour is not riddled with the same dilemma: it is mutually beneficial and, thus, represent a coordination problem. Since individuals who share labour have common interests and share preferences, they always benefit from cooperation. Also referred to as mutualism, coordination has been argued to be a better representation for many situations of human cooperation. Nevertheless, they have been viewed as less interesting and trivial than collective action dilemmas: when everyone benefits from collective action, the cooperative solution should be obvious.
In contrast, less focus has been placed on cooperative production, the primary form of cooperation among pastoralists.
Consequently, COMPLEXITY’s second step is to use field studies to comparatively investigate to what degree pastoral cooperation is structured by evolutionary factors and investigate how cooperation affects pastoral performance.
The evolution of political complexity: from small-scale cooperative group to empires?
There is also a view that livestock, as the primary source of wealth, limits the development of inequalities, making pastoralism unable to support complex organisations.
However, the Gini coefficient for reindeer in Norway indicates that wealth in livestock is more unevenly distributed than for Norway in general (see this preprint https://doi.org/10.31235/osf.io/zv92t).
Temporal trends in wealth inequality measured as reindeer numbers for (A) the Saami reindeer husbandry in Norway and (B) the Saami reindeer husbandry in the North and South (Fig 1.). The Gini coefficient ranges from 0 (perfect equality; everyone owns equally) to 1 (perfect inequality; one individual owns everything). Data for Gross Domestic Product (GDP) for Norway downloaded from Statistics of Norway (https://www.ssb.no/). See preprint https://doi.org/10.31235/osf.io/zv92t for details.
Since we cannot observe the history of nomadic empires, COMPLEXITY’ will model if, for example, livestock as wealth can generate inequalities resulting in hierarchical power structures.
The third step is thus to combine empirical data with Agent-Based Modelling, to investigate whether cooperative herding groups can be considered prototypes for more complex organisations.
The funding makes it possible to hire 2 postdocs and 2 Phds working alongside me in Tromsø!
I usually publish a short summary on this blog, but recently I’ve been learning to analyze text using Python so I thought I should try to leverage Python to help me summarize my own paper.
The result? Have a look (I’ve only removed citations and reorganized the sentences for flow):
Background
The Sami – both pastoralists and hunters – in Norway had a larger unit than the family, i.e., the siida.
History
Historically, it has been characterized as a relatively small group based on kinship.
The siida could refer to both the territory, its resources and the people that use it.
The core institutions are the baiki (household) and the siida (band).
Names of siidas were, in other words, local.
Moreover, it was informally led by a wealthy and skillful person whose authority was primarily related to herding.
One of these groups’ critical aspects is that they are dynamic: composition and size change according to the season, and members are free to join and leave groups as they see fit.
Results
Only two herders reported to have changed summer and winter siida since 2000.
Furthermore, while the siida continues to be family-based, leadership is becoming more formal.
Nevertheless, decision-making continues to be influenced by concerns of equality.
Code
The code is shown below. Lacks a bit in comments, but should work for documents. I’ve load the text used from a docx file.
Imports
import numpy as np
import os
import sys
import nltk
from nltk.corpus import stopwords
import re
import textacy # have installed spacy==3.0 and textacy==0.11
import textacy.preprocessing as tprep
import docx
import networkx as nx
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
import os
import warnings
warnings.filterwarnings("ignore")
stop_words = nltk.corpus.stopwords.words('english')
wtk = nltk.tokenize.RegexpTokenizer(r'\w+')
wnl = nltk.stem.wordnet.WordNetLemmatizer()
Loading file
doc = docx.Document('your_filename.docx')
Functions for processing and summarizing text
def extract_text_doc(doc):
paras = [p.text for p in doc.paragraphs if p.text]
revised_paras = [p for p in paras if len(p.split('.')) >1]
text = " ".join(revised_paras)
return text
def normalize_document(paper):
'''
Tokenize ++
'''
paper = paper.lower()
paper_tokens = [token.strip() for token in wtk.tokenize(paper)]
paper_tokens = [wnl.lemmatize(token) for token in paper_tokens if not token.isnumeric()]
paper_tokens = [token for token in paper_tokens if len(token) >2]
paper_tokens = [token for token in paper_tokens if token not in stop_words]
doc = ' '.join(paper_tokens)
return doc
def normalize(text):
'''
Normalizes text, string as input, returns normalized string
'''
text = tprep.normalize.hyphenated_words(text)
text = tprep.normalize.quotation_marks(text)
text = tprep.normalize.unicode(text)
text = tprep.remove.accents(text)
return text
def text_rank_summarizer(norm_sentences, original_sentences, num_sent):
if len(sentences) < num_sent:
num_sent -=1
else:
num_sent=num_sent
tv_p = TfidfVectorizer(min_df=1, max_df=1, ngram_range=(1,1), use_idf=True)
dt_matrix = tv_p.fit_transform(norm_sentences)
dt_matrix = dt_matrix.toarray()
vocab = tv_p.get_feature_names()
td_matrix = dt_matrix.T
similarity_matrix = np.matmul(dt_matrix, dt_matrix.T)
similarity_graph = nx.from_numpy_array(similarity_matrix)
scores = nx.pagerank(similarity_graph)
ranked_sentences = sorted(((score,index) for index, score in scores.items()))
top_sentence_indices = [ranked_sentences[index][1] for index in range(num_sent)]
top_sentence_indices.sort()
summary = "\n".join(np.array(original_sentences)[top_sentence_indices])
return summary
Historically, it has been characterized as a relatively small group based on kinship.
Moreover, it was informally led by a wealthy and skilful person whose authority was primarily related to herding.
Only two herders reported to have changed summer and winter siida since 2000.
Furthermore, while the siida continues to be family-based, leadership is becoming more formal.
Nevertheless, decision-making continues to be influenced by concerns of equality.
One of these groups' critical aspects is that they are dynamic: composition and size changes according to the season, and members are free to join and leave groups as they see fit.
Lowie (1945) writes that the Sami – both pastoralists and hunters – in Norway had a larger unit than the family, i.e., the siida.
Names of siidas were, in other words, local.
The siida could refer to both the territory, its resources and the people that use it (see also Riseth 2000, 120).
The core institutions are the baiki (household) and the siida (band).
For more options and better ways of summarizing text, check out https://pypi.org/project/sumy/ with its many summarizer classes. For example, TextRankSummarizer is similar (but way better) than the approach taken here.
But similarly, it conceptualizes the relationship between sentences as a graph: each sentence is considered as vertex and each vertex is linked to the other vertex. But, rather than using PageRank from networkx for similarity, it uses Jaccard Similarity.
As I wrote in my previous post, my recent grant application submitted earlier this year to the Norwegian Research Council was rejected.
I have no problem with a reject (well, I actually do since it is a kick-ass project!). I try to follow Paul J. Silvia’s advice in the book “How To write a Lot” concerning rejections:
To write a lot, you should rethink your mental models of rejections and publications. Rejections are like a sales tax on publications: The more papers you publish, the more rejections you receive (p. 100-101).
What I question this year is the application’s evaluation and what it entails concerning the referees (and thus the Research Council of Norway) views of “proper” anthropology.
While the project is hailed as promising and innovative due to its comparative approach, it fails because it uses a quantitative approach instead of participant observation. To quote the evaluation:
…the lack of qualitative methods such as participant observation, for example, are major problems.
In effect, the evaluation seems to indicate that, in 2020, it is still not appropriate for anthropologists—at least for the panel evaluating the proposal—to collect quantitative data.
The proposal uses a methodological approach with demonstrable success in investigating cooperation—namely, experimental economic games and social network analysis.
Moreover, it is pointed out that the analyses stipulated are well thought through but:
Yet, it is not credible that the complexity of social relations – not to speak or norms, behavior and the relation between the two – can be explored through experiments and through statistical analysis with a limited number of variables
So what I actually think of as one of the significant strengths of quantitative analyses, i.e. the operationalising of critical variables and relationships based on specific research questions, is, in fact, a weakness because it simplifies the complexity of a given culture.
Yes, it does simplify complexity. This is not a shortcoming; it is a strength if your aim is to look at similarities and differences between and within societies and cultures.
Suppose you’re on the other hand, concerned with the underlying ideology, cosmology, beliefs, cultural understanding of cooperation. In that case, participant observation is a useful tool.
More to the point, a quantitive approach forces you to be precise with assumptions and research questions. A level of precision not necessary for a qualitative approach.
I have no problem with disagreeing with a choice of method. After all, methods should follow the research question(s).
What I object to is the kind of philosophical pre-commitment to a specific tool to be used by ALL anthropologists that the evaluation seems to imply.
While an n = 1 does not lend a lot of credibility to my position, this is precisely the point.
My experience might not at all be representative.
It does not tell a full story of how the research Council of Norway evaluates proposals because no similarities or variations can be gleaned from it.
But then again, this is precisely the kind of evidence participant observation is good at accessing, namely showing detailed, but yet anecdotal, information about any given topic.
From my point of view, this evaluation shows a cognitive bias often referred to as Maslow’s hammer:
‘I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail’.
Research questions should guide the use of the method; you should not be limited by tacit understandings of a discipline’s “proper” technique. Anything else leads to imperfect science and shoddy scholarship.
Next deadline is the 10th of February. I might resubmit…
Or I might wait until the wind changes direction and numbers are not viewed as anathema to anthropology (if ever).
Because, the fundamental problem for me is that while rejections are the norm when doing science, IT is hard to see how to revise this proposal to give it a higher chance of success without changing the underlying idea!
However, I’ve managed to successfully secure 3 grants from the Norwegian Research Council before, so it is not out of reach.
Luckily, the paper got published on the same day as I got grant applications rejected from the Research Council of Norway.
Why bring that up? Because the paper and one of the grant applications have some obvious parallels.
The paper documents why cooperation is vital for Tibetan pastoralists: it increases control of herds, reduces individual household’s labour demand and increases the potential for economic diversification.
Tibetan herder bring a subset of the herd back to camp for the night. Photo (C) Marius Warg Næss
The grant application extends this aspect: it wants to comparatively investigate pastoral cooperation by taking as its starting point that cooperative herding groups are a necessary component of pastoral life.
The paradox is that while it is almost impossible for pastoral households to maintain production without cooperative labour investment and mutual help from other households, a comparative perspective of cooperative herding group formation is currently lacking.
Moreover, while cooperation is widely documented, it is treated in a somewhat ad hoc fashion. For example, it has been used as an explanation for why there is no relationship between household labour investment and pastoral production (see this paper).
Furthermore, herding groups have been described as fluid: changing composition from year to year and between seasons. Thus, they have been viewed as less important than more extensive and more political, groupings.
My personal point of view that despite the instability of herding units, they represent an essential building block of nomadic societies because they are concerned with daily cooperation.
More to the point, we know relatively little about them: what evidence we have represent a snapshot in time. We lack longitudinal data as well as data concerning how cooperation is changing as a consequence of changes in land tenure.
While getting grant rejections sucks, I still think this is a worthwhile project that I’ll pursue.
Anyone interested in collaborating on such a project is welcome to contact me.
A recent paper argues that climate is more important than density in the reindeer husbandry in Norway. Using the same analysis, I find that reindeer density is essential: In high-density environments, average varit (1.5-year-old bucks) carcass weight is 8 kg lower, and calf carcass weight is 4 kg lower compared to low-density environments.
In short, the paper aims to challenge the official view of overstocking and reframe reindeer herding in terms of non-equilibrium ecology.
Their focus of attack is the Røros model which, according to the authors, hinges on
“… classic ecological equilibrium models where there is a clear unequivocal relationship between animal densities, production, and carcass weights”
p. 15
As such the article fits nicely in a growing trend: rather than investigating problems currently facing pastoralists, the main point is to establish systems as non-equilibrium, and thus all issues are assumed resolved, or at least externally caused (for an excellent example from the reindeer husbandry in Norway, check out ‘Conceptualising resilience in Norwegian Sámi reindeer pastoralism‘).
In another paper, some of the same authors have, for example, argued that reindeer herding in Norway is better characterised as a non-equilibrium system
“…where herbivore populations fluctuate randomly according to external influences, [and] the concepts of carrying capacity and overgrazing have no discernible meaning”.
‘Productivity beyond density’ goes at least further in attempting to quantify the relative importance of non-equilibrium factors (such as climate) and equilibrium factors (such as density).
While the paper is well-written and exciting, I find it a bit strange that in the only quantitative analysis they present the sole focus is on statistically significant effects of precipitation and temperature for the carcass weights of reindeer:
While the analysis shows indeed that climate factors (precipitation: all the daily observation in the stated period and growing degree days [GDD]) are significant, the discussion of the table completely fails to address two critical factors:
They never discuss whether the variables measuring climate is correlated or not (as they are monthly based, it wouldn’t be a huge surprise if they are).
High or even moderate, collinearity is problematic when effects are weak (as the climate effect sizes indicate). If collinearity is ignored, it is possible to end up with a statistical analysis where nothing is significant, but were dropping one predictor may make others significant, or even change the sign of estimated parameters.
The point is technical; it would be interesting to see how these potential problems were accounted for.
Concerning effect size, the most substantial effect by far is that of density: -0.16 kg for calves and -0.32 kg for varit (1.5-year-old bucks).
In effect, this has a considerable impact on the carcass weights in high-density vs low-density environments
Keep in mind that they do not provide information concerning variable transformation, so I take it for granted that the intercept represents average carcass weights when every other variable is at zero. I also take for granted that all variables are continuous. Moreover, not all of the data was in the supplemental material so I couldn’t re-analyse the data properly.
In short, at density 0 average calf carcass weight is 18.52 kg and average varit carcass weight is 25.34 kg.
The paper does not indicate the range of density utilised in this analysis, but Fig. 5 presents the range for mainland districts (which are the same districts used in table) to be from 0 to 25.
Disregarding the climate parameters (since there are no interactions and the range of the climate parameters are not presented) density has a significant effect in high-density environments:
Calves: 18.52 – 0.16 X 25 = 14.52 kg
Varit: 25.34 – 0.32 X 25 = 17.34 kg
In short, the model in Table 1 predicts that the difference in varit carcass weight between a low-density environment and a high-density environment is 8 kg. For calf carcass weight, the model predicts a difference of 4 kg.
While I fully agree with the authors that an over-emphasis on density and herd size is too simplistic when modelling pastoral production, it is bizarre that the above is not communicated at all.
Part of the problem, I think, stems from the simplified representation of non-equilibrium ecology. Concerning Africa and Asia, for example, they write “…a wholesale paradigm shift from equilibrium to non-equilibrium modelling took place from the early 1990s” (p. 9).
He mainly focused on periodic events such as droughts (a density-independent factor) and more persistent factors such as herd size (a density-dependent factor)
In other words, he investigated the degree to which density-dependent and density-independent factors explained herd size fluctuations (data for 60 years).
In short, he found:
In years with high precipitation, the population of cattle approaches a ceiling, which he terms the carrying capacity. As density increases, the birth rate drops, and mortality rates increases (although they never reach equilibrium and the cattle population never reaches its theoretical maximum).
The cattle population never reaches a maximum because stochastic events such as droughts occur and kill off large parts. Noteworthy, the number of animals killed by these events was more substantial than what can be predicted from density-dependent factors alone.
In the long term, it thus looks like non-equilibrium factors have the most significant impacts on cattle populations. Still, equilibrium factors are essential in years without stochastic climatic effects and when the population is high.
Scoones’ investigation show what seems now to be forgotten:
It is unlikely that any system is characterised by either equilibrium or non-equilibrium factors alone, but rather that they both operate on a continuum.
“To understand the effects of climate change on nomadic pastoralists, it is thus necessary to move beyond the simplistic dichotomy of characterising pastoral system as equilibrial (density dependence: livestock and pastures are regulated by grazing pressure) or non-equilibrial (density independence: livestock and pastures are limited by external factors such as climate) and look at the interplay between density dependent and density independent factors”
The debate about reindeer husbandry in Norway is characterised by two contrasting views.
On one hand is the prevailing view of overstocking and rangeland degradation.
On the other hand, is the view that overstocking and overuse represents a misreading of the Arctic landscape that perpetuates a dominant crisis narrative that functions as “… an enduring ‘social fact’, whose narrative reality is in large part decoupled from its supposed scientific basis” (Benjaminsen et al. 2015:228).
While the overstocking perspective is based on a presumed ‘Tragedy of the Commons’, the other perspective argue that reindeer herding is characterised as a non-equilibrium system
“…where herbivore populations fluctuate randomly according to external influences, [and] the concepts of carrying capacity and overgrazing have no discernible meaning” (ibid.:223).
Through a comparative historical analysis, I argue that herding is better viewed as an assurance game with two different strategies for minimising risk:
maximising quantity (i.e., increasing livestock numbers or herd size)
maximising livestock quality (i.e., increasing livestock body mass)
I demonstrate that intra-group competition has led to the adoption of (1) in the Northern parts of Norway, while inter-group competition has led to the adoption of (2) in the Southern parts.
In Scandinavia there is a growing concern that the reindeer husbandry is in a state of crisis, but results from our recent study indicates that the Swedish reindeer husbandry is in fact in better condition now compared to the past (1945-1965).
Pastoralism, Climate Change and Policy 