In connection with the ERC funded project “From small-scale cooperative herding groups to nomadic empires – a cross-cultural approach, a workshop will be arranged on Thursday 6th of November in Tromsø, Norway.
Time: Thursday November 6 10:00-14:00
Room: “Ny- Ålesund” 2nd floor, Fram Centre, Tromsø, Norway.
There will also be possible to attend the workshop on Teams, the link is provided in the program below.
Guro and Wurihan launched fieldwork for COMPLEXITY, focusing on Mongolian herders. Guro maps social networks and uses experimental games to study cooperation, while Wurihan examines animal health and cooperation in handling disease outbreaks. They are now heading to Övörkhangai Province for interviews.
July 1st this year fieldwork was officially launched for COMPLEXITY.
Guro, a PhD-student on the project, and Wurihan, a postdoc on the project started their journey towards Mongolia and their research among herders.
Thy started their journey from the capital Ulaanbaatar where they met up with local connections and networks.
The focus of their projects are somewhat different, yet interlinked through the lens of cooperation.
Guro’s research will include mapping social networks and experimental games. Experimental games are a widespread research method to investigate human behavior, such as cooperation.
By combining these approaches, Guro will be able to examine the importance of kinship, reciprocity, network position and social environment for those the herders choose to cooperate with.
Wurihan, is more focused on using participant observation to explore the role of animal health and how cooperation affects handling of the outbreak of animal diseases.
After a couple of days in Ulaanbaatar, they set their course towards herding communities.
They are travelling to Övörkhangai Province, which among other things have the location of the ancient city of Karakorum.
On their way, they also stopped by the Erdene Zuu Monastery, which is the oldest surviving Buddhist monastery in Mongolia.
Construction of this monastery started in 1585 after a meeting with the 3rd Dalai Lama and the declaration of Tibetan Buddhism as the state religion of Mongolia.
Some pictures below, all taken by Guro and Wurihan (C).
Stay tuned for more updates!
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.
Just got a paper published in Humanities & Social Sciences Communications.
The paper is open accessed and can be found here.
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).
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.
You can read the full paper here.
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 compare the median reindeer number (the number of reindeer a herder right in the middle of the distribution has) with the tenth-percentile reindeer number (the number of reindeer someone near the bottom of the income distribution has).
If the 50/10 ratio shrinks it means that poorer households are catching up with the ‘middle-class’.
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.
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!
The ramifications are mind-blowing!!
Earlier this year I got an article published in Acta Borealia.
The paper, The Sami cooperative herding group: the siida system from past to present, is open access.
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):
The Sami – both pastoralists and hunters – in Norway had a larger unit than the family, i.e., the siida.
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.
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.
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.
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()doc = docx.Document('your_filename.docx')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 summarynormalize_corpus = np.vectorize(normalize_document)
text = extract_text_doc(doc)
text1 = normalize(text)
sentences = nltk.sent_tokenize(text1)
norm_sentences = normalize_corpus(sentences)
summary = text_rank_summarizer(norm_sentences, sentences, 10)
print(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.
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.
A recent paper, ‘Productivity beyond density: A critique of management models for reindeer pastoralism in Norway’, published in Pastoralism: Research, Policy and Practice sets out to investigate the validity of the premise that there is a strong relationship between density and carcass weights in the reindeer husbandry in Norway.
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”.
‘Misreading the Arctic landscape: A political ecology of reindeer, carrying capacities, and overstocking in Finnmark, Norway’, p. 223
‘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).
This is in fact, only partially true.
In the chapter ‘Why are there so many animals? Cattle population dynamics in the communal areas of Zimbabwe’, Ian Scoones, for example, investigated factors affecting herd growth among Zimbabwean pastoralists.
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 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.
This supports the predominant ecological perspective that at high population sizes, herbivores are sensitive to a combination of density-dependent and -independent factors, which has been shown for reindeer in Norway.
As I argued in the paper ‘Climate Change, Risk Management and the End of Nomadic Pastoralism’.
“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”
p. 131
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).
In my new paper, Cultural Group Selection and the Evolution of Reindeer Herding in Norway, I argue differently.
Through a comparative historical analysis, I argue that herding is better viewed as an assurance game with two different strategies for minimising risk:
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.
Read the full paper here.
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).
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 better condition now compared to the past (1945-1965). Continue reading “The pursuit of populations collapses: long-term dynamics of semi-domestic reindeer in Sweden”
People rely on one another in fundamental ways, but cooperation in groups can be fragile. Every day, we face tensions between acting in a socially responsible manner and following our own self-interest. These situations are called social dilemmas and they come in varying shades of subtlety, from littering and eBay to overpopulation and climate change. Overcoming these dilemmas can make all the difference, especially for marginalised groups such as pastoralists – people who make their living from herding animals.
Pastoralists use about a quarter of the world’s land for grazing their herds. Nowadays, all over the world, governments are privatising many of their pastures, and so herders must work together in increasingly fragmented places.
We wanted to learn how groups of Saami reindeer herders living in Norway’s Arctic Circle worked together. Our study, just published in the journal Human Ecology, found that cooperation pivoted around the ‘siida’: a…
View original post 420 more words
I’ve just got a paper accepted in Land Use Policy about nomadic pastoralists in Tibet and hunting. As we all know, space is limited in scientific journals, so here is additional text as well as pictures. Continue reading “Tibetan lives: Hunting”
Pastoralism, Climate Change and Policy 