This is absolute gold:
This is absolute gold:
Coastal West Africa and Nigeria, the Great Lakes region, the Ethiopian highlands, the Nile Delta and the Mediterranean coast pack half of Africa’s population.
Source: interesting maps
The Daily Maverick has an excellent piece on how the ongoing insurgency in northern Mozambique may be reshaping the illicit trade industry in the country:
The most reliable reports of the insurgents developing an illicit income stream are linked to the heroin trade. There is a significant range in street-level heroin prices across East and Southern Africa. The range in prices in northern Mozambique – far greater than found in any other research site – reflects the variance in heroin quality available in Cabo Delgado that we also found during qualitative fieldwork in the region…
There has been a significant recent shift in the rhetoric and style of attacks committed by the Cabo Delgado insurgents. Rather than terrorising communities as in previous months, they are instead attacking state infrastructure and military bases. They have used their increasingly vocal media campaign to declare their intentions to create a caliphate. Analysts we interviewed suggest that part of the insurgents’ aim is to re-establish control over areas historically controlled by Muslim sultanates along the Swahili coast. This historical claim would play into the caliphate narrative and the group’s claim of legitimacy.
If this territorial control were achieved – along the coast from Quissanga to Palma as well as on the key inland transport corridor along the N380 road and the town of Macomia – this could vastly change the dynamics of the insurgency.
Control over key sea and land routes would allow the insurgents to “tax” legal and illicit economies in the region more systematically. While there may already be some protection of heroin trafficking and involvement in the gold and ruby trade, this could expand to include human smuggling, timber trafficking and possibly a share of the illegal wildlife trade.
The insurgents’ access to Mozambique’s illicit trade networks is an ominous development. Taxation of the drug trade and access to point resources like gold will likely boost the insurgents’ staying power and capacity for violence, while also weakening their dependence on local populations. That probably means more civilian deaths.
This is from a new paper by Alicia Barriga and Nathan Fiala in World Development:
Results from the tests showed very high levels of DNA similarity (above 98%) and good performance in general, but highly variable quality in terms of the ability of the seed to germinate under standard conditions. We do not see differences in average outcomes across the distribution levels, though variation in seed performance does increase further down the supply chain.
The results of the tests point to potentially important issues for the quality of seeds. The variation in germination suggests that buying a random bag of seeds in this particular distribution chain can matter a lot for farmer’s production. The high rate of seed similarity suggests that the main concern among policy makers and researchers, that sellers add inert or low-quality material to the seeds, is likely not the case, at least for the maize sector in the districts we study. However, given the remoteness of these districts and the lack of any oversight in these areas, we believe the results are likely a lower bound for the country as a whole.
The supply chain analysis suggests that the quality of seed does not deteriorate along the supply chain. The quality is the same, on average, across all types of suppliers after leaving the breeders. However, we observe high variation of seeds’ performance results on germination, moisture, and vigor, suggesting that results are more consistent with issues of mishandling and poor storage of seeds, possibly related to temperature or quality controls, rather than sellers purposefully adulterating seeds. Variation on these indicators is usually associated with mishandling during transportation and storage.
As the authors note in the paper, African governments and their external donors have put a lot of effort in “certification and labeling so as to reduce the possibility of adulteration by downstream sellers”. Obviously, e-labels and systems of verifying seed authenticity in the fight against adulteration are important. But equally important is an understanding of how the seed distribution system works. And that is one of the major contributions of this paper. Corruption is not always the problem.
Interestingly, Uganda bests both Kenya and Tanzania on productivity in the cereal sector (I made the graph using FAO data). Despite starting off with relatively lower productivity and having gone through civil conflict beginning in the late 1970s, Uganda has since around 2007 clearly separated itself from both Kenya and Tanzania (and appears to have plateaued). Productivity in Kenya peaked in the early 1980s and has pretty much stagnated since. Tanzania’s figures appear to be trending upwards having collapsed in the early 2000s. There is likely an element of soil quality and general aridity involved in these trends. According to the FAO, Kenya and Tanzania use fertilizer at significantly higher rates than Uganda. For comparison, cereal yield in Vietnam is about 2.7 times higher than in Uganda.
Thandekile Moyo has as great piece over at Africa Portal on life after 40 years of independence in Zimbabwe. Ian Smith’s Rhodesia was swept into the dustbin of history on April 18th, 1980. Since then Zimbabwe has gone through a lot, as vividly described by Moyo. Overall, Zimbabwean elites have consistently betrayed the ideals of the Second Chimurenga over the last 40 years.
Who are the “born frees”?
They call those of us born in Zimbabwe and after 1980 “bornfrees”. We are the “lucky” generations, the generations that do not know the heartbreak and terror of war, generations that know nothing about the indignity and injustice of racism, nothing about the brutality of domination and white supremacism and the helplessness of poverty. We know nothing about suffering, we were born free.
On the economy:
Many “bornfrees” in Zimbabwe still live with their parents. Forty-year-old men and women who should by now have built their own homes are stuck at home because we cannot afford to move out. Most Zimbabweans are either unemployed, underemployed or living from hand to mouth. The Government is the biggest employer and pays an average of ZW$2,500/month (USD$70).
On the provision of essential public services:
In spite of the novel coronavirus (COVID-19) pandemic, Zimbabwe’s Vice President Constantino Chiwenga flew to China in March because that is where he receives his healthcare. Zimbabwean leaders do not use Zimbabwean hospitals. When then Vice President Mnangagwa was suspected to have been a victim of poisoning at a rally in 2017, he was airlifted to South Africa for treatment. When Vice President Kembo Mohadi fell ill in 2019, he too was flown to South Africa. This is the legacy left by Robert Mugabe who himself eventually died at a hospital in Singapore.
On the Zimbabwean state’s approach to competitive electoral politics:
When Matebeleland resoundingly voted for ZAPU in the 1980 elections the “Black Government” responded by arresting ZAPU leaders and murdering 20,000 of their supporters in a genocide known as Gukurahundi.
Such torture and murder of opposition supporters have continued over the years. Just last year, a comedian, Samantha Kurera was abducted by suspected state agents and tortured for producing skits considered to be anti-government.
What is there to celebrate about Zimbabwean independence?
Zimbabwe turns 40 on 18 April. Growing up, Independence Day was a major deal. With age though, I find myself disoriented and struggling to comprehend what exactly there is to celebrate. What does it mean to be independent? Bornfree?
Free of what? Free from what? Free to do what?
This is the abstract and excerpts from Andayi, Chaves, and Widdowson, a paper focusing on the impact of the Spanish flu on coastal Kenya:
The 1918 influenza pandemic was the most significant pandemic recorded in human history. Worldwide, an estimated half a billion persons were infected and 20 to 100 million people died in three waves during 1918 to 1919. Yet the impact of this pandemic has been poorly documented in many countries especially those in Africa. We used colonial-era records to describe the impact of 1918 influenza pandemic in the Coast Province of Kenya. We gathered quantitative data on facility use and all-cause mortality from 1912 to 1925, and pandemic-specific data from active reporting from September 1918 to March 1919. We also extracted quotes from correspondence to complement the quantitative data and describe the societal impact of the pandemic. We found that crude mortality rates and healthcare utilization increased six- and three-fold, respectively, in 1918, and estimated a pandemic mortality rate of 25.3 deaths/1000 people/year (emphasis added). Impact to society and the health care system was dramatic as evidenced by correspondence. In conclusion, the 1918 pandemic profoundly affected Coastal Kenya. Preparation for the next pandemic requires continued improvement in surveillance, education about influenza vaccines, and efforts to prevent, detect and respond to novel influenza outbreaks.
We noted, that in 1918, the crude death rates and healthcare utilization drastically increased, six- and three-fold, respectively and stayed relatively high until at least 1925. The sharp increase in health care utilization was certainly due to the pandemic and is corroborated by the anecdotal reporting of overwhelmed health systems. The very large majority of these cases would have been in the native population, though we had no data on race. The higher rates of mortality and facility visits after 1918 compared to before 1918 were likely due to improved reporting health facility expansion rather than prolonged pandemic transmission. Equally, it is plausible that several documented outbreaks such as the plague (1920) and smallpox (1925), also contributed to high reported mortality and morbidity in those late years studied. We estimate pandemic mortality from September 1918 to March 1919 to be approximately 25 deaths/1000 population and morbidity at 176/1000 population or an attack rate of 17.6% (emphasis added).
Writing over at The Conversation, Andayi notes that overall the flu might have killed as many as 150,000 people in the Kenya Colony, or 4-6% of the population at the time. The Spanish flu (which actually probably originated in New York) could have killed anywhere between 1-5% of the global population.
The Spanish flu is believed to have come to Kenya with returning veterans who docked in the Mombasa port. The country was still a British colony at the time. In nine months the epidemic killed about 150,000 people, between 4% and 6% of the population at the time.
COVID-19 is nowhere near these mortality rates. The estimates I have seen (which for some reason are for “Africa” and not individual countries) suggest that between 300k and 1.3m people might die of COVID-19 on the Continent (see image with UNECA estimates). Proportionately, that would mean roughly between 12k – 51k Kenyans, or .03-.01% of the population (still absolutely catastrophic figures).
If you know of any country-level estimates please share in the comments.
A number of papers (on agricultural productivity, conflict, food security, and impacts of climate change, for example) use cropland cover data as controls. How good are these data?
Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency.
The results show that total crop-land area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODISCollection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale.We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate(Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.
Here’s what they did:
…. we compared the spatial agreement of cropland to assess the consistency of five datasets in the same location. These datasets were overlapped to generate a new composite map revealing whether and where the original datasets agreed on the same locations (Yang et al., 2017). Pixels of the composite map were assigned values ranging from 0 to 5. The highest value 5 represents the complete agreement, where all five datasets were consistent in cropland identification for a pixel. As the value decreases, spatial consistency between these crop-land datasets decreases. The lowest value with value 1 means that only one dataset identifies the pixel as cropland.
The best consistency of five datasets occurs in Egypt, with the complete agreement value of 47.86 %, while the highest disagreement is in Western Sahara, whose spatial disagreement is 91.08 %.