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The Myth of a Food Crisis

  • The Bioscience Resource Project


Previously I have suggested that the fate of the global food chain rests on a moral question. Customers will either come to believe that sustainable organic local and ethical (SOLE) food can feed the globe, or they will acquiesce to the narrative of the industrialised food system on the decisive grounds of moral necessity–that only pesticides, corporate concentration, and GMOs can forestall mass starvation. The defining assertion in this space is the claim of a potential global food production deficit: the supposed challenge of 'feeding the 10 billion'. The crisis narrative has been led by the UN's Food and Agriculture Organisation (FAO) whose mathematical modelling of the food system has predicted a 70% increase (now 60%) in the annual amount of food needed by 2050. By carefully examining the assumptions underlying its modelling, however, I show here that FAO modellers have consistently underestimated global food supplies and consistently overestimated food demand. But, as shown by its ability to adjust to the biofuel boom, the food system contains massive amounts of under-utilised capacity which goes uncounted by current quantitative models.
Rethinking Food and Agriculture.
© 2021 Elsevier Inc. All rights reserved.
The myth of a food crisis
Jonathan Latham
The Bioscience Resource Project, Ithaca, NY, United States
5.1 Introduction
World Agriculture Towards 2030/2050 is a major report predicting global agricultural
trends (Alexandratos & Bruinsma, 2012). It was produced by the economics division
of the UN Food and Agriculture Organization (FAO). In its abstract the FAO authors
make a prominent disclaimer. Its projections, they stress (both on p. i and p. 7), are
not to be used for normative purposes; that is, their report is not a prescription of how
the global food system should develop. It is merely an exploratory model; their most
reliable projection of business as usual (Alexandratos & Bruinsma, 2012).
In all probability this disclaimer resulted from the intense global attention that its
predecessor (FAO, 2006) received. This “interim report” was cited across the globe
as claiming that the world must produce 70% more food by the year 2050. This 70%
number (sometimes even adjusted to a “doubling”) was almost invariably recruited
to bolster a number of technological modernizing agendas for agriculture, for in-
stance, in the promotion of genetically modified crops. Thus the UK’s chief scientist
in 2009 predicted an imminent “perfect storm” of climate change and food shortages
(Beddington 2009). Similar analyses were repeated in scientific articles, in generalist
publications such as the Economist magazine, and by agribusiness (Peekhaus, 2010;
Tomlinson, 2011; Stone & Glover, 2011).
Thus FAO’s number was repeatedly taken out of context and presented as a grand
challenge requiring special efforts or difficult compromises. That is, it was used nor-
matively. Those citing FAO may not have said that:
The battle to feed all of humanity is over. In the 1970s the world will undergo
famines—hundreds of millions of people are going to starve to death in spite of any
crash programs embarked upon now. (The Population Bomb, Paul Ehrlich, 1968).
However, the implications were comparable.
But this is not merely a simple story of statistics being taken out of context. In 2016,
the same FAO department described in more detail their modeling system (in 2012 it
was renamed the Global Agriculture Perspectives System, or GAPS) used to derive
the original prediction (Kavalleri etal., 2016). Drawing attention, in a clearly norma-
tive fashion, to its newest quantitative prediction the authors wrote: “A key finding…
is that world food production should increase by some 60% from 2005/07 to 2050”
(Kavalleri etal., 2016, p. 1, emphasis added). By contradicting their colleagues’ pre-
vious disclaimer, Kavalleri etal. raised the issue, since FAO is a stakeholder in the
food crisis narrative, of whether the original disclaimer was sincere and whether more
could not have been done to avoid the normative usages of FAO numbers.
94 Rethinking Food and Agriculture
The answer given in this chapter supports the analysis of Tomlinson that a “slide”
is operating in many of the texts written by FAO, and that this slide is particularly
problematic in texts written by FAO leadership (cited in Tomlinson, 2011). Given that
FAO oscillates between normative and nonnormative statements, this slide might be
better called a “shuffle,” but it embodies perfectly the central paradox of all quantita-
tive models of the global food system, whether produced by FAO or by others. This
paradox is that, though FAO modeling supposedly exists to “identify challenges in
world food and agricultural sectors and to offer strategic policy perspectives” in an
unbiased fashion (Kavalleri etal., 2016, p. 1), what GAPS does in practice is quan-
tify food. This frames agriculture as primarily a question of production. The focus
on production, no matter any disclaimer, is normative, because it marginalizes issues
of poverty and access to food, ecological costs, and social costs. These are either
unexamined or subsidiary. So while the titles of FAOs reports and models are broad,
e.g., World Agriculture: Towards 2015/30, the focus is narrowly on the quantification
of production, even though, according to the International Assessment of Agricultural
Knowledge, Science and Technology for Development (IAASTD), the truly “key”
questions swirling around agriculture are not about productivity (IAASTD, 2009).
Productivity, concluded the IAASTD report, is a distraction. As Robert Watson, chair
of IAASTD, told the press at its launch, in agriculture, “Business as usual is not an op-
tion.” The real question of agriculture is: Can we feed the people without also feeding
social and ecological disasters?
But farmer suicides and insect declines, salinization, dead zones, and the pollu-
tion of water bodies and other consequences of dysfunctional agriculture are absent
from World Agriculture Towards 2030/2050. Moreover, World Agriculture Towards
2030/2050 even fails, ultimately, to show that productivity per se merits specific mod-
eling attention. First, because the model it describes predicts that any necessary pro-
duction increases will be solved by business as usual. Second, it also predicts “Modest
reductions in the numbers undernourished” by 2050 but this reduction is dependent
on continued economic growth (i.e., increases in wealth); that is, not on agricultural
production (Alexandratos & Bruinsma, 2012).
FAO’s previous iteration, World Agriculture Towards 2015/2030, had reached a vir-
tually identical conclusion. It projected that out of a then total of 850 million hungry
people, just 120 million would be lifted out of hunger if food production reached its
target increase of 70% by 2030 (Bruinsma, 2003).
So, even according to FAO’s own models, increasing production does not solve
hunger. This result mirrors the conclusion of Amartya Sen in his celebrated history
Poverty and Famines. When hunger and famine strike, he found, production shortfalls
have virtually never been the cause (Sen, 1981). To many food system commentators
this is settled beyond question (e.g., Lappé & Collins, 2015). But it is a finding that
has nevertheless been disregarded by many, including FAO leaders (Tomlinson, 2011),
who have instead commonly cited FAO in support of a scarcity narrative with its con-
sequent need for a productivity focus (e.g., Conway, 2012). Thus the scarcity view,
whose credibility rests almost purely on the findings of models like GAPS, finds, at
best, only equivocal support there.
The myth of a food crisis 95
5.1.1 Global food models and projections
The purpose of this chapter, however, is to provide a quantitative critique of models
like GAPS at the level of their underlying assumptions.
Unless otherwise noted the focus will be on FAO’s GAPS. This focus is specifically
not intended to validate the quantification of food; indeed, quantification of food as
calories and weight is detrimental to a full understanding of food systems. It is rather
an acknowledgment that FAO’s work is the most prominently cited and that, encour-
aged by FAO’s shuffle, the world has overwhelmingly interpreted this 70% number as
normative. In 2012 FAO's prediction was updated to 60%, mainly to reflect a shifting
baseline: we are now much closer to 2050 than we were in 2003 (Alexandratos &
Bruinsma, 2012). So, at the risk of appearing to validate the general approach, it is on
a purely quantitative level that these models are most transparently open to challenge.
Malthus, 1798 is considered to have made the first mathematical model of a food
system. His simple projection concluded that exponential population growth would
eventually outstrip linear supply growth. The basic form of his model, followed ever
since, was to separate food supply from food demand (McCalla & Revoredo, 2001).
Besides FAO, institutions such as the International Food Policy Research Institute
(IFPRI) have developed their own models (Robinson etal., 2015). In addition, spe-
cial projects such as the Millennium Ecosystem Assessment, the Comprehensive
Assessment of Water Management, and Agrimonde (2009) (a joint project of the
French Institut Nationale de la Recherche Agronomique and the Centre de Cooperation
Internationale en Recherche Agronomique) have extended the general method but with
an emphasis on investigating specific questions, such as water constraints, climate
impacts, and the effects of specific policy decisions (de Fraiture etal., 2007; Fischer
etal., 1988; Rosegrant etal., 1995, 2001; Parry etal., 2004; Chaumet etal., 2009).
All are intended to inform decision making. However, those more suited to exploring
diverse potential outcomes are often called scenarios. These distinctions, along with
some of the strengths and weaknesses of the models, have previously been reviewed
by Reilly and Willenbockel (2010) and by Wise (2013).
What these reviewers note, above all, is that there is overall a strong degree of con-
sistency among models and scenarios that there is no need for extraordinary measures
to enhance production. To quote FAO: “from the standpoint of global production poten-
tial there should be no insurmountable constraints” (Alexandratos & Bruinsma, 2012).
None foresees a classic Ehrlich-style crisis, unless they expressly incorporate in their
scenarios some form of mismanagement. For instance, the Millennium Assessment
has as one of its four scenarios “Order through Strength” (OS) that envisages low
cooperation and high trade barriers. Under OS conditions there is no overall global
food shortage but there is increased malnutrition and even civil war in parts of Africa.
This is a broadly reassuring conclusion, but it should nevertheless be qualified by
the looming shadow of climate change (Battisti and Naylor, 2009; Nelson etal., 2010).
Agreement that food production is unlikely to develop into a crisis situation (climate
excepted) has not banished the alarmist narrative in the media, however (see, e.g.,
Hincks, 2018).
96 Rethinking Food and Agriculture
Yet, there are grounds to suppose that even this convergence, which does predict
a need for increased production, is excessively pessimistic. In 2011 researchers from
the World Bank Institute proposed that the world already produced enough food for
14 billion people (Herren etal., 2011). This number is well above UN population pre-
dictions, which are expected to reach 10–11 billion in 2050 and perhaps even decline
thereafter (UN, 2017).
Moreover, models also contradict global food price trends. Before the 2007/2008
price spike caused by changes in US and EU biofuel policies (de Gorter etal., 2015),
food prices had been declining at approximately 4% per year. Since that spike, prices
appear to have returned approximately to that track. This long-term decline, across
every sector of agriculture, suggests strongly that food supply significantly exceeds
current food demand and that the gap is if anything widening. The exact extent of this
excess is not clear but the 2017 FAO estimate for global cereal stocks is 762 million
tons. This amount represents approximately one-third of annual global production
(FAOSTAT). Thus, independent of any modeling, there are strong grounds for suppos-
ing that even the most optimistic models are still pessimistic. They are overestimating
demand or underestimating supply, or both. The overarching questions are: How does
one reconcile low (and declining) food prices and persistent global commodity gluts
with the projections, claimed by GAPS and other models, of the need to produce more
food? Are the models flawed? If so, what are those flaws?
5.1.2 How flawed are food system models?
The use of highly complex models always raises many questions of how well they
represent reality (Scrieciu, 2007). But food system models are especially complex,
seeking as they do to integrate biophysical, social, economic, and institutional com-
ponents. Thus a criticism sometimes made of such models is their use of calories as
the measure of nutrition (e.g., Herforth, 2015). Both nutritionists and those seeking
a more expansive definition of food security have pointed out that calories fall well
short of the definition of food security adopted at the 1996 World Food Summit: “Food
security exists when all people, at all times, have physical and economic access to
sufficient, safe and nutritious food to meet their dietary needs and food preferences”
(e.g., Burchi etal., 2011). Thus the achievement of caloric sufficiency may ultimately
be irrelevant.
To frame this diversity of critiques the primary issues with quantitative models
are sometimes divided into technical uncertainties, methodological uncertainties, and
epistemological uncertainties (Funtowicz & Ravetz, 1990).
Narrower technical concerns include the “knowledge gaps and priorities” raised
by Reilly and Willenbockel (2010) and also by Wise (2013). On this level these au-
thors agree there exist very significant problems with data quality. In many countries
that extends to quantifying even the most basic inputs of the models: poverty, GDP,
water availability, even simple population. The problem of questionable data is high-
lighted by the case of Ghana. Its national statistical agency announced in 2010 that it
was revising all future GDP estimates upward by over 60%. This made Ghana into a
lower-middle-income country literally overnight (Jerven, 2012). Such difficulties are
The myth of a food crisis 97
acknowledged (though ultimately dismissed) in FAO’s Food balance sheets: a hand-
book (FAO, 2001).
Problems of comparable magnitude also apply to modeling the relationships be-
tween data points. According to Reilly and Willenbockel: “more work on the vali-
dation of model components used in integrated assessment studies is required.” But
these authors equally caution that validation is a two-edged sword; calibrating models
to past experiences, especially in the presence of climate change and other poten-
tial abrupt changes, introduces the problem commonly known as “overfitting.” Some
of these “technical”-level difficulties are acknowledged by the modelers themselves
(e.g., Bruinsma, 2003).
A yet further significant problem is that, because they come from many different
nations, datasets in separate parts of the models are based on different scales, time peri-
ods, and conceptual schemes (FAO, 2 001). Methods to reconcile such disparities are not
available, however. Unsurprisingly perhaps, having discussed these limitations, Reilly
and Willenbockel conclude that “model outputs should not be misinterpreted as fore-
casts with well-defined confidence intervals. Rather they are meant to provide quantified
insights about the complex interactions in a highly interdependent system and the poten-
tial general order of size effects” (Reilly & Willenbockel, 2010). This comment raises
some important issues. The first is that this limitation seems to have generally eluded
those who cite these numbers. Second, without confidence intervals no one—including
the modelers themselves—knows what this “general size order of effects” is.
We can gain insight into how such uncertainties might affect the quality of predic-
tions by examining an extended critique made by Thomas Hertel in his presidential
address to the American Agricultural and Applied Economics Association (Hertel,
2011). A major assumption in FAO and other models, notes Hertel, concerns how they
relate prices, demand, and supply. Focusing on FAO’s quantitative model (pre-GAPS)
he notes that it assumed that agricultural supply hardly responds to higher prices. This
assumption was introduced because measurements of how food supply responds to
demand in agriculture have mostly been taken over the short term. The remit of these
models is the longer term, however. Measurements taken over the long term suggest
that the elasticity picture is very different. Hertel contends that if future growth in
demand was sufficiently great for food prices to rise, then this would in turn stimulate
supply. Thus higher agricultural prices can be expected to favor high yields, raise land
prices (protecting existing land and bringing more into production), stimulate agri-
cultural research, and reduce waste, and this is indeed what the longer-term evidence
shows (Hertel, 2011). Even the declining trend in the growth of global crop yields,
which according to FAO is a major determinant of future food availability, may be a
function of price. To this end, Hertel quotes economist Robert Herdt: “the economics
of substantially higher yields is not attractive” (International Rice Research Institute,
1979). In this connection, Hertel also quotes FAO economist Jelle Bruinsma: “given
the right incentives, much of the increased demand for cereals and oilseeds in 2050
could be met using existing technology.” Hertel therefore concludes that the frequently
noted long-term “slowing of yield growth may simply be due to a slowing of net de-
mand growth.And he summarizes: “it is not clear that the resulting models are well-
suited for the kind of long run sustainability analysis envisioned here.
98 Rethinking Food and Agriculture
To summarize Hertel: there is strong evidence that incentives acting on farmers and
other decision-makers are key to explaining agricultural productivity, but since com-
modity prices have been in long-term decline, FAO has been modeling a low-incentive
With this as background the next section is devoted to analyzing four additional
assumptions underlying predictive models and using GAPS as the example.
5.2 The assumptions of GAPS
Key assumptions made by GAPS that strongly affect its predictions of future produc-
tion needs are as follows:
Assumption 1: That biofuels are driven by “demand”
Since 2002 the development of the liquid biofuel sector (ethanol and biodiesel) has
been very rapid. According to US Department of Agriculture figures, the US biofuel
industry consumed 127 million tons of maize in 2011, which was 15.6% of world
maize production. Additionally, in Brazil over 50% of sugarcane is used for biofuels
and in 2009 the EU consumed as biofuel 9 million tons of vegetable (mostly rapeseed)
oil as well as 9 million tons of cereals (Alexandratos & Bruinsma, 2012). Specifically
for ethanol, the 2016 figures for global production were, in millions of gallons, United
States (15,250), Brazil (7295), European Union (1377), China (845), and Canada
(436) (Mohanty & Swain, 2019).
The initial consequences of the biofuel boom were price spikes that propagated
through the global supply chain to affect most commodities (de Gorter, Drabik & Just,
2015; McMichael, 2009). These price spikes induced riots, hunger, and financial hard-
ships and, in some countries, political upheaval. The diversion of substantial quantities
of presumptive agricultural produce into biofuels is a perturbation that has, however,
ultimately been absorbed by the physical supply chain. Commodities have now all but
returned to their prespike prices (OECD-FAO, 2016).
In the models of the FAO, biofuels are deemed a demand. That is, they reduce
availability but they don’t contribute to feeding the population (except when certain
residues are fed to animals). Looking to the future, FAO further assumes that biofuels
will continue to expand in area, plateauing in 2020 (Alexandratos & Bruinsma, 2012,
p. 97).
The justification for treating biofuels as demand is that biodiesel and bioethanol
are environmentally beneficial products of deliberative policy choices. This position is
highly questionable. First, biofuels from palm oil or soybeans and ethanol from corn
cause immense biodiversity losses. Additionally, their net greenhouse gas emissions
provide no benefits or are only positive when measured over hundreds of years (e.g.,
Germer & Sauerborn, 2008; Danielsen etal., 2009). Second, the necessity justification
is dubious since the development of biofuel policies has been driven not by climate
concerns but by complex agglomerations of lobbying interests seeking to boost agri-
cultural demand so they can sell more inputs (Baines, 2015; ActionAid, 2013). The
significant point about markets driven by lobbying, rather than genuine demand, is
that if demand for food were, as expected, to increase (and/or food prices were to rise)
The myth of a food crisis 99
the interest in biofuels would vanish because their key political attraction is to prop
up demand (Baines, 2015). In other words, land devoted to biofuels is available, if
needed, for feeding populations but this opportunity is rendered invisible when models
such as GAPS categorize biofuels as a demand. The amounts of food converted into
biofuels are very substantial. For instance, ActionAid concluded that the G8 countries
(which exclude Brazil) consumed annually enough biofuel to feed 441 million people
(ActionAid, 2013). If measured today that figure would undoubtedly be much greater.
It is curious that FAO modelers do not address the ethics of biofuel diversion. First,
because, in a hungry world, the dilemma raised by diverting biofuels at the expense
of food availability is a rather obvious one (Chakrabortty, 2008), and second, because,
elsewhere in discussing their models, FAO economists do digress on the environmen-
tal and ethical implications of various actions, thus violating their supposed nonnor-
mative stance (e.g., Alexandratos & Bruinsma, 2012, p. 131). Third, given that the
premise of models like GAPS is that increasing production is an ethical imperative,
why is it not equally ethically problematic to subtract from food availability by burn-
ing it for transportation? FAO’s modelers have again been inconsistent in the applica-
tion of the nonnormative approach.
On one level, FAO’s failure to incorporate biofuels as potential food supplies for
hundreds of millions of people and instead integrate them into their model as “de-
mand” is an error. But it also represents a further instance of inconsistency: shuffling
in and out of normative modes. Is it a coincidence that FAO’s economists only take an
explicitly normative stance when it aligns with powerful interest groups? At the same
time, the shuffle is very much facilitated by GAPS having as its underlying paradigm
the normative expectation that production trumps all.
Assumption 2: That current agricultural production systems are optimized for
According to FAO statistics, if we truly wanted to maximize global yield as mea-
sured by calories/ha per day we would all eat sweet potatoes (70 × 10(3) kcal/ha/day).
Or, if we lived further from the equator, potatoes (54 × 10(3) kcal/ha/day) (FAO, http:// Of course, there are reasons why we do
not eat only these crops. Those reasons range from the agronomic benefits of crop
rotations to the physical and cultural needs for a varied diet. However, farmers also
grow many crops (such as coffee and grapes for wine) that have very limited ability to
feed people. As a consequence, world agriculture has the potential to greatly enhance
productivity through crop substitution.
This potential exists rather obviously in developed countries such as the United
States and the European Union, where subsidies and market monopolies are far more
powerful drivers of production than are calories or nutrition. One might suppose, how-
ever, that countries like Bangladesh (whose population of 160 million resides in an
area the size of New York state) might be different. Bangladesh has one of the highest
population densities in the world and one of the highest poverty and food insecu-
rity rates. However, although wheat yields about half that of winter season rice in
Bangladeshi conditions, the market price of wheat is higher and the input costs are
much lower (J. Duxbury, pers. comm.). Bangladeshi farmers therefore grow wheat on
415,000 ha (FAOSTAT, 2017). Such farmers are chasing markets not nutrition.
100 Rethinking Food and Agriculture
But perhaps the most glaring instance of suboptimal nutritional performance in
agriculture is meat consumption. Historically, much meat and dairy production took
advantage of marginal land that was less suited or unsuited to other forms of crop-
ping. Increasingly, however, especially in many “developed” countries, prime land
is cropped for animal feed. Even the most efficient converters of this feed (fish and
chickens) yield a worse caloric return per hectare than the least nutritious vegetable,
while the least efficient (beef) yields approximately fourfold less again (Cassidy etal.,
2013). Thus it has been estimated that beef, in a feedlot system, has a feed conversion
efficiency, measured in calories, of 3% (compared to chicken with 12%) (Cassidy
etal., 2013). Clearly, this implies major opportunities for substitution since about 35%
of the US corn crop goes to animal feed (Baines, 2015).
In spite of these diverse opportunities, substitutions of higher-yielding crops for
lower-yielding ones is a possibility neglected by GAPS. In contrast, GAPS does allow
substitution in the opposite direction. This is when GAPS allows higher meat con-
sumption following income rises. GAPS is thus again inconsistent.
The standard justification for allowing this substitution is “consumer demand.”
As populations become wealthier they “naturally” eat more meat, goes the theory.
Predominantly vegetarian countries excepted, there is certainly a correlation between
wealth and meat consumption. The caveat required is that OECD countries spend
$318 billion annually on agricultural subsidies. These overwhelmingly go to support-
ing either meat or biofuels (OECD, 2002). Virtually none of it goes to subsidizing
fruits and vegetables. Consequently, “the power and freedom of choice attributed to
consumers are questionable” and so also is any straightforward expectation that other
nations will closely follow this path, unless they too decide to subsidize meat produc-
tion (Rivera-Ferre, 2009).
How much extra food could result from crop substitutions overlooked by GAPS?
Any calculation is a difficult one since much depends on what crop is substituted and
what replaces it. The case studied most intensively is the impact of meat consump-
tion. A recent estimate is that 4 billion additional people could be fed if animals were
absent from the global feed chain (Cassidy etal., 2013). This study did not substitute
animal rearing with the most calorific crop, however. It is worth noting that this con-
clusion largely assumed Western-style rearing and consumption patterns. Thus it is
not applicable to those parts of the world where nomadic and seminomadic systems
Another type of substitution is the growing use of mixed cropping, agroecological
production systems, and conservation agriculture. These can further increase yields
beyond the monocultures assumed in GAPS, sometimes dramatically (Sampson,
2018; Kassam etal., 2009). In 2015/16, conservation agriculture occupied approxi-
mately 180 million hectares of cropland globally, and since 2008/09 has expanded by
over 10 million hectares a year (Kassam etal., 2018).
In conclusion, models in general (and not only GAPS) are disregarding clear oppor-
tunities for crop substitutions that have the potential to feed many billions of people.
Assumption 3: The existence of maximum “yield potentials”
An important assumption found in GAPS and other models is the use of the con-
cept of “yield potential.Yield potential describes the theory that crops have a genetic
The myth of a food crisis 101
yield ceiling beyond which cropping systems (usually envisaged as inputs of fertilizer
or chemicals) cannot lift them. Because of this ceiling, GAPS presumes that produc-
tivity increases can only come in either of two ways. Either slowly through long-term
research and breeding efforts that raise the yield potential of each crop, or, alterna-
tively, by persuading those farmers who use “suboptimal” seeds or methods and there-
fore operate far below this ceiling to approach the current yield potential. Thus yield
potentials figure prominently in GAPS. FAO states this assumption in many places,
e.g.: “There is a realization that the chances of a new Green Revolution or of one-off
quantum jumps in yields, are now rather limited” (Alexandratos & Bruinsma, 2012,
p. 125).
Yet these yield potentials are theoretical only. They are not proven to exist and
may not exist. Perhaps the leading exemplar of this is rice, the world’s most important
crop. The yield potential of rice is standardly estimated at 8–10 t per hectare (Peng
etal., 1999). Such high yields are assumed to occur only under agronomic conditions
of very high fertilizer and chemical inputs and with ideal soil and watering regimes.
Yet the world record for rice production is 22.4 t (Diwakar etal., 2012). This record
was achieved with few inputs by a farmer using a method called the System of Rice
Intensification (SRI). What this record implies, and peer-reviewed SRI research sup-
ports this, is that rice is far below its supposed yield potential when grown under stan-
dard “optimal” conditions (or that yield potential has no real-world meaning) (Kassam
etal., 2011; Taylor & Bhasme, 2019).
The implication of the yields achieved by SRI is that sustainable yields and pro-
ductivity exceed those assumed by all quantitative global models by several multiples.
Since theoretical yield potentials are ordinarily rarely met on real farms, 22.4 t rep-
resents in practice a potential tripling of yields over standard expectations. SRI meth-
ods have also been applied to other crops, again giving significant yield improvements
(Abraham etal., 2014). In 2013, SRI was estimated to have 9.5 million practitioners
(Uphoff, 2017). By 2019 this number had at least doubled (N. Uphoff, pers. comm.).
By accepting the concept of yield potential and ignoring SRI, quantitative models are
overlooking one of the most rapidly spreading developments in agriculture (Stoop
etal., 2017).
Since rice is the staple of half the globe (3.5 billion people) it can be readily ap-
preciated that a tripling of yields, especially since SRI is a more sustainable method,
represents the potential to feed perhaps a further 7 billion people (Fageria, 2007).
Assumption 4: That global food production is approximately equal to global food
Unlike in most sectors of the economy, agricultural production can exceed con-
sumption at the global or local scale. If we take cereals (wheat, rice, barley, millet,
sorghum, and oats) as an example, excess production occurs even in densely populated
countries such as India and China. In 2017, FAO estimated global stores of cereals
at 762 million tons; this is out of a total global cereal production (in 2017) of 2595
million tons (FAOSTAT).
These stocks represent an insurance against calamity. However, this 762 million
tons also represents an excess of supply over global demand. An important property
of these stocks is their perishability. Depending on the climate, the quality of storage,
102 Rethinking Food and Agriculture
and the crop species, they may rot or be eaten by rodents or insects; thus even if stocks
are not growing, crops may still be entering them at a high rate. The second relevant
property of stocks is that, if there are multiple harvests per annum, quantities of lost
stocks may represent multiples of the steady-state amount of an annualized store. For
example, if 33% of each rice crop is lost in storage and there are three rice storage pe-
riods, corresponding to three harvests, then 100% of the annual total stock is, in effect,
lost each year. The consequence of these properties is that it is very important to count
quantities entering stocks since, if needed, they are available for use. Even if stored
well, stocks eventually degrade. In China, wheat stocks are considered by analysts to
last maximally 3–4years and an average of 2years. For this reason, China, which is
one of the biggest stock holders of rice and wheat, began a biofuel policy to consume
excess stocks of wheat. This has steadily grown and now generates 845 million gallons
of ethanol per year (Mohanty & Swain, 2019). Despite this program, Chinese wheat
stocks are still growing.
What this ultimately means is that it is important to understand how much is en-
tering stocks each year. Unfortunately, for modeling purposes, FAO assumes that “At
the world level production equals consumption” (Alexandratos & Bruinsma, 2012;
see also FAO, 2001). This assumption is restated in the 2016 GAPS description: “The
model assumes a closed world economy so that at the end of every simulation period
global demand equals supply” (Kavalleri etal., 2016). Amounts entering stocks are
counted, but only as the difference between opening stocks and closing stocks (FAO,
2001). The net effect is to ignore losses to insects, mold, rodents, and age.
The simple way to show how this method ignores lost and degraded stocks is to
work through an example. If 762 million tons was the amount in stock on January
1 and all of it was lost to rats and insects in the subsequent year and then the entire
amount was replaced in the following year, FAO’s counting method would register no
change to stocks and no addition to stocks. That is, it would appear that nothing had
entered stocks even though 762 million tons had in reality done so.
How much of the global grain supply is lost in storage? Estimating postharvest
losses is difficult and seems to be a low priority. Furthermore, many estimates are not
necessarily applicable to stocks but rather to postharvest in general. FAO estimates
that postharvest losses in low–middle-income countries are approximately 6.4% for
cereals. Most cereal and pulse loss estimates are much higher, but also highly vari-
able and they acknowledge much uncertainty (Boxall, 2001; Kumar & Kalita, 2017;
Sharon etal., 2014). Estimates include 20%–30% for maize in Africa (Tefera etal.,
2011); 12% and 44% for maize in the West Cameroonian highlands during the first
6months of storage (Tapondjou etal., 2002); 11%–17% for rice in India, without
counting storage (Alavi etal., 2012); and 35% for rice in India (Scrimshaw, 1978).
Some reports estimate very high levels, for example, 59% after 90days in sub-Saharan
Africa (Kumar & Kalita, 2017). Thus FAO’s figures for postharvest losses are very
much at the low end, especially since storage is often considered the stage of greatest
deterioration (Kumar & Kalita, 2017).
One could say that GAPS makes two errors that cancel each other out to balance
the model. It ignores what enters stocks (unless they change) and it ignores losses
within them. This is fine from the point of view of building a closed model but the
The myth of a food crisis 103
significance of the errors for the purpose of estimating food availability is potentially
very great: there is an uncounted annual excess of production, which is lost in storage,
but is available to meet future demand. To state this in Malthusian terms, population is
below current production and population (at least demand) can afford to grow before
the two become equal. This is not to say that stocks and reserves are undesirable or
unnecessary, but it is to say that a very significant unacknowledged gap likely exists.
Current estimates of both stocks and stock losses are highly uncertain. However, if
we accept its numbers at face value then FAO is undercounting food equivalent to the
cereal needs of perhaps 1–2 billion people, even without counting the losses of more
perishable (noncereal) crops.
In summary, the four assumptions discussed here generate the following esti-
mate of extra food potentially available (over and above the estimates of FAO’s
GAPS). Assumption 1: 500 million; Assumption 2: 4 billion; Assumption 3: 7 bil-
lion; Assumption 4: 1–2 billion. The sum total (12.5 billion) is certainly a low-end
figure because Assumption 1 underreports current biofuels because its data are old;
Assumption 2 only includes the substitutions of meat by crops and not higher calorie
crops by lower calories ones (because no studies exist); and Assumption 3 only in-
cludes rice, whereas SRI suggests other yield ceilings may also be similarly flawed.
Nevertheless, 12.5 billion people’s worth of spare food is a very large underestimate.
5.3 What are models for?
The foregoing discussion has highlighted that quantitative models such as FAO’s
GAPS are highly reliant on questionable assumptions. Summarized briefly, first,
GAPS does not adequately take into account the potential to substitute higher-
yielding food species for lower-yielding ones. Second, GAPS neglects that food can
and would be grown instead of biofuels if needed. Third, GAPS contains unreason-
ably low expectations of achievable yields of existing crops, in particular rice, which
is the staple of half the global population. Fourth, GAPS neglects annual surpluses
lost in storage.
To this critique of GAPS and related food models should be added that of Thomas
Hertel, who has asserted that FAO has underestimated the potential for food availabil-
ity to rise with prices (Hertel, 2011).
Thus the discrepancy that prompted this chapter to be written, which was between
long-term (decreasing) global prices that demonstrate increasing overproduction and
quantitative models that predict looming scarcity, can readily be resolved. The mod-
els are flawed. They underestimate actual or potential supply and they exaggerate
Quantitative models in science, in technology, and in economics have never been
more popular (Porter, 1995). To explain this ascent, two distinct schools of thought
have arisen. The standard explanation is that quantitative models are objective ap-
proximations to reality that have become more prevalent because, with computers and
other developments, they have become progressively more powerful and more useful
104 Rethinking Food and Agriculture
The second, rival, explanation is that the rise of such models is attributable to their
usefulness as pretenses to objectivity. As British scientist Lancelot Hogben wrote in
1933, they allow the possibility of “concealing assumptions which have no factual
basis behind an impressive façade of flawless algebra.(Another formulation is at-
tributed to ecological modeler Dick Levins: “All quantitative models are qualitative
models in disguise.”) In this way, models can function as smokescreens (disguises)
for either conscious or unconscious institutional biases and objectives. In the words
of Theodore Porter: “Quantification is a way of making decisions without seeming to
decide” (Porter, 1995).
The standard explanation of the rise of modeling is surely flawed, at least in part.
All modeling is a retreat from reality in which complexity is reduced to uniformity
and qualities to quantities. In whittling data and simplifying interactions, choices
must be made. Perhaps nowhere in all of quantitative modeling is simplification
more problematic than in a representation of the food system whose component
parts are biological, climatic, hydrological, economic, and social in character; that
is, they are incommensurable. Thus modelers are inevitably faced with difficult
choices over which of the myriad potential contributory factors to include and
which to exclude. In the same way, they have to devise how to mathematically
simplify the likely nature of the relations between them. These choices represent
challenges to objectivity.
A good example of this objectivity problem as it applies to food models is the
time frame of FAO’s annual accounting. Its agricultural year runs from the beginning
of January to the end of December (FAO, 2001). It is thus calibrated to the needs of
Europe and North America and not those of the Southern Hemisphere or the tropics.
This choice exemplifies how the values of the West, but also of international trade,
and of finance are subtly present in all such models at the expense of the much less
prominent or quantifiable priorities of the poorer countries (predominantly Southern
and tropical) whose interests these models claim to serve.
The assertion of an objective modeler is therefore untenable. This does not mean
that all models are necessarily valueless. But it does suggest we ask a more subtle ques-
tion about the good faith and disinterestedness of its authors and funders: What steps
do the builders of such models take to minimize the potential for unconscious bias?
From this perspective it is not reassuring that FAO claims to rely “as much as pos-
sible” on in-house experts (Alexandratos & Bruinsma, 2012). Nor that FAO presents
its data with an implied high degree of confidence, especially in prominent fora (e.g.,
Diouf, 2008, cited in Tomlinson, 2011). Meanwhile, the references in FAO documents
to assumptions, limitations, and uncertainties are sparse and relegated to middle pages
or back pages or annexes. Sometimes these are therein even discounted without expla-
nation (Reilly & Willenbockel, 2010; Wise, 2013).
Third, the frequent shifts into the normative language (of “should”) and back again
by FAO in its successive reports is again suggestive that institutional preferences exist
but are not well managed. Perhaps the most instructive formulation of the disinterest-
edness question is to ask (if only rhetorically): If its models gave answers that were di-
ametrically inverse to the ones presented (i.e., that food provision was ample), would
FAO stand by them?
The myth of a food crisis 105
5.3.1 Conflicts of interest amid the food crisis
This question leads to the consideration of what is most noticeable about the four
questionable assumptions identified in this chapter, that they are not neutral in their
effects. Each has the consequence either of exaggerating demand or of underestimat-
ing supply, either in the present or in the future. Thus they all serve to exaggerate a
Malthusian scenario. The same is true for the assumption noted by Hertel, that FAO
fails to appropriately take into account the effects of price on production (Hertel,
2011). It too underestimates future food supply. Together the effect amounts to the
food requirements of, minimally, many billions of people.
A potential explanation for the Malthusian exaggeration is that FAO, like almost
every large institution in the food/academic/philanthropic nexus, has a conflict of in-
terest. If there were no threat of a food crisis of some kind, FAO’s institutional raison
d’etre (motto: Fiat Panis) would vanish. Additionally, given these conflicts, some food
crisis variants are clearly preferable, from FAO’s perspective, over others. If institu-
tions like FAO, IFPRI, or the World Bank were to frame the problems of agriculture
as resulting primarily from maldistribution of land, democratic deficits, poverty, or
the excessive power of agribusiness, they would come into conflict with agribusiness,
governments, and wealthy landowners, the very people who, directly or indirectly,
fund or control these institutions. Far safer, politically, to blame hunger on lack of
production; that is, the farmers (Food First, 2016; Sampson, 2018).
In this context it is useful to recall the seemingly neglected predictions of FAO’s
own models that increasing food production would have little effect on the number of
people going hungry (Alexandratos & Bruinsma, 2012; Bruinsma, 2003). This pre-
diction came true. In 2018, even though food production did subsequently increase,
and food prices fell, the number of malnourished rose to 821 million. If FAO wants to
solve hunger, their own model is telling them to look elsewhere than increasing pro-
duction. This is a highly damaging finding to models, like GAPS, with a productivist
premise. Yet, 17years on from 2003 FAO still targets productivity, as does almost
every major food security institution and philanthropic effort (Food First, 2016).
5.3.2 Final thoughts
One interest group in particular benefits from the premising of models such as GAPS
on the question of productivity. “Feeding the world” is the principal public relations
gambit of international agribusiness. Only agribusiness has the yields to save the poor
and starving, is their claim (Peekhaus, 2010; Stone & Glover, 2011). If the scarcity
narrative is true, that claim is powerful. It transforms agriculture into a moral issue
(Dibden etal., 2013; Latham, 2015). Pesticides, genetically modified organisms
(GMOs), and monocultures may have negative consequences, goes the narrative, but
they are the necessary alternative to starvation. The sole alternative, accordingly, is
merely a luxury for the privileged and of no interest to policymakers. Alternatively, if
scarcity is a myth, then all pesticides are sprayed, and all GMOs exist, exclusively for
profit. The destruction of the ecosphere, which is largely for the sake of agriculture, is
effectively a waste (IAASTD, 2009; IPBES, 2019). The stakes are high.
106 Rethinking Food and Agriculture
Agribusiness stands or falls on this point. The reason, as George Lakoff has argued,
is that humans think and act in moral terms. They wish to think of themselves as
“good” (Lakoff, 2004). Thus the focus of quantitative models on productivity, which
asserts a moral essence, is a gift to agribusiness since, to be effective, a credible third
party has to legitimize the scarcity narrative.
So, while very significant effort and attention have been directed toward creat-
ing GAPS and other models devoted to estimating and predicting global agricultural
demand and production, there remains effectively no clear evidence that lack of pro-
ductivity plays a pivotal role in the hunger epidemic that supposedly stimulates them
(Lappé & Collins, 2015; Sen, 1981). In contrast, there is abundant, even overwhelm-
ing, evidence that agriculture, and in particular industrial monocultures, needs to be
realigned to become kinder to ecosystems and more beneficial for the individuals and
the communities that feed us (HLPE, 2013; IAASTD, 2009). This will not occur, how-
ever, until the scarcity narrative is set aside.
The necessity of following an alternative path is acknowledged in places by FAO
modelers. The foreword to FAO’s latest major modeling report, The future of food and
agriculture: Alternative pathways to 2050, says: “Swift and purposeful actions are
needed to ensure the sustainability of food and agriculture systems in the long run”
(FAO, 2018). Yet even that report still perpetuates the scarcity narrative. It privileges
a quantitative productivist focus as the ultimate arbiter of the alternative pathways it
explores, thereby undermining all such possibilities. What is needed instead, from the
FAO as well as others, is a focus on the broad consequences of agriculture and food
systems, that is, their multifunctionality.
Multifunctionality is the idea that agriculture is deeply embedded in other systems,
and therefore how it is conducted has consequences well beyond the single metric
of crop production by volume (IAASTD, 2009; Kremen & Merenlender, 2018). For
instance, industrial agriculture is the single largest contributor to climate change and
other forms of atmospheric pollution (Bauer etal., 2016; Goodland & Anhang, 2009;
Steinfeld etal., 2006). Agriculture also provides livelihoods; it cleans or pollutes wa-
ter (aquifers, surface waters, and oceans); it conserves or degrades biodiversity; it
provides landscape value and culturally appropriate food; and so forth (IUCN Task
Force on Systemic Pesticides, 2017). These contributions of agriculture, all effectively
ignored by quantitative models of calorific production, should receive at least as much
consideration as does productivity from decision-makers, if only because, unlike pro-
ductivity, these functions are often more at risk.
Having discussed some of the limitations of modeling agricultural productivity, which
is a comparatively simple output, it can reasonably be questioned whether modeling of
agriculture on a global level can ever perform a constructive role. Perhaps ultimately a
more useful role for modeling is at more local scales, and modeling that asks questions
of a more defined nature. Work on nitrogen modeling of the Paris basin constitutes an
example of how modeling can have more modest aims yet be useful to policymakers and
others (Billen etal., 2012). Perhaps FAO should now move its focus to assessing the rel-
ative effects of pesticides and other inputs on biodiversity or human health from different
agricultural systems? This is a domain that the current regulation of pesticides is failing
to address (Va nd e n b e r g e t a l . , 2 0 1 2). Such an analysis would beneficially serve to put ag-
riculture on notice that its long-ignored externalities will in future move to center stage.
The myth of a food crisis 107
The author is extremely grateful to Timothy Wise, Philip McMichael, Allison Wilson, and three
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sparing/agriculture intensification model. Proceedings of the National Academy of
Sciences, 107, 5786–5791.
... Their proposed system can provide predictive support to food supply chain vehicles to reroute if they encounter any restricted route. The food yield requirement is justified in (Latham, 2021) for the coming year. The authors clarify the food's unreported requirements and thus estimate the food's future needs for the world population. ...
... • Controlled the HVAC based on multi-agent deep reinforcement learning with an attention mechanism. (Latham, 2021) Hunger and food deficit analysis ...
Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information and communication advancements will empower better administration of accessible resources. The eventual fate of the world's betterment lies in its urban environment advancement. The fast influx of individuals creates possibility, yet it additionally causes challenges. Creating sustainable, reasonable space in the world's steadily extending cities is tested confronting governments worldwide. The model of the smart cities rises, where the rights and well-being of the smart city citizens are assured, the industry is in action, and the assessment of urban planning from an environmental point of view. This paper presents a survey on analyzing future technologies and requirements for future smart cities. We provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era. Such technologies include deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying Cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured. We provide a detailed review of the existing future smart cities application frameworks. Furthermore, we discuss various technological challenges of future smart cities. Finally, we identify the future dimensions of smart cities to develop smart cities with the precedence of smart living.
... Their proposed system can provide predictive support to food supply chain vehicles to reroute if they encounter any restricted route. The food yield requirement is justified in [112] for the coming year. The authors clarify the food's unreported requirements and thus estimate the food's future needs for the world population. ...
... It can control the HVAC based on multi-agent deep reinforcement learning with an attention mechanism. [112] The growing concern in the agriculture field worldwide is the expected increasing food deficit. UN's food and agriculture organization (FAO) is closely monitoring and working on the issue. ...
... Their proposed system can provide predictive support to food supply chain vehicles to reroute if they encounter any restricted route. The food yield requirement is justified in [112] for the coming year. The authors clarify the food's unreported requirements and thus estimate the food's future needs for the world population. ...
... It can control the HVAC based on multi-agent deep reinforcement learning with an attention mechanism. [112] The growing concern in the agriculture field worldwide is the expected increasing food deficit. UN's food and agriculture organization (FAO) is closely monitoring and working on the issue. ...
... The world population of humans was about 8 billion; this is considered over-grown as per 2017 (Mottet and Tempio, 2017). Rapid population growth in Africa contributes to at least half of the world's inhabitants growing from 1.1 billion to 2.4 billion (Alexandratos and Bruinsma., 2012;Latham, 2021). This growth is a result of the densely young generation population that is capable of reproduction. ...
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Egg storage has been a problem due to ineffective methods subjecting many farmers and egg retailers to losses. These techniques include various models involving statistical analysis of the storage conditions on the egg quality. Apparent deficiencies of the information from the randomized complete block design model prompted this study. The study evaluated the effect of storage temperature at three levels (5 oC, 19.5 oC, 30 oC) and duration at four levels (2nd, 12th, 22nd, 32nd) on egg quality using a fixed and mixed-effect model. We used a total of 618 fresh and unfertilized eggs from ISA (Institut de Sélection Animale) brown layers. We determined egg quality by the changes of physical characterization under storage conditions. The study used Restricted maximum likelihood and analysis of variance methods to assess the efficiency of fixed and mixed effect models. Results showed that the physical components of the egg were significantly affected at 5 oC, 19.5 oC, and 30 oC(P<0.05). The effect was more adverse on eggs stored at 30 oC for 32 days. However, storage temperatures of 5 oC and 19.5 oC led to an extensive reduction in the Haugh unit, yolk index, and egg white height. On the other hand, it increased the weight loss and albumen diameter under storage for 2nd, 12th, 22nd and 32nd-time intervals. Based on these findings, the study recommends 5 oC for egg quality preservation. The eggs should be refrigerated for 32 days, stored at 19.5 oC for 14 days, and lastly kept at 30 oC for a maximum of 7 days. The fixed-effect models exhibited more minor variances in diameter and height of albumen, yolk index, weight loss, and Haugh unit. This overlapped instances where the fixed-effect models were significantly the same as the mixed-effect models. This study proposes that the fixed effect model is the most appropriate for randomized completely block design experiments.
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It has been clear for many years that agriculture in Europe needs to become more sustainable and requires fundamental change. In recent years, more and more “innovative” techniques or products have been presented as THE one big solution – examples include precision farming, indoor farming, new genetic engineering or carbon farming. It is often suggested that the problems of the agricultural system can now be solved, although only partial areas are affected. Can carbon farming make agriculture climate-friendly? Is indoor farming sustainable? Can new genetic engineering be used to breed more resistant plants? Does precision farming solve the issue of over-fertilisation? This study takes a closer look at some of the currently prominently discussed technical “sustainability solutions” and asks critical questions about their effectiveness. Do techno-fixes prominently discussed in policy papers and the media bring agriculture into harmony with ecosystems, animal welfare and societal needs?
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How can we manage farmlands, forests, and rangelands to respond to the triple challenge of the Anthropocene—biodiversity loss, climate change, and unsustainable land use? When managed by using biodiversity-based techniques such as agroforestry, silvopasture, diversified farming, and ecosystem-based forest management, these socioeconomic systems can help maintain biodiversity and provide habitat connectivity, thereby complementing protected areas and providing greater resilience to climate change. Simultaneously, the use of these management techniques can improve yields and profitability more sustainably, enhancing livelihoods and food security. This approach to “working lands conservation” can create landscapes that work for nature and people. However, many socioeconomic challenges impede the uptake of biodiversity-based land management practices. Although improving voluntary incentives, market instruments, environmental regulations, and governance is essential to support working lands conservation, it is community action, social movements, and broad coalitions among citizens, businesses, nonprofits, and government agencies that have the power to transform how we manage land and protect the environment.
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Bioethanol has recently emerged as a major issue in energy policy to fulfill growing energy demand and sustainable economy. Ethanol as a fuel source today owes positive impact on rural America, the environment, and United States energy security. Bioethanol from corn and wheat is considered as the first-generation biofuel compared to other sources of biofuel because only hexose sugar is targeted for fermentation. High titer ethanol (>5%) can be achieved from corn and wheat by using simpler steps (grinding/milling, cooking, and liquefaction) before fermentation using the yeast strain (Saccharomyces cerevisiae). Recently, bioethanol from corn and wheat is adopted at industrial-scale in several developed and developing countries to fulfill the demands of the bioethanol. Fuel ethanol production is an energy-efficient process today, additional research is ongoing to improve its long-term economic viability.
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The system of rice intensification (SRI) has been promoted across Asia as a means to improve rice yields while decreasing water use and external inputs. It is argued to be a generalisable means by which to revalidate smallholder livelihoods and improve food security across the region. Current debates about SRI, however, remain predominantly technical in scope, focusing on field‐level outcomes. To more adequately understand the potential of SRI for smallholder farmers, we argue that it is necessary to situate SRI within a political ecology framework that addresses how the adoption and practice of SRI is shaped by uneven access to key assets including labour, water, and extension networks. Fieldwork conducted in Mahabubnagar district in Telangana, south India—where SRI had been widely disadopted despite the achievement of higher yields—is used to illustrate why agronomic analysis must engage directly with the complex social contexts in which farmers operate.
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Trusting Trade and the Private Sector for Food Security in Southeast Asia challenges policy makers who oversee the rice sector in Southeast Asia and reexamines deep-rooted precepts about their responsibilities. The authors argue that fixating on national self-sufficiency has been costly and counterproductive, and cooperation can both improve rice production at home and expand regional trade. Trusting Trade specifically examines private sector participation in the rice and (yellow) maize markets in five countries in the Association of Southeast Asian Nations (ASEAN) Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. The study identifies both the private sector s potential role in providing greater regional food security and feasible ways to strengthen public and private sector cooperation in managing regional food supply chains. Trusting Trade also examines actionable ways to deepen and strengthen regional markets that support trade in food staples. The study s recommendations are meant to be implemented primarily through new forms of partnerships between the public and private sectors. Trusting Trade will be of interest to policy makers in the ASEAN member states and its development partners as well as others interested in food security, supply chains, and trade in Southeast Asia.
Among all of the possible approaches to reducing hunger in the world, efforts to increase agricultural productivity dominate in development institutions and large philanthropies. In this productivist paradigm, the function of agriculture is narrow, and further investments in industrial agriculture are the greatest need. This view clashes with the intricate diversity and multiple functions of farms and gardens in Yucatan, Mexico. Agroecosystems there are spectacularly diverse. Besides providing many products to eat and sell, those farms are uniquely well suited to feed households in the increasingly erratic weather of Yucatan, where droughts and storms often wipe out certain crops. In a diverse garden, there is nearly always something to eat. There is little evidence that increasing agricultural production alone promotes food security, and there are many instances in which the drive for productivity has exacerbated hunger. In this article, I investigate why productivism has dominated development policy and discourse for so long.
A growing and increasingly urbanized global population will raise the pressures to intensify agricultural production systems, as the total surface area of arable lands remains constant or even diminishes in many parts of the world. Mainstream agricultural research and development have been emphasizing a ‘green revolution’ type intensification largely based on new seeds of crop varieties, while relying on agrochemicals to achieve short-term economic sustainability. During the 1980s the ‘System of Rice Intensification (SRI)’ was empirically developed in Madagascar. This set of practices was initially and widely rejected by mainstream (rice) research in spite of its often spectacular grain yields as obtained in farmer fields across many Asian and African countries. Some of the practices proved equally relevant for other crops such as wheat maize and sugarcane. SRI’s popularity among Indian smallholders, originated from its attractive yields at reduced rates/costs for seeds, water, nutrients and chemicals (fertilizers and pesticides). These features have sparked research into both the agronomic/biological as well as socioeconomic/humanity disciplines. This paper seeks to capitalize on the lessons learned from SRI, also with respect to other crops, and explores additional opportunities for intensification based on ecological processes as supported by insights from (soil) microbiological, biochemical and molecular research. Recent studies suggest that soil organic matter, plant roots, soil biota and biodiversity of plants and (micro-) organisms – rather than agrochemicals – will be the critical elements of environment-friendly and sustainable forms of agriculture. To capitalize on these factors, large reductions in plant densities (>50%) would be required to allow expanded root systems as the basis for increased plant physiological efficiency. The paper elaborates also on the socioeconomic/cultural issues involved in possible transitions as farmer’s technical/managerial skills and insights were crucial in the successful introduction and dissemination of SRI practices so far. The article is based on an extensive review of literature complemented by field observations and farmer interviews in different parts of India and West Africa.