ArticlePDF Available

Importance of food-demand management for climate mitigation


Abstract and Figures

Recent studies show that current trends in yield improvement will not be su cientto meet projected global food demand in 2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The usual proposed alternative-intensification with increased resource use-also has negative effects. It is therefore imperative to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas emissions. Some authors have emphasized a role for sustainable intensification in closing global 'yield gaps' between the currently realized and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions reductions, and to provide global food security in 2050.
Content may be subject to copyright.
Importance of food-demand management for
climate mitigation
Bojana Bajželj1*, Keith S. Richards2, Julian M. Allwood1, Pete Smith3, John S. Dennis4,
Elizabeth Curmi1and Christopher A. Gilligan5
Recent studies show that current trends in yield improvement will not be sucient to meet projected global food demand in
2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of
losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The
usual proposed alternative—intensification with increased resource use—also has negative eects. It is therefore imperative
to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas
emissions. Some authors have emphasized a role for sustainable intensification in closing global ‘yield gaps’ between the
currently realized and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show
that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options
for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for
demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions
reductions, and to provide global food security in 2050.
Over 35% of the Earth’s permanent ice-free land is used for
food production and, both historically and at present, this
has been the greatest driver of deforestation1and associated
biodiversity loss. Food demand has increased globally with the
increase in global population and its affluence. Globally, the demand
for food will undoubtedly increase in the medium-term future.
The United Nations’ Food and Agriculture Organization (FAO) has
projected that cropland and pasture-based food production will see
a 60% increase by 2050, calculated in tonnages weighted by crop
prices2. Another study3projected a 100% increase in cropland-
based production, measured in calories, and including both food
and livestock feed. The difference between the two studies can be
partly explained by shifts towards more cropland-grown livestock
feed (as opposed to pasture-based), as countries become richer.
Because agriculture is not on track to meet this demand,
according to current trends in yields4, it has been widely
suggested that we should strengthen global efforts in sustainable
intensification of agriculture5–8. This involves an increase in crop
yields while also improving fertilizer, pesticide and irrigation use-
efficiency. The existence of yield gaps—the difference between
yields achieved in best-practice agriculture and average yields in
each agro-climatic zone—suggests that the scope for sustainable
intensification is large. Yield gaps are wide in some developing
countries, notably in Sub-Saharan Africa, but also exist in developed
countries9,10. However, to complement these supply-side options,
demand-side measures may also be necessary6–8,11–13.
The objectives of this paper are to estimate the environmental
consequences of the increasing food demand by 2050, and to
quantify the extent to which sustainable intensification and
demand reduction measures could reduce them. Previous
quantitative studies have examined future food systems and
their impacts on land use14. However, few have touched on
sustainable intensification3or demand-side reductions12,15,16. The
types of model used in these studies include multiple regression
analysis3, partial equilibrium models (such as the IMPACT (ref. 17)
and GLOBIOM (ref. 18) models), and Integrated Assessment
models (such as IMAGE; ref. 19). We based our calculations
on a transparent, data-based biophysical analysis, which allows
us to vary the key drivers of future land use, including those
on the demand side. Our scenario based on current trends
predicts a higher need for agricultural expansion than previous
models20. Reasons include using less optimistic projections for
future agricultural productivity4, and not including barriers for
land-use conversions. Our methodology is described in more
detail in Supplementary Notes 1–2, Figs 1–8, and Tables 1–20.
A comparison between our approach and previous studies is
detailed in Supplementary Notes.
Analysis of current land use as a baseline
Our approach uses a model of the current global land system, with
2009 as a base year, based on empirical data. Two key components
of this model are: an analysis of land distribution, which enables
us to allocate land-use change, and determine natural ecosystem
losses and GHG emissions; and a map of agricultural biomass flows,
which is required to represent the demand-side options. In Fig. 1
we visualize the land system in 2009 with two Sankey diagrams,
one for each component: Fig. 1a shows the distribution of land use,
which connects to a representation of agricultural biomass flows
(Fig. 1b). Sankey diagrams act as a visual accounting system and
facilitate communication to a wide array of stakeholders in land use
and management, by illustrating magnitudes, flows and efficiencies.
The analysis of land distribution overlays agricultural suitability10
with global biomes21 and current land use22,23 in each region
(Fig. 1a). This shows in which biomes cropland and pasture
1Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK, 2Department of Geography, University of Cambridge, Cambridge,
CB2 3EN, UK, 3Scottish Food Security Alliance-Crops and Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen,
AB24 3UU, UK, 4Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB2 3RA, UK, 5Department of Plant
Sciences, University of Cambridge, Cambridge, CB2 3EA, UK. *e-mail:
Dense shrubland
Open shrubland
rock/ice Barren
SOC and nut.
SOC and nut.
Other plants
Ungrazed plants
Erosion, timing
0.7 0.7
2.7 4.6
Land-use and suitability distribution (area)
Global biomes
Land-use Natural
NPP potential
(Pg C yr−1)
(Pg C yr−1)(Pg C yr−1)
Processing Ecosystem
(Pg C yr−1)(Pg C yr−1)
Land agricultural
suitability (Mkm2)
Agricultural biomass flow (biomass carbon/year)
Figure 1 | Distribution of terrestrial biomes, suitability and land use and its connection to the global agricultural annual biomass flows for 2009. a, Major
global biomes are traced onto three classes of land for agricultural suitability. 40% of the total ice-free land area is suitable for agriculture, of which about
half is already in agricultural use for either pasture or cropping. b, Pasture and cropland areas support agricultural biomass growth, which we follow through
harvesting and processing stages, to the delivery of final services. In both panels the width of each line is proportional to the magnitude of flow. Black lines
show losses.
expansion have happened in the past, and where they are likely to
occur in the future. For example, further cropland expansion is likely
in tropical forests and savannahs, where approximately 75% of their
area is suitable for agriculture.
Where possible, we base the agricultural biomass flow analysis
for the base year of 2009 (Fig. 1b) on FAO agricultural statistics24.
These are supplemented where necessary by other data sources25–29:
for example on pre-harvest losses, livestock feeds, crop residues and
their uses. Given the uncertainty in the data, subsistence farming
is likely to be under-represented. Food sourced from forests and
aquatic systems is not included. Net primary productivity potential
of cropland and pasture is a starting point for biomass flows. Some
productivity potential is lost (5 PgC yr1) to soil erosion (caused
by overgrazing on pasture) and to the use of cropping systems
that do not achieve the productivity of all-year natural vegetation.
On the other hand, humans artificially improve productivity with
irrigation30,31 and fertilization32 (adding 4.3 PgC yr1).
It is striking how small the amount of food actually delivered
is (0.7 PgC yr1, or 2,490 kcal person1d1), compared with overall
cropland productivity (8.3 PgC yr1), or compared to harvest
(2.4 PgC yr1). The discrepancies are mainly due to the inefficiency
of supplying food calories as livestock products, and to losses in
every step in the system (shown in Fig. 1b as black curved lines).
Livestock globally consume 4.6 PgC yr1as feed (1.2 PgC yr1of
crop products, 0.7 PgC yr1of crop residues and 2.7 PgC yr1of
pasture forage). The main outputs, meat and dairy, contain only
about 0.12 PgC yr1or 2.6% of that carbon mass, before losses
(contributing 410 kcal person1d1). These results are confirmation
of both the trophic energy inefficiency and the land-intensiveness of
animal-based food products. We estimate that grazing on pasture
unsuitable for cropping, whose natural climax vegetation is grass
or shrubs, contributes approximately 14% of total livestock feed
measured in carbon mass (0.6 PgC yr1). Such land use has no
opportunity cost in cropping and does not cause deforestation,
but can still have negative consequences for carbon storage and
biodiversity. The latter is particularly true for ‘improved’ pastures,
which, as opposed to semi-natural pastures, are sown and require
artificial inputs. If we also add the crop residue feeds and processing
co-products as efficient contributions to the livestock production
system, together these support about 30% of current livestock
production; the remaining 70% has to be seen as a very inefficient
use of land to produce food.
Losses due to pests and weeds account for 1.0PgC yr1, or
13% of plant growth on cropland (Fig. 1). This calculation is
based on a single study29 and is highly uncertain, highlighting
the need for new world-wide studies of preventable pre-harvest
losses. Losses further down the chain are smaller in mass, but
nevertheless represent significant fractions of their representative
flows (agricultural losses 0.18 PgC yr1(12%), processing losses
0.06 PgC yr1(8%) and food waste losses 0.08 PgC yr1(12%); these
are calculated on the basis of a previous top-down study of losses in
agriculture26). Importantly, the later in the chain the loss of biomass
occurs, the more wasteful is the loss, as the biomass has already
undergone previous transformation stages that required inputs of
resources and energy.
From our analysis shown on Fig. 1, it is clear that if the demand
for inefficient pathways of food supply (that is, livestock products)
disproportionally increases, the whole system becomes not only
larger, but also less efficient. Previous studies3,17,33 directly link the
demand for food commodities to agricultural production without
considering possible changes in the supply chain that connect the
two, and put most emphasis on yields. Our biomass flow map
highlights that opportunities to reduce waste and improve efficiency
are equally important.
Future scenarios
The interplay between intensification, waste reduction and dietary
preferences, informed our choice for six parameter combinations
for scenarios in 2050 (Table 1). The probabilities of these key
variables are unknown. We examine sustainable intensification to
the point of yield-gap closures as the scenario that best represents
the collection of supply-side management changes that improve
food supply and reduce environmental impact. It includes improved
irrigation efficiency and eliminates over-fertilization. Food waste
Table 1 | Main parameters for the six core scenarios, split into
two groups.
Scenarios Yields Demand-side reductions
in yields
Yield gap
50% food
CT1 ×
CT2 × ×
CT3 × × ×
YG1 ×
YG2 × ×
YG3 × × ×
The Current Trends (CT) scenarios assume yields in each region will continue to increase at
current rates4. The Yield Gap (YG)scenarios assume that sustainable intensification will achieve
yield gap closures10 in all regions. Both yield scenarios are set against three dierent options on
the demand-side: no changes to the system; a 50% reduction in food and agricultural waste; and
waste reduction as above plus a move towards healthy diets, meaning the average consumption
of sugar, oil, meat and dairy is limited accordingto expert health recommendations37–40 .
and dietary change are the two most prominent demand-side
measures proposed in previous studies12,34,35 and have been shown
to have a large potential, so we have selected these two for closer
examination in our study. Changes in agricultural biomass flows and
land distributions in the six scenarios are shown in Supplementary
Fig. 9. For each scenario we estimated four indicators: forest
losses, carbon emissions (from land-use change and agricultural
production), fertilizer use and irrigation use (Table 2).
Baseline scenarios assume that global population increases
to 9.6 billion by 2050 (ref. 36), and that dietary preferences
change with socio-economic transitions2. The average per capita
consumption increases to 2,710kcal d1(including 470 kcal of
livestock products). Large conversion (+42%) to cropland will be
necessary if yield improvements at current rates, combined with
livestock intensification, are the only changes to the agricultural
system (CT1 scenario, see Table 2). A predicted increase in food
demand would result in an overall 77% increase in agriculture-
related GHG emissions, due to increased deforestation rates (a 78%
increase to 7.1 GtCO2e yr1; mostly in Sub-Saharan Africa and
South-East Asia) and increased emissions from livestock, fertilizer
and higher agricultural energy use associated with mechanized
agriculture (a 76% increase to 13.0 GtCO2e yr1). There would
also be large losses of tropical forests (3Mkm2) and other valuable
ecosystems. This scenario, which represents ‘business-as-usual’,
would, therefore, have a number of very detrimental consequences.
The YG1 scenario (‘yield gap closure’) fares a lot better
(Table 2). Previous studies3,33 have already established that decreased
deforestation more than offsets any increase in emissions associated
with sustainable intensification. Here we confirm this, while also
including some relevant emission sources omitted in previous
studies (fertilizer production and agricultural energy use). However,
without demand reductions, cropland would still need to expand
by 5%, pasture by 15%, and GHG emissions would increase by
42% compared with current levels, even with currently-attainable
yields being achieved world-wide. Our results indicate that yield-
gap closures achieved with sustainable intensification would not
meet projected future demands without an increase in agricultural
area and in GHG emissions. Sustainable intensification is crucial;
however, it is unlikely to be sufficient.
Demand-side reductions show further promise. Here we
quantify potential savings from cutting food and agricultural
waste by half, which has previously been suggested as a promising
mitigation strategy26,34,35. These scenarios (CT2 and YG2) reduce
the area of cropland by 14% and GHG emissions by 22–28%
(4.5GtCO2e yr1)compared with their respective baseline
scenarios for 2050 (CT1 and YG1; Table 2). Along with the reduced
cropping area, reducing waste would also reduce fertilizer and
irrigation water demand and associated environmental impacts.
Improvement potentials are similar in scale in all regions; improving
crop storage in developing countries while raising awareness and
setting policy targets for food-waste reduction worldwide could be
viable climate mitigation strategies.
We also tested dietary adaptation as a demand-side measure,
by assuming average diets that are considered to be ‘healthy’ on
the basis of nutritional evidence37–40. Their parameterization is
described in detail in the notes to Supplementary Table 3. The
main alteration from the projected dietary preferences is a reduction
in the consumption of energy-rich food commodities (sugars and
saturated fats, including livestock products) in regions where diets
projected for 2050 exceed established health recommendations. The
necessary alterations vary by regions. For example, in industrialized
regions, the average consumption of livestock products (which
are high in saturated fats) largely exceeds healthy levels37, and a
reduction, or no further increase, could be desirable on health
grounds. However, we recognize that livestock can play a critical
nutritional role in many regions, societies and agricultural systems.
The model ensures that adjusted diets still provide enough protein37,
and a daily calorie intake of 2,500 kcal, through an increase in
pulses and staples. These levels are conservative to avoid potential
deficiency at an individual level. Regional cultural preferences and
crop suitability are retained where possible within these guidelines.
Such altered average diets can hardly capture the complexities of
nutritional requirements across regional populations; but for brevity
we hereafter refer to them as ‘Healthy Diets’.
Scenarios involving Healthy Diets (CT3 and YG3 in Table 2)
reduce the area necessary for cropping by 5%, pasture by 25%
and the total GHG emissions by 45%, compared to the CT2 and
YG2 scenarios. Almost all of these large GHG emission savings (5.6
out of 6 GtCO2e yr1) are associated with livestock reductions.
There are two sources of these savings: a decrease in enteric
fermentation and manure emissions, and carbon sequestration
occurring with a return of some of crop and pasture lands to
natural vegetation. Implementation of healthy diets would therefore
greatly benefit both the environment and the general health of the
population37 in regions where excessive consumption of energy-rich
food occurs, or may develop.
The changes towards healthy diets are greatest in the
industrialized world, which, with some exceptions, also produces
most of the livestock products. Therefore the greatest reductions
in impacts are in temperate zones, rather than the tropics. All
scenarios, including the most optimistic one (YG3), incur losses of
pristine tropical forests due to the combination of large predicted
increases in population and per capita food demand in the tropics,
and the suitability of current forest land for conversion to cropland.
One of the goals of sustainable agriculture is to avoid further
expansion into tropical forests7, but this appears to be unachievable
with changes in the agricultural sector alone.
The results from our model are highly sensitive to some
assumptions, especially those about yields, total population and
livestock system developments; they are somewhat sensitive to
fertilizer assumptions and less sensitive to assumptions about trade
(Table 3 and Supplementary Note). If global population is assumed
to be 14% higher, then the resulting cropland area increases by
14%, and GHG emissions increase by 26%. Under more pessimistic
assumptions, results change even more. For example, if we assume
yield stagnation on today’s level, we would expect the resulting
cropland area to increase by about 27%, (the difference between
today’s yields and yields in CT1). However, the combination
of demand growth and stagnating yields causes expansion into
relatively unsuitable land in regions that exhaust their reserves
Table 2 | Main indicator outputs for six 2050 scenarios.
Units 2009CT1 CT2 CT3 YG1 YG2 YG3
Cropland Mkm215.6 22.2 (+42%) 19.2 (+23%) 18.2 (+17%) 16.4 (+5%) 14.2 (9%) 13.7 (12%)
Pasture Mkm232.8 37.1 (+13%) 33.7 (+3%) 25.4 (23%) 37.7 (+15%) 33.9 (+3%) 25.8 (21%)
Net forest coverMkm226.1 22.6 (14%) 23.9 (8%) 26.0 (0%) 24.0 (8%) 25.9 (1%) 27.2 (+4%)
Tropical pristine forests Mkm27.9 7.2 (10%) 7.3 (8%) 7.5 (6%) 7.5 (6%) 7.7 (3%) 7.7 (3%)
Total GHG emissions GtCO2yr111.4 20.2 (+77%) 15.7 (+38%) 9.3 (19%) 16.2 (+42%) 11.7 (+2%) 5.9 (48%)
Fertilizer use Mt yr1106 154 (+45%) 136 (+29%) 125 (+18%) 190 (+79%) 161 (+51%) 145 (+37%)
Irrigation water use km3yr12,890 6,370 (+120%) 5,410 (+87%) 5,270 (+82%) 4,500 (+56%) 3,830 (+33%) 3,790 (+31%)
Percentages in brackets arerelative to values in 2009. In the two scenarios with no demand management, cropland area increases for 5–42%, pasture for13–15%, there is significant deforestation and an
increase in GHG emissions. YG scenarios farebetter across the indicators, with the exception of fertilizer use. Demand reduction measures on the other hand improve all indicators. Showing middle
values23,24,31,49, uncertainty ranges are up to ±70%. Excluding boreal forests.
Table 3 | One-at-a-time sensitivity analysis for population, yield trends, trade, livestock intensification and fertilizer, using the CT1
or YG1 scenario as a baseline.
Sensitivity scenario Change in inputs from the
baseline scenario
Change in key outputs Relative sensitivity index
GHG emissions
Cropland GHGs
UN high population 2050 population from 9.6 to
10.9 billion (+14%)
25.3 (+14%) 25.4 (+26%) 1.05 1.90
UN low population 2050 population from 9.6 to
8.3 billion (-14%)
19.0 (14%) 15.0 (26%) 1.05 1.89
Stagnating yields Average yield from 1.8 to
1.3 tC ha1(27%)
31.2 (+41%) 28.8 (+43%) 1.44 1.51
Two-fold increase in yield improvement rates Average yield from 1.8 to
2.3 tC ha1(+27%)
17.9 (19%) 16.1 (20%) 0.72 0.76
Increased trade from baseline scenarioTotal trade from 103,300 to
162,800 tC (+58%)
21.6 (3%) 19.7 (2%) 0.02 0.04
Fertilizer use eciency in YG1 improved further Total fertilizer use from 189,820
to 151,748 ktN (20%)
16.4 (0%) 15.5 (4%) 0 0.21
GHG emissions
Pasture GHGs
Livestock densities and feed as in 2009 Livestock products per area from
44.5 to 21.8 kgCha1(51%)
73.3 (+98%) 27.7 (+37%) 1.91 0.73
Increased stocking density, but no intensification Livestock products per area from
44.5 to 33.5 kgCha1(25%)
47.9 (+29%) 23.1 (+15%) 1.18 0.59
Intensification, but 2009 stocking density Livestock products per area from
44.5 to 34.4 kgCha1(23%)
50.5 (+36%) 24.5 (+22%) 1.59 0.95
We varied the inputs based on alternative projectionsin the literature, or if such explicit projections are missing, by what we consider to be plausible levels. The larger the relativesensitivity index
(last two columns, either positive or negative), the more sensitive the model outputs are.Calculated as the ratio between the change in the input parameter and the relative change in the output.
The increased trade scenario assumes that any surplus cropland in land-rich countries(N. America, W. Europe) will not be abandoned, but used for exports into regions with largest cropland deficits.
Without accounting for increased GHG emissions from transport, this incursa small net emission saving.
of suitable land, resulting in a higher, 41% increase in cropland
area required.
Our results show that only when strategies include significant
elements of demand reduction is it possible to prevent an increase
in agricultural expansion and agriculture-related GHG emissions.
As previously suggested, the reduction of meat consumption could
be tackled with economic incentives (such as a carbon tax) and
the livestock sector should be included into a comprehensive
climate mitigation policy11. Defining appropriate incentives may
require some policy innovation and experimentation, but a strong
commitment for devising and monitoring them seems essential41.
Nutritional experts40 have called for healthy nutrition to be elevated
to the highest priority in national agendas, and that health
requirements should dictate agricultural priorities, not vice versa.
Our results are consistent with the findings of the recent IPCC
report which reported a significant, but uncertain, potential for
GHG reduction in agriculture from demand-side measures such as
dietary change and waste reduction42; at the same time, this delivers
better outcomes for food security and environmental impacts.
This study focuses on the overall global picture, but it is
important to be aware of the demand differences between regions,
and farming systems within regions. The South Asian and Sub-
Saharan African regions are predicted to be the most critical in terms
of the agricultural land expansion needed to meet the demand, in
all scenarios. Water is a local issue, but even on regional levels the
estimated amount of irrigation needed to support higher yields is
challenging. The irrigation demand in South Asia, for example, is
projected to increase by 80% in the YG3 scenario, and up to 200%
in the CT1 scenario (Supplementary Table 12). Such large increases
in irrigation water supply may not be possible, given that today
the use of groundwater is already excessive in many places. For
example, the extraction from the Upper Ganges aquifer is already
GHG emissions from agriculture and LUC
2° target 2009 emissions
GtCO2e yr−1
Figure 2 | Diagram showing the total GHG emissions from agriculture and
land-use change due to agricultural expansion, for the six scenarios. The
2009 emissions from these sources are shown for comparison, as is the
target in 2050 for avoiding dangerous climate change45 (which should also
accommodate energy, industry, and land-use-change emissions from other
non-agricultural sources, such as settlement expansion). Agricultural
energy use is already included and represents 2–3GtCO2e.
50 times larger than its estimated recharge rate43. Yield increases
from increased irrigation may not be fully realized, implying that, to
meet the demand, even greater expansion of cropland into natural
landscapes would be necessary.
The model presented here would benefit from further
developments to include yield as a function of availability of water
and fertilizer, and the inclusion of climate change as a driver of
yield changes and irrigation demand. This would enable estimation
of how shortfalls in irrigation water availability might affect future
food production. Bioenergy scenarios also lie outside the scope of
this paper; unless food demand patterns change significantly, there
seems to be little spare land for bioenergy developments without a
reduction of food availability. It is important to note that the model
results we present here are conservative in estimating the extent of
agricultural land use and its associated emissions in the absence of
these model limitations.
Although it is theoretically possible to decarbonize energy supply,
such complete reductions are unattainable in the livestock part
of the agricultural sector. Although there are many mitigation
options in agriculture44, our study indicates that a decrease in overall
agriculture-related emissions can only be achieved by employing
demand-side reductions. The agriculture-related emissions in our
business-as-usual scenario (CT1) alone almost reach the full 2C
target emissions allowance in 2050 (21 ±3 GtCO2e yr1; ref. 45).
Even scenario YG2, with yield-gap closures coupled with halving of
food waste, reaches more than a half of the target, leaving only the
other half for all other energy and industrial processing emissions
(Fig. 2). The share of emissions related to agriculture may therefore
increase in the future. However, to date, global food and land-
use scenarios have received relatively little consideration in climate
change mitigation policies compared with the consideration given
to the energy supply and end-use sectors.
Reducing emissions from agriculture is essential to reduce
the risks of dangerous climate change. The agricultural industry
must strive to improve yields and food distribution, but improved
diets and reductions in food waste are also essential to deliver
emissions reductions, and to provide enough food for the global
population of 2050.
Future land-use predictions are based on a model that describes the physical
characteristics of global land-use and agricultural systems. This model was
composed by collecting and fitting together the empirical data from many global
datasets. It has two crucial components: the land-use distribution analysis and the
agricultural biomass flow map. The analysis of land-use distribution was achieved
by overlaying data on global biomes21, current land use22,23,46 and agricultural
suitability10 in a Geographical Information System.
The agricultural biomass flow map allows us to model changes in food
supply chains explicitly, together with livestock management systems, agricultural
waste, food waste and dietary preferences. It is constructed in the manner of a
material flow analysis, so that the flows always add up to the total vegetation
growth on cropland and pasture, measured as net primary productivity (NPP) in
grams of carbon. It follows the allocation of agricultural vegetation biomass to
harvest, residues, losses and ecosystems in the first instance, and then to food,
feed, fibre, fuel, soil recycling, losses and intermediate steps. This biomass flow
map is first parameterized with 2009 data. FAOSTAT statistics24 provide most of
the data, supplemented by some characterization of livestock feed systems25,
agricultural residue quantification and uses25,47, and losses at each stage26,29 .
The model with these two major components was used to assess the
consequence of future food demands and changes in the agricultural systems in
12 global regions. Calculations can be described conceptually as the
following sequence:
Future consumption for each commodity in a region was calculated as a
product of the per capita future dietary preferences associated with
socio-economic changes as projected by the FAO (ref. 2) and regional population
from the UN mid-range projections36. Aggregated by carbon mass, these add up
to a 57% increase in food consumption, underpinned by a 75% increase in
cropland productivity. Healthy dietary preferences37–40 are taken as an alternative.
Required future production is calculated on the basis of the predicted future
consumption and the characterized agricultural biomass flow map. We assume
that agricultural systems in 2050 are different from those of today, in terms of the
increased share of cropland-grown feed for livestock, and improved livestock
efficiency. Trade between regions is assumed to remain the same. Changes in
agricultural waste are implemented at this stage.
Future cropland area is a result of the required future production and yields.
The Current Trends (CT) scenarios assume yields in each region will continue to
increase linearly at current rates, which are taken from a recent global yield
study4. The Yield Gap (YG) scenarios assume that sustainable intensification will
achieve yield gap closures in all regions, achieving the current potentially
attainable yields for their agro-ecological zone. Yield gaps for each region and
crop are taken from the GAEZ study10.
Future pasture area is a result of future demand for grazing and the assumed
livestock stocking densities. Unfortunately there are no statistics that could be
used to estimate possible stocking densities on global levels. We compared results
from a global dynamic vegetation model, a previous livestock energy model25 ,
and livestock product statistics24, to determine that some regions can significantly
increase densities (Latin America, SE Asia), whereas in others they are already
very high (W. Europe, N. America). Because of many unknowns (about stocking
densities as well as livestock management systems), pasture areas are
highly uncertain.
The location of future cropland and pasture expansions (or retractions) is
based on the land suitability component of the land distribution analysis,
described above. Losses of ecosystems and GHG emissions are also dependant on
the distribution of agricultural expansion over current land use and biomes in
each region.
Fertilizer and irrigation use is estimated on the basis of current trends in
their uses and total cropland area for each scenario. The YG scenarios assume an
increase in irrigation use efficiency, whereas fertilizer use is set at high enough
levels to support optimum yields.
GHG emissions from land-use change (LUC) are calculated on the basis of
the ‘before and after’ land carbon pools, which depend on the biome and land
use. We used the published methodology and parameters to obtain GHG values
of ecosystems48. Only emissions from agriculture expansion and contraction
are included.
GHG emissions from agriculture associated with fertilizer use and
production, rice paddy methane emissions, emissions from enteric fermentation
and manure management, as well as energy use in mechanization, are also
calculated. Calculations are based on scaling up today’s emissions49,50 linearly with
emission sources.
Received 7 April 2014; accepted 26 July 2014;
published online 31 August 2014
1. Houghton, R. A. Carbon emissions and the drivers of deforestation and forest
degradation in the tropics. Curr. Opin. Environ. Sustain. 4, 1–7 (2012).
2. Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050. The
2012 Revision (FAO, 2012).
3. Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the
sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108,
1–5 (2011).
4. Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient
to double global crop production by 2050. PLoS ONE 8, e66428 (2013).
5. Reaping the Benefits; Science and the Sustainable Intensification of Global
Agriculture (The Royal Society, 2009).
6. Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people.
Science 327, 812–818 (2010).
7. Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).
8. Garnett, T. et al. Sustainable intensification in agriculture: Premises and
policies. Science 341, 33–34 (2013).
9. Mueller, N. D. et al. Closing yield gaps through nutrient and water
management. Nature 490, 254–257 (2012).
10. IIASA and FAO Global Agro-ecological Zones (GAEZ v3.0)(IASSA/FAO, 2010);
11. Ripple, W. J. et al. Ruminants, climate change and climate policy. Nature Clim.
Change 4, 2–5 (2013).
12. Stehfest, E. et al. Climate benefits of changing diet. Climatic Change 95,
83–102 (2009).
13. Smith, P. Delivering food security without increasing pressure on land. Glob.
Food Sec. 2, 18–23 (2013).
14. Smith, P. et al. Competition for land. Phil. Trans. R. Soc. 365, 2941–2957 (2010).
15. Westhoek, H. et al. Food choices, health and environment: Effects of cutting
Europe’s meat and dairy intake. Glob. Environ. Change 26, 196–205 (2014).
16. Hedenus, F., Wirsenius, S. & Johansson, D. J. A. The importance of reduced
meat and dairy consumption for meeting stringent climate change targets.
Climatic Change 124, 79–91 (2014).
17. Rosegrant, M. W. et al. International Model for Policy Analysis of Agricultural
Commodities and Trade (IMPACT)(IFPRI, 2008).
18. Havlík, P. et al. Climate change mitigation through livestock system transitions.
Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).
19. Netherland’s environmental assessment agency IMAGE User Manual (PBL,
20. Schmitz, C. et al. Land-use change trajectories up to 2050: Insights from a
global agro-economic model comparison. Agric. Econ. 45, 69–84 (2014).
21. Ramankutty, N., Foley, J. A. in ISLSCP Initiat. II Collect (eds Hall, F. G. et al.)
(ORNL DAAC, 2010);
22. Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet:
1. Geographic distribution of global agricultural lands in the year 2000. Glob.
Biogeochem. Cycles 22, 1–19 (2008).
23. FAO Global Forest Resources Assessment 2010 (2010);
24. FAO FAOSTAT (FAO, 2013);
25. Wirsenius, S. Human Use of Land and Organic Materials Modeling the Turnover
of Biomass in the Global Food System PhD thesis, Chalmers Univ. Technology
and Göteborg Univ. (2000).
26. Gustavsson, J., Cederberg, C., Sonnesson, U., van Otterdijk, R. & Meybeck, A.
Global Food Losses and Food Waste (FAO, 2011).
27. Scarlat, N., Martinov, M. & Dallemand, J-F. Assessment of the availability of
agricultural crop residues in the European Union: Potential and limitations for
bioenergy use. Waste Manage. 30, 1889–1897 (2010).
28. Haberl, H. et al. Quantifying and mapping the human appropriation of net
primary production in Earth’s terrestrial ecosystems. Proc. Natl Acad. Sci. USA
104, 12942–12947 (2007).
29. Oerke, E. & Dehne, H. Global crop production and the efficacy of crop
protection—current situation and future trends. Eur. J. Plant Pathol. 103,
203–215 (1997).
30. Faurès, J-M., Svendsen, M. & Turral, H. in Water Food, Water Life
A Compr. Assess. Water Manage. Agric. (ed. Molden, D.) 353–394
(IWMI/Earthscan, 2007).
31. FAO AQUASTAT Database (FAO, 2013);
32. FAO FertiStat—Fertilizer Use by Crop Statistics (FAO, 2013);
33. Valin, H. et al. Agricultural productivity and greenhouse gas emissions:
Trade-offs or synergies between mitigation and food security? Environ. Res.
Lett. 8, 035019 (2013).
34. The Future of Food and Farming. Final Project Report (The Government Office
for Science, 2011).
35. Kummu, M. et al. Lost food, wasted resources: Global food supply chain losses
and their impacts on freshwater, cropland, and fertiliser use. Sci. Total Environ.
438, 477–489 (2012).
36. United Nations Population Division United Nations Population Projections 2013
Revision (United Nations, 2013);
publications/world-population-prospects-the-2012- revision.html
37. Willett, W. Eat, Drink, and be Healthy The Harvard Medical School Guide to
Healthy Eating (Simon and Schuster, 2001).
38. WHO & FAO Joint WHO/FAO Expert Consultation on Diet, Nutrition and the
Prevention of Chronic Diseases (WHO, 2003).
39. The American Heart Association’s Diet and Lifestyle Recommendations
(American Heart Association, 2014);
40. Simopoulos, A. P., Bourne, P. G. & Faergeman, O. Bellagio report on healthy
agriculture, healthy nutrition, healthy people. Nutrients 5, 411–423 (2013).
41. Garnett, T. Changing What We Eat (The Food Climate Research
Network, 2014).
42. Smith, P. et al. in Climate Change 2014: Mitigation of Climate Change
(eds Edenhofer, O. et al.) Ch. 11 (IPCC, Cambridge Univ. Press, 2014).
43. Smith, P. et al. How much land-based greenhouse gas mitigation can be
achieved without compromising food security and environmental goals? Glob.
Change Biol. 19, 2285–2302 (2013).
44. Gleeson, T., Wada, Y., Bierkens, M. F. P. & van Beek, L. P. H. Water balance of
global aquifers revealed by groundwater footprint. Nature 488, 197–200 (2012).
45. Rogelj, J. et al. Emission pathways consistent with a 2 C global temperature
limit. Nature Clim. Change 1, 413–418 (2011).
46. Schneider, A., Friedl, M. A. & Potere, D. A new map of global urban extent
from MODIS satellite data. Environ. Res. Lett. 4, 044003 (2009).
47. Streets, D. G., Yarber, K. F., Woo, J. H. & Carmichael, G. R. Biomass burning in
Asia: Annual and seasonal estimates and atmospheric emissions. Glob.
Biogeochem. Cycles 17, 1099 (2003).
48. Anderson-Teixeira, K. J. & DeLucia, E. H. The greenhouse gas value of
ecosystems. Glob. Change Biol. 17, 425–438 (2011).
49. Bajželj, B., Allwood, J. M. & Cullen, J. M. Designing climate change mitigation
plans that add up. Environ. Sci. Technol. 47, 8062–8069 (2013).
50. European Commission, Joint Research Centre & Netherlands Environmental
Assessment Agency Emission Database for Global Atmospheric Research
(EDGAR), Release Version 4.2., 2012 (JRC, 2014);
This work was funded by a grant to the University of Cambridge from BP as part of their
Energy Sustainability Challenge.
Author contributions
B.B., J.M.A., K.S.R., C.A.G., J.S.D. and E.C. developed the model, B.B., P.S., J.M.A. and
K.S.R. designed the study/scenarios, B.B., K.S.R. and C.A.G. analysed the outputs, and all
authors wrote the paper with B.B. leading.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
permissions information is available online at
Correspondence and requests for materials should be addressed to B.B.
Competing financial interests
The authors declare no competing financial interests.
... Global warming negatively impacts plant productivity, development [1] and crop yields [2] in many areas of the globe, inducing important cascade effects on related ecosystems services, including the C sink capacity of terrestrial ecosystems, and posing further challenges to food provisioning in view of the forecasted increase in population [3]. Adaptation strategies and solutions to minimize the negative impacts of climate change and climate extreme on agroforestry systems are a key priority [4][5][6]. ...
Full-text available
Hydrochar, carbon-rich material produced during the thermochemical processing of biomass, is receiving increased attention due to its potential value as soil amendment. It can increase agroforestry systems’ productivity through direct and indirect effects on growth and soil quality. Hydrochar may also directly help mitigate climate change by sequestering stable carbon compounds in the soil and perhaps indirectly through increased C uptake by trees. In this research, we aim to evaluate how the application of hydrochar produced by two feedstock types, Cynara cardunculus L. (Hc) residuals and sewage sludge (Hs), and in two different doses (3 and 6 kg m−2) could improve the growth and water use efficiency of Populus alba L., a fast-growing tree species largely used in agroforestry as bioenergy crops and in C sequestration. We considered five plants per treatment, and we measured apical growth, secondary growth, leaf area and intrinsic water use efficiency in each plant for the whole growing season from February to October 2022. Our results highlighted that hydrochar applications stimulate the growth and water use efficiency of plants and that the double dose (6 kg m−2) of both hydrochars, and particularly Hc, had positive effects on plant performance, especially during extremely hot periods. Indeed, the year 2022 was characterized by a heat wave during the summer period, and this condition allowed us to evaluate how plants, growing in soils amended with hydrochar, could perform under climate extremes. Our findings showed that the control plants experienced severe damage in terms of dried stems and dried leaves during summer 2022, while hydrochar applications reduced these effects.
... Many variables are associated with sustainable food production, including efficiently using land, utilizing all produced food, and creating food trade regimes [3]. Improving crop yield is insufficient to meet the projected global food demand, and focusing on decreasing food waste can aid in meeting the demand and lowering carbon emissions [4]. Additionally, global water demand is expected to increase by 20 to 30% by 2050, and the agriculture sector currently comprises approx. ...
Full-text available
An agent-based modeling framework is developed and employed to replicate the interactions among urban farms. The objectives are to efficiently manage an urban farm’s food, energy, and water resources, decrease food waste, and increase the food availability for the local community. A case study of eleven farms was investigated in Vancouver, Canada to study the linkages between the resources in the urban food, energy, and water nexus. Each urban farm in the simulation belonged to a community microgrid generating electricity from solar and wind. The local farms aimed to provide fresh produce for their respective local communities. However, at some points, they lacked supply, and at other points, there was excess supply, leading to food waste. Food waste can be converted into fertilizers or bioenergy. However, an alternative solution must be employed due to the natural resources required for production, efficiently managing resources, and adhering to sustainability guidelines. In this paper, an optimization framework was integrated within the agent-based model to create a micro supply chain. The supply chain directly linked the producers with the consumers by severing the links involved in a traditional food supply. Each urban farm in the study collaborated to reduce food wastage and meet consumer demands, establishing farmer-to-farmer exchange in transitional agriculture. The optimization-based micro supply chain aimed to minimize costs and meet the equilibrium between food supply and demand. Regular communication between the farms reduced food waste by 96.9% over 16 weeks. As a result, the fresh food availability increased for the local community, as exemplified by the consumer purchases over the same period. Moreover, the simulation results indicated that the renewable energy generation at the community microgrids aided in the generation of 22,774 Mwh from solar and 2568 Mwh from wind. This has the potential to significantly reduce CO2 emissions in areas that heavily rely on non-renewable energy sources.
... The researcher is thus an actor and stakeholder of collective action. system than trying to change each component of the system, e.g., the agricultural component (BajŽelj et al., 2014). The project, therefore, mainly targets consumers and farmers that are currently present in the region, without neglecting other agrifood system actors (Lamine, 2015). ...
Full-text available
Many agrifood systems around the world can be characterized as unsustainable. Research is increasingly required to inform the necessary radical transformations of the ways we produce, process, transport, and consume food. This article presents the research approach and methods of an ongoing project carried out at a long-term social–ecological research site, the Zone Atelier Plaine and Val de Sèvre (western France). The research project presented here, Aliment'Actions, started in 2018 and within 10 years of its implementation seeks to study and trigger transformation to enhance the sustainability and resilience of the regional agrifood system. Its research agenda contains four types of actions: (a) backdrop actions that enhance communication and trust between researchers and local stakeholders, (b) targeted actions that are conducted in specific villages with a wide range of stakeholders to elaborate and implement various transformation levers, (c) assessment actions evaluating the effects of different interventions, and (d) communication and result from dissemination actions. Overall, these actions aim to co-produce knowledge, raise awareness regarding challenges in the food system, envision new interactions between stakeholders, collectively generate innovative ideas, and catalyze actions oriented toward agrifood system transformation. The project implementation is adaptive and iterative, from theory to practice. This Methods paper puts this ongoing project into the perspective of other place-based research initiatives and provides insights on how to foster the engagement of non-academic actors in transdisciplinary research supporting agrifood system transformation.
... It was previously estimated that 1 in 7 farmers could not afford sufficient fertilizers to meet crop requirements, impacting their ability to produce food (IAASTD, 2009). In contrast, nutrient pollution through fertilizer overuse and insufficient wastewater treatment, can trigger toxic algal blooms and create coastal dead zones threatening human and animal health (Bajželj et al., 2014). These effects will be exacerbated by climate change. ...
Full-text available
Food systems depend on reliable supplies of phosphorus to fertilize soils. Since 2020, a pandemic, geopolitical disputes, trade wars and escalating fuel prices have driven a >400% increase in phosphorus commodity prices, contributing to the current food crisis. The Russia-Ukraine conflict has disrupted phosphate trade further. Concurrently, phosphorus losses to freshwaters, through insufficient municipal wastewater treatment and inappropriate fertilizer use and land management practices, are a significant threat to water quality globally. Despite precariously balanced food and water security risks, nations are largely unaware of their “phosphorus vulnerability” and phosphorus is markedly absent in national and global policies addressing food and water security. Phosphorus vulnerability can be described as the degree to which people/systems are susceptible to harm due to the physical, geopolitical and socio-economic dimensions of global phosphorus scarcity and pollution. Here, we bring the current price spike into focus, highlighting the drivers, policy responses and their consequences. We highlight the need for an integrated assessment of phosphorus vulnerability that considers environmental, socio-economic and climate change risks across scales. We illustrate how reducing phosphorus waste, increasing phosphorus recycling, and wider system transformation can reduce national reliance on imported phosphorus, whilst enhancing food and water security. The current crisis in fertilizer prices represents a wake-up call for the international community to embrace the global phosphorus challenge.
... There is an absolute necessity to increase global food production by up to 70% (Schmidhuber & Tubiello, 2007) to keep pace with rapid population growth (Tilman, Balzer, Hill, & Befort, 2011) which is predicted to reach nearly 9.7 billion by 2050 (Chelaifa et al., 2020) and roughly 11 billion by 2100 (Mountford & Rapoport, 2015;UN, 2019). Since early agricultural history to the 1950s, the global farming sector concentrated on increasing productive arable land area to enable an increase in food production (Bajţelj et al., 2014;Lal, 2008;Norton, Alwang, & Masters, 2007). Then, in the late 1960s, the approach changed to improving the sustainability and productivity of existing land. ...
Full-text available
With the rapid global trend towards mechanized, continuous and dense cropping systems that provide agricultural efficiency to meet consumer demand, soil compaction has become a recognized problem. Soil compaction under modern machines has had immense impact on productive land‘s physical, chemical and biological properties, including soil-water storage capacity, fertiliser use efficiency, and plant root architecture. As a result, farms are experiencing substantially reduced crop yields and economic returns. The percentage of soil compaction increases with increased soil clay fraction. Numerous investigations have been conducted to evaluate the technical, economic and soil-crop efficiency of compaction mitigation strategies, but deep tillage has not received sufficient consideration, particularly in relation to high clay content soils. This study was conducted to technically and economically evaluate a range of deep ripping systems, and study the effect of tillage on soil and crop grown on cohesive soils. A series of field experiments were conducted to parametrise a soil tillage force prediction model, previously developed by Godwin and O‘Dogherty (2007) and the Agricultural Productions Systems sIMulator (APSIM) developed by the Agricultural Production Systems Research Unit in Australia (Holzworth et al., 2014; Keating et al., 2003). The behaviour of soil physical properties, power requirements of ripping operations and cost, and agronomic and economic performance of sorghum and wheat were assessed at the University of Southern Queensland‘s research ground in Toowoomba, Queensland (Australia) over two consecutive seasons (2015-16 and 2016-17). The work was conducted by replicating the soil conditions commonly found in non-controlled or ‗random‘ traffic farming systems, referred to as RTF. Sorghum was also grown at a commercial farm located in Evanslea near Toowoomba, under controlled traffic (CTF) conditions (a farm system based on a permanent lanes for machinery traffic) during the 2018 summer crop season. The soil types at the two sites are Red Ferrosol (69.1% clay, 10.0% silt, and 20.9% sand) and Black Vertosol (64.8% clay, 23.4% silt, and 11.8% sand). Three levels of deep ripping depth, namely, Deep Ripping 1 (D1= 0-0.3 m), Deep Ripping 2 (D2= 0- 0.6 m), and Control (C= no ripping) were applied using a Barrow single tine ripper at the Ag plot site - USQ, and a Tilco eight-tine ripper was used at the Evanslea site. The tillage operations were performed at 2.7 km/h. A predetermined optimum N fertiliser rate was applied after sorghum and wheat sowing at the Ag plot site. The field experiments were conducted according to the randomized complete block design (RCBD). The Statistical Package for Social Scientists (SPSS) software was utilized to analyse the significance of the differences between the variables at the probability level of 5% as the least significant difference (LSD). The statistical analysis results showed that the D2 treatment significantly reduced soil bulk density and soil strength by up to 5% and 24% for Red Ferrosol soil, and by up to 6% and 40% for Black Vertosol soil respectively, and increased water content compared with the D1 and C treatments. Overall results showed that D2 was superior in ameliorating the properties of both soils. In both soils, energy requirement results showed that tillage draft force and tractor power requirements were dependent on tillage depth, but for both tillage treatments, energy consumption was slightly lower for the CTF system (Evanslea site) than the RTF system at Ag plot site. Crop performance results showed that at the Ag plot site, the grain and biomass yields were highest by up to 19% for sorghum and by up to 30% for wheat when the D2 treatment was applied, compared to the D1 and C treated crop yield components. Also, the grain and biomass yields were highest for fertilised soil by up to 10% for sorghum and by up to 16% and 25% for wheat respectively, in comparison with the non-fertilised treatments soils yield. Fertilising of D2 treated soil produced the highest significant yield of sorghum grain (5360 kg/ha), biomass (13269 kg/ha), wheat grain (2419 kg/ha), and biomass (5960 kg/ha) compared to the yield of the other treatment interactions. However, at Evanslea site, the D1 treatment showed significantly higher yield and yield components for sorghum compared with C practice (by up to 17% higher yield), and no differences were observed for treatment D2. Economically, the D1 treatment required the lowest total operational cost at both sites, which was estimated at AUD125/ha and AUD25.8/ha at the Ag plot and Evanslea sites, respectively. These results compare to AUD139.3/ha (Ag plot) and AUD30.8/ha (Evanslea) for the D2 ripping system. With regard to economic returns, at the Ag plot site, D2 yielded the highest sorghum gross benefit (AUD1422/ha) and net benefit (AUD1122/ha), wheat gross benefit (AUD590/ha) and net benefit (AUD482.3/ha), 2017 season gross benefit (AUD 2011.7/ha) and 2017 season net benefit (AUD 1604.7/ha), compared to D1 and C soil benefits. The economic fertiliser application at this site achieved the highest gross benefit for sorghum (AUD1384.2/ha), wheat (AUD555.6/ha), and 2017 season (AUD1939.8/ha) respectively, in comparison with the non-fertilised soils‘ total return. Also, fertilised D2 treated soil resulted in the highest sorghum gross benefit (AUD1512.9/ha) and net benefit (AUD1170.3/ha), wheat gross benefit (AUD633.7/ha) and net benefit (AUD492.4/ha), 2017 season gross benefit (AUD2146.6/ha), and net benefit (AUD1662.7/ha) compared to other interactions‘ benefits. At the Evanslea site, D1 significantly increased sorghum gross benefit and net benefit by up to 17% (AUD2277.9/ha) and by up to 20% (AUD1825.5/ha), respectively compared to C benefits, and no differences were observed with treatment D2. The average of APSIM derived results for the long-term (1980-2017) at the Ag plot site showed that the D2 treatment reported consistently higher grain sorghum (4192 kg/ha), biomass (11454 kg/ha), wheat grain (3783 kg/ha), and biomass (10623 kg/ha), compared to the D1 and C treatments‘ yields under the same long-term conditions. However, at the Evanslea site, for long-term (1980-2018), APSIM simulation showed that D1 treatment increased the yield of sorghum grain and biomass significantly by up to 10% (5823 kg/ha) and 11% (12171 kg/ha), respectively compared to C treatment‘s production, but these increases were found not significant with the D2 yields‘ components. APSIM model simulation of field experiment conditions during 2017 season at the Ag plot site showed that the D2 treatment also had the highest significant yield of sorghum grain (5284 kg/ha), biomass (12488 kg/ha), wheat grain (2341 kg/ha) and biomass (6081 kg/ha) compared to the C and D1 crop yields. Similarly, APSIM model simulation of field experiment circumstances during the 2018 season at the Evanslea site showed that the D1 treatment produced the highest yield of sorghum grain (7129 kg/ha), biomass (13364 kg/ha) yields, compared to the C and D1 crop yields. Overall, both the long and short-term model outputs were in good agreement with experimental data, suggesting beneficial effects of deep tillage in improving cereal crops‘ productivity in this region. Moreover, in comparison with the study findings, the model prediction error rate was ±7, which indicates that the developed model approach is valid and calibrated during this study. Results derived from the G&O soil tillage mechanics model under the Ag plot and Evanslea soil conditions showed that the required tractive force increases with the increasing operation working depth. Furthermore, the D1 was superior, requiring the lowest draft force at Ag plot (7.48 kN) and Evanslea (19.65 kN) soils, compared to the D2 required forces which were 43.28 kN and 41.41kN at both sites, respectively. In general, the model values were in line with the experiments' draft forces and when compared with the study readings, the model prediction error rate was ±8, which indicates that it is also valid and calibrated during this study. Finally, the study provides conclusions and recommendations that contribute to crop production improvement in the face of recurrent and increasing challenges, as well as emphasizing the necessity of correct management and cultivation of economically important crops after the application of deep ripping to produce accurate results that serve decision-making in the agricultural sector.
... However, food security faces a number of challenges across both production and consumption. With the growing world population, by 2050, it is projected that we will need 120% more water, 42% more cropland, lose 14% more forest, and produce 77% more greenhouse gas emissions [2]. Paradoxically, only in European Union, in 2016, 2.6 million Citation: Teixeira, A.; Sánchez-Hernández, E.; Noversa, J.; Cunha, A.; Cortez, I.; Marques, G.; ...
Full-text available
The harmful effect of synthetic fungicides on the environment and the development of resistance by fungi raises concerns about their security and future efficacy. In this work, we investigated plant by-products with the antifungal activity that could be safe alternatives to conventional fungicides. The in vitro antifungal potential of plant by-product extracts showed that garlic peel extract (GPE) was the most effective against several phytopathogenic fungi. Accordingly, in ex situ assays with apples, GPE significantly reduced the lesion size caused by subepidermal inoculation with Colletotrichum acutatum spores. In addition, Saccharomyces cerevisiae mutant strains affected in ergosterol synthesis showed higher resistance to GPE than the parental strain, indicating that the extract might target an intermediate of this pathway. Moreover, GPE affects the cell wall, given that bck1 and mkk1/mkk2 mutants were less able to cope with the stress because of the impairment of the remodeling mechanisms. Regarding the apoptosis-deficient mutant yca1, sensitivity was similar to that of the parental strain, suggesting that the extract does not induce apoptosis. A diverse group of sulfur compounds was identified by gas chromatography-mass spectrometry (GC/MS). Our findings contribute to the elucidation of the antifungal mechanism of GPE and highlight its potential as an alternative biofungicide in agriculture.
... To date, almost all land resources have been depleted and the reduction of agricultural land continues due to urbanization and soil degradation. This suggests that providing mankind with food is possible only by increasing crop yields [2,3,7]. So in the sphere of increasing cereal productivity (the main source of food) there are three main fields: genetic -selection development; creation and improvement of agrotechnologies; location optimization and specialization of production [24]. ...
Full-text available
The article presents field research results on studying the effect of treating maize plants with growth regulating biological products on the formation of productivity of lines-parental components for optimizing the elements of cultivation technology. The experimental scheme included the effect of Bio-gel and Helafit®-combi biological products on the productivity of parental components of different FAO groups and genetic plasms of maize hybrids at different densities of 70,000, 80,000, 90,000 plants ha-1. Studies have shown that for the maximum manifestation of the "weight of 1,000 grains" indicator the optimal density is 70,000 plants ha-1. The increase in yield is positively influenced by the increase in the weight of 1,000 seeds, which is due to the line genotype and the use of the Bio-gel, Helafit®-combi biologically active products. Pre-sowing treatment of maize seeds with Bio-gel and Helafit®-combi increased laboratory seed germination. With the Bio-gel product applied, the laboratory germination increased by an average of 1.5%, with Helafit®-combi used seed germination increased by 2.4%. In our studies, the maximum seed yield in the early-ripening line of the DK 281 parental component was recorded at a density of 90,000 plants ha-1 and the treatment with Helafit®-combi and amounted to 3.65 t ha-1. The maximum yield of the DK 247 parental component was observed at a density of 80,000 plants ha-1 and the treatment with Helafit®-combi and amounted to 4.89 t ha-1. Mid-late lines DK 411 and DK 445 parental components showed the highest yields at densities of 70,000 plants ha-1 and after the treatment with Helafit®-combi which amounted to 4.65 and 6.30 t ha-1 , respectively.
Full-text available
Sustainable intensification (SI) of agriculture is required to satisfy the growing populations' nutritional needs, and therefore food security while limiting negative environmental impacts. The study aims to investigate the global scientific output of sustainable intensification research from 2010 to 20 August 2021. The data was retrieved from the Web of Science (WoS) Core Collection and was analyzed using a bibliometric method and VOS viewer to determine the most productive countries and organizations by collaboration analysis, including the keywords to analyze the research hotspots and trends, and the most cited publications in the field. From the 1,610 studies published in the theme of sustainable agriculture by 6,346 authors belonging to 1,981 organizations and 115 countries, the study found an increased number of publications and citations in 2020, with 293 publications and 10,275 citations. The United States ranked highest in countries collaborating with the most publications in the field. The occurrence of keywords like “food security”, “climate change”, “agriculture”, “ecosystem services”, “conservation agriculture”, “Sub-Sahara Africa”, “Africa”, “biodiversity”, and “maize” in both author and all keywords (author and index) reveal the significance of sustainable intensification in Africa, as a solution to food insecurity under climate change conditions. The availability of funding agencies from big economies explains the growing interest by developing countries in the SI of agriculture research due to the growing population, food insecurity, and access to limited land for farming.
Full-text available
Humanity is challenged with making progress toward global biodiversity, freshwater, and climate goals, while providing food and nutritional security for everyone. Our current food and land-use systems are incompatible with this ambition making them unsustainable. Papers in this special feature introduce a participatory, integrated modeling approach applied to provide insights on how to transform food and land-use systems to sustainable trajectories in 12 countries: Argentina, Australia, Canada, China, Germany, Finland, India, Mexico, Rwanda, Sweden, the UK, and USA. Papers are based on work completed by members of the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative, a network of in-country research teams engaging policymakers and other local stakeholders to co-develop future food and land-use scenarios and modeling their national and global sustainability impacts. Here, we discuss the key leverage points, methodological advances, and multi-sector engagement strategies presented and applied in this collection of work to set countries and our planet on course for achieving food security, biodiversity, freshwater, and climate targets by 2050.
Full-text available
This thesis is directed towards the issue of the long-term demand and supply of biomass for food, energy and materials. In the coming decades, the global requirements for biomass for such services are likely to increase substantially. Therefore, improved knowledge of options for mitigating the long-term production requirements and the associated effects on the Earth system is essential. The thesis gives a thorough survey of the current flows of biomass in the food system. This survey was carried out by means of a physical model which was developed as part of the work. For eight world regions, the model is used to calculate the necessary production of crops and other phytomass from a prescribed end-use of food, efficiency in food production and processing, as well as use of by-products and residues. The model includes all major categories of phytomass used in the food system, depicts all flows and processes on a mass and energy balance basis, and contains detailed descriptions of the production and use of all major by-products and residues generated within the system. The global appropriation of terrestrial phytomass production induced by the food system was estimated to some 13 Pg dry matter per year in 1992-94. Of this phytomass, about 0.97 Pg, or 7.5 percent, ended up as food commodities eaten. Animal food systems accounted for roughly two-thirds of the total appropriation of phytomass, whereas their contribution to the human diet was about one-tenth. Use of by-products and residues as feed, and for other purposes within the food system, was estimated to about 1.8 Pg dry matter, or 14 percent of the total phytomass appropriation. The results also show large differences in efficiency for animal food systems, between regions as well as between separate commodities. The feed conversion efficiencies of cattle meat systems were estimated to about 2 percent in industrial regions, and around 0.5 percent in most non-industrial regions (on gross energy basis). For pig and poultry systems, feed conversion efficiencies were roughly a factor of ten higher. The differences suggest that there is a substantial scope for mitigating the long-term production demand for crops and other phytomass by increases in efficiency and changes in dietary preferences.
Full-text available
For agriculture, there are three major options for mitigating greenhouse gas (GHG) emissions: 1) productivity improvements, particularly in the livestock sector; 2) dedicated technical mitigation measures; and 3) human dietary changes. The aim of the paper is to estimate long-term agricultural GHG emissions, under different mitigation scenarios, and to relate them to the emissions space compatible with the 2 °C temperature target. Our estimates include emissions up to 2070 from agricultural soils, manure management, enteric fermentation and paddy rice fields, and are based on IPCC Tier 2 methodology. We find that baseline agricultural CO2-equivalent emissions (using Global Warming Potentials with a 100 year time horizon) will be approximately 13 Gton CO2eq/year in 2070, compared to 7.1 Gton CO2eq/year 2000. However, if faster growth in livestock productivity is combined with dedicated technical mitigation measures, emissions may be kept to 7.7 Gton CO2eq/year in 2070. If structural changes in human diets are included, emissions may be reduced further, to 3–5 Gton CO2eq/year in 2070. The total annual emissions for meeting the 2 °C target with a chance above 50 % is in the order of 13 Gton CO2eq/year or less in 2070, for all sectors combined. We conclude that reduced ruminant meat and dairy consumption will be indispensable for reaching the 2 °C target with a high probability, unless unprecedented advances in technology take place.
Full-text available
THE MODEL.................................................................................................................................7 I. Basic Methodology on Food............................................................................................. 7
Full-text available
Western diets are characterised by a high intake of meat, dairy products and eggs, causing an intake of saturated fat and red meat in quantities that exceed dietary recommendations. The associated livestock production requires large areas of land and lead to high nitrogen and greenhouse gas emission levels. Although several studies have examined the potential impact of dietary changes on greenhouse gas emissions and land use, those on health, the agricultural system and other environmental aspects (such as nitrogen emissions) have only been studied to a limited extent. By using biophysical models and methods, we examined the large-scale consequences in the European Union of replacing 25–50% of animal-derived foods with plant-based foods on a dietary energy basis, assuming corresponding changes in production. We tested the effects of these alternative diets and found that halving the consumption of meat, dairy products and eggs in the European Union would achieve a 40% reduction in nitrogen emissions, 25–40% reduction in greenhouse gas emissions and 23% per capita less use of cropland for food production. In addition, the dietary changes would also lower health risks. The European Union would become a net exporter of cereals, while the use of soymeal would be reduced by 75%. The nitrogen use efficiency (NUE) of the food system would increase from the current 18% to between 41% and 47%, depending on choices made regarding land use. As agriculture is the major source of nitrogen pollution, this is expected to result in a significant improvement in both air and water quality in the EU. The resulting 40% reduction in the intake of saturated fat would lead to a reduction in cardiovascular mortality. These diet-led changes in food production patterns would have a large economic impact on livestock farmers and associated supply-chain actors, such as the feed industry and meat-processing sector.
Full-text available
Significance The livestock sector contributes significantly to global warming through greenhouse gas (GHG) emissions. At the same time, livestock is an invaluable source of nutrition and livelihood for millions of poor people. Therefore, climate mitigation policies involving livestock must be designed with extreme care. Here we demonstrate the large mitigation potential inherent in the heterogeneity of livestock production systems. We find that even within existing systems, autonomous transitions from extensive to more productive systems would decrease GHG emissions and improve food availability. Most effective climate policies involving livestock would be those targeting emissions from land-use change. To minimize the economic and social cost, policies should target emissions at their source—on the supply side—rather than on the demand side.