1.74 | Fall 2020 | Graduate

Land, Water, Food, and Climate

SECTION 2 | Food and Natural Resources: Demand and Supply, Current and Projected

Overview

How much food will we need in the future? What are current & projected agricultural demands on natural resources? How do conditions change at different scales and for different income groups?

This section of the course begins in Class 2 with a review of population, diet, and crop loss trends, which together determine how demand for food will change. We consider the global situation first but also examine regional differences and changes over time. The readings and supplementary materials (S1–S4) indicate that global food demand will grow considerably in the next few decades. However, they also project that demand will stabilize in most regions by the end of the 21st century.  This has important implications, suggesting that the present century is a turning point as well as a challenge for global agriculture. 

The first part of S5 provides some simple estimates of the increases in food energy (calorie) and protein demands that are implied by projected increases in population and per capita consumption.  These estimates suggest that global calorie demand in 2050 may be as high as 1.5 times current levels, more in some regions. How will this affect the demand for inputs to the agricultural system?  A simplified list of essential inputs includes

  • Natural resources: Land, water, solar energy for photosynthesis, and nutrients
  • Human inputs: Labor, capital, additional energy required to produce and distribute food, and cultivar seeds bred to have desirable properties.

The human inputs required for a 50% increase in production are probably not seriously limiting, especially on regional or global scales. But the natural resources required, especially land and water, may be limiting and are, in fact, already limiting in certain regions.

The second part of S5 considers the additional natural resources needed to meet projected increases in food demand. These resource demands depend on technological efficiencies that determine how much land (yield), water (water use efficiency), and nutrients (nutrient use efficiency) are required to produce one unit of a given crop. The concept of sustainable intensification discussed by Godfray et al (2010) in Class 1 is largely focused on improvements of these efficiencies.

In Classes 3 and 4 we review the water, land, and nutrient resources available for producing food. The readings and Supporting Information (S8) indicate that the areas where soil, terrain, and climate are suitable for successful crop production are scattered and often not located where food demand is greatest. Most of the best land and readily accessible water is already used for some form of agriculture. These findings suggest that there may not be enough unused land and water to meet projected food demand with current technology and management practices. Consequently, the analysis in S5 concludes that there will likely need to be improvements in both technology and management to meet the food security challenges of this century. This topic is considered further in Section 3.

Section 2 Class Topics

Class 2: Demand for Food: Population, Diet, and Food Loss 
Class 3: Resources for Food Production: Water 
Class 4: Resources for Food Production: Land and Nutrients

Section 2 Supporting information (SI)

S1. UN Global and Regional Population Projections 
S2. FAO Food Loss Charts 
S3. FAOSTAT Food Balance Sheets 
S4. Background Data on Food Security 
S5. Food and Natural Resource Demands 
S6. The Water Cycle 
S7. Recent Cropland Expansion from Deforestation 
S8. Global Variability in Climate and Crop Suitability

Courtesy UN Population Division. License: CC BY.

This class discusses five readings that explore the primary factors affecting demand for food—population, diet, and food loss. One way to view the demand side of food security is to distinguish increases in population from increases in per capita demand. Cleland (2013) provides a brief review of demography and discusses likely global population growth over the next few decades. This review complements the United Nations population data provided in S1. A major message is that population in many regions will level off, and perhaps even decline during the 21st century. However, large increases can be expected in a few high growth regions such as sub-Saharan Africa. These geographical imbalances will affect food security, trade, and opportunities for economic development. Mismatches between regional demand and production are reflected in S4, which shows that global production of cereal and meat have grown faster than global population while production in East Africa has barely kept up with its rapidly growing population. This situation is especially critical since many residents of East Africa are unable to afford food imports to supplement inadequate local production.

Per capita demand is generally more difficult to predict than population because it depends on many influences, including nutritional requirements, income, and cultural factors. The role of nutrition is discussed by Gomez et al. (2013), who expands the traditional definition of malnutrition to include overnutrition and micronutrient deficiencies as well as undernutrition. Some of the data in S4 show the close connection between income and malnutrition.

Smil (2002) discusses the inefficiencies that arise when plant energy and protein are converted by animals to meat for human consumption. Increases in meat consumption translate into much larger increases in the consumption of vegetal crops for feed and, therefore, into large increases in the demand for natural resources such as land, water, and nutrients. Most dietary predictions suggest that global per capita demand for food will grow over the next several decades. This reflects dietary trends in developing countries, where meat and total calorie consumption tend to increase as incomes rise.

Courtesy Elsevier, Inc., http://www.sciencedirect.com. Used with permission.

Alexander et al. (2017) cover the topic of food loss, which can be viewed either as an increase in demand or a reduction in supply. They estimate global food losses and inefficiencies during production and distribution as well as the impact of food waste at the consumption end of the food chain (further detail from the optional readings is provided in S2). The portion of food lost to pests is considered in the first three sections of Yudelman et al. (1998). Calculations included in S5 show how growing population could combine with dietary change and food loss to significantly increase both global and regional food demand by 2050.

It is reasonable to ask why per capita food demand needs to increase on a global scale, although increases in some regions, such as sub–Saharan Africa, will be essential to ensure proper nutrition. There certainly are ways to reduce per capita demand. Food production losses could probably be decreased through more investment in storage and pest control. Food waste could be reduced through changes in consumer behavior, most likely prompted by price increases. It is also possible that alternative plant-based sources of protein could decrease net crop demand by reducing consumption of less efficient animal-based food products. However, the readings suggest that it is not likely that per capita demand will be substantially decreased through voluntary measures, especially in light of current dietary trends in the developing world, where meat consumption is increasing rather than decreasing.

The significant increase in food demand expected over the next 50 to 100 years motivates us to estimate the additional resources that will be needed to meet this demand. Some simple calculations in S5 illustrate how increasing food demand could affect crop production and associated consumption of critical natural resources. Classes 3 and 4 consider whether our land, water, and nutrient reserves are sufficient to meet this demand.

Required Readings

Population and Demography

Meat Demand on Crops

Global Nutrition Survey

Food Losses and Inefficiencies during and after Harvest

Crop Losses to Pests

Optional Readings

Prospects for Global Agriculture

Nutrition and Food Security

  • Mark Gibson. 2012. The Feeding of Nations: Redefining Food Security for the 21st Century. Taylor and Francis. ISBN: 9781439839515.

Food Losses during and after Harvest

Pest Losses

Discussion Points

  • Comparing Ehrlich and Ehrlich (Class 1) to Cleland (Class 2) and to UN projections from S1, do you think population growth is going to be a critical obstacle to meeting human needs in the future? Or is the demographic transition gradually reducing fertility rates to replacement levels?
  • Is it realistic to expect significant decreases in food demand through i) population control, ii) dietary change (reduced meat consumption in both developed and undeveloped countries), iii) reduced production losses through investment in infrastructure and better pest control, iv) reduced food waste through consumer education? How would you implement the required changes in policy and behavior?

This class begins by considering the demands placed on global and regional water supplies. The key concept behind the analysis is the hydrologic cycle, which is briefly discussed in S6. Postel et al. (1996) estimate average annual global fresh water fluxes for the portions of the hydrologic cycle most relevant to human uses of water (see attached figure below). They show that agriculture uses much more water than the municipal and industrial sectors, both globally and at river basin scales. However, the agricultural water is a small fraction of the total water passing through the landscape, with most precipitation over land going to evapotranspiration from natural vegetation and to runoff to the ocean.

Based on Postel (1996), Rost (2008), and FAO (2014).

Rosa et al. (2020) consider the impacts of spatial and temporal variations in the demand and availability of water that are not included in the global average analysis of Postel et al. (1996). These variations lead to local water scarcity, which occurs when the demand for water at a given time and place exceeds the supply. Rosa et al. (2020) identify different types of water scarcity, relying on the concepts of blue and green water discussed in S6. Green water is local rainfall that is stored as soil moisture and can be taken up by terrestrial plants. Much of it is subsequently lost to evapotranspiration. Blue water originates from rainfall that flows through rivers, lakes, and aquifers, where it can be withdrawn for human uses. When the available green water is not sufficient to meet the needs of a particular crop blue water can be used to make up the soil moisture deficit through irrigation. Irrigation effectively transfers water from runoff to crop evapotranspiration (i.e. from blue to green water), modifying the natural water cycle.

Rosa et al. (2020) use model-generated water scarcity indices to show where and when various types of scarcity occur. Their analysis indicates that irrigation withdrawals are unsustainable (i.e. are depleting freshwater reserves) on a global scale. However, some agricultural areas that are chronically water short have the potential for locally sustainable increases in irrigation but do not have the economic resources to develop the required infrastructure. This finding suggests that better management and planning of irrigation development could provide sustainable improvements in water and food security.

The uneven distribution of water resources has put significant stress on limited surface and groundwater resources in areas that are currently heavily populated, such as China and India. Water stresses are likely to increase in areas where population is growing rapidly, particularly in Africa and parts of central Asia. Hertig and Gleeson (2012) focus on the use of groundwater resources, which are particularly important sources for irrigation water in arid and semi-arid regions. They cite a number of references that document the widespread occurrence of groundwater depletion in important agricultural regions, especially in North America and Asia. Overall, the readings in this class indicate that spatial and temporal variability greatly complicate our ability to determine whether there is enough readily available water to support a significant expansion in agricultural production.

Required Readings

Human Appropriation of Water

Distribution of Water Resources

Groundwater Depletion

Optional Readings

Water Scarcity

Groundwater Depletion

Discussion Points

  • How would you answer the question: “Are we running out of water” (for food production)?
  • What more would you need to know to determine if it is feasible to reduce global water consumption to sustainable levels while satisfying the food demands of growing populations in water scarce regions?

This class considers land and nutrient resources available for agriculture, paralleling the inventory approach to water resource assessments taken in Class 3. Although remote observations taken from space have revolutionized our knowledge of land use patterns and trends, it is still difficult to distinguish specific crops, estimate crop yields, or evaluate soil fertility using remote sensing alone. Ground-based observations are needed to validate remote sensing data, which are more extensive but less accurate. Some preliminary assessments of agricultural land use are presented in the class readings and in S8.

Lambin and Meyfroidt (2011) rely on land use estimates obtained from a variety of sources to construct global land budgets for 2000 and 2030. They indicate that current crop land and pasture together use about 40% of global ice-free land. Much of the remaining land is unsuitable for agriculture or in tropical forest regions where extensive agricultural development could be unsustainable and contribute to climate change. The Lambin and Meyfroidt analysis suggests that the additional land suitable for rural and urban needs is about 5% of currently used land. The most favorable options for land expansion are unprotected sparsely populated savannahs in South America and Africa. In some areas it may be possible to convert currently unsuitable land to agricultural uses if there is extensive investment in infrastructure and soil improvement, but these measures may not be economically feasible at current food prices. Some context is provided in S7, which briefly discusses recent cropland expansion from deforestation.

Zabel et al. (2014) provide an alternative model-based approach for identifying land suitable for agriculture. They consider soil, terrain, and climate in an analysis that maps areas that can support particular food, feed, and energy crops. Their analysis indicates that only about 10% of the global land suitable for some form of agriculture is still available for cultivation. Both Lambin and Meyfroidt (2011) and Zabel et al. (2014) suggest that we have nearly used up the global supply of agriculturally suitable land, although there may be room for some cropland expansion in particular regions. It may be more efficient and environmentally acceptable to increase production by redistributing crops to make better use of currently available land and water resources (see Class 10).

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Tilman et al. (2000) discuss the role of biofuels, which may compete with food crops for land and other natural resources. They argue that biofuel development can be based on feedstocks that avoid such competition while still providing cost–effective energy. Their discussion complements the mass balance analysis of Alexander et al. (2017) presented in Class 2.

Fixen (2009) estimates global supplies of three key soil nutrients (nitrogen, phosphorus, and potassium) required for crop production. The supplies of all three appear to be sufficient until at least 2050 but these nutrients are sometimes not readily available to farmers, especially poor farmers, in the areas where they are most needed. Also, significant increases in the quantities of nitrogen and phosphorus fertilizer applied to crops are likely to have adverse environmental effects, especially on downstream ecosystems and water bodies (see Class 6).

Required Readings

Land Use

Identification of Land Suitable for Crops

Biofuels

Nutrient Supplies

  • Paul E. Fixen and Adrian M. Johnston. 2009. “World Fertilizer Nutrient Reserves: A View to the Future.” Better Crops with Plant Food. 93, no. 3: 8–11.

Optional Reading

Cropland Expansion

Land Use Alternatives

Biofuels

Crop Suitability

  • IIASA, FAO. 2012. “Global Agro-Ecological Zones—Model Documentation (GAEZ v. 3.0).” International Institute of Applied Systems Analysis & Food and Agricultural Organization, Laxenburg, Austria & Rome, Italy.

Discussion Points

  • How could you determine the potential for converting pasture land currently used for grazing or feed to cropland? For context, recall that the existing pasture land area is significantly larger than cropland area. Why do you think the conversion of pasture land to cropland is not discussed in our readings as an option for increasing crop production?
  • Do you believe that biofuels can be grown on land that is not suitable for food crops? What technological advances do you think need to be made for this to be possible (consult the optional reading by Searchinger and Heimlich)?
  • How can we trade off the disadvantages of deforestation with the advantages of being able to use deforested land to grow more food?

These UN population plots are based on historically reported population values before 2020 and on projections based on predicted trends in fertility (measured as total number of children per woman) starting in 2020 and ending in 2100 (UN Population Division, 2019). The total population (all genders and ages) in the designated region is presented as a median value computed from a Monte Carlo technique. The plots also include high and low fertility bounds (+- 0.5 child per woman). 

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Figure S1.1 UN Estimated Population Trends (UN Population Division. 2019)    
(Click each individual figure to see its bigger version.)

Courtesy UN Population Division. License: CC BY.

The global estimates in Figure S1.1 show that the population median peaks around 2100 and the low fertility variant peaks around 2055, after which the population decreases. By contrast, even the low fertility variant in the sub-Saharan Africa plot is still increasing at the end of the century. The sub-Saharan Africa median population increases by a factor of nearly 3.5 between 2020 and 2100, growing from 1.1 to 3.8 billion.

The remaining plots show much slower population trends for the US, China, and high-income countries (primarily Europe and North America). Note that the median value for China peaks around 2030 while the high-income median value peaks around 2045. These results indicate that population will be growing primarily in countries that are currently lower income.

Figure S1.2 shows an estimated global population density map for 2000 (Salvatore et al, 2005). The spatial resolution is 30 arc seconds (1 km2 at the equator). Note the high population density in the generally water limited areas of the Middle East, East Africa, South Asia, and northern China (see the comparison to climate maps shown in S8).

Figure S1.2 Population Density (Salvatore et al., 2005).

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Figure S1.3 Fertility rates over three time periods (UN Population Division. 2019).    
(Click the figure to see a bigger popup image.)

Courtesy UN Population Division. License: CC BY.

The maps in Figure S1.3 above show UN fertility rate estimates (in number of children per woman) over two periods in the past and for 2050–2055 (UN Population Division, 2019). The demographic transition that reduced population growth in much of Asia and South America between 1970 and 2015 is apparent. Although African fertility rates also dropped over this period they were still much higher than elsewhere. In some African countries the 2010–2015 average fertility rate was well above 5 children per woman. However, the UN predicts that most countries in Africa will have lower fertility rates by 2050–2055. This suggests that the pressure on African food supplies may gradually become less intense in the second half of the twenty-first century, although the population will still be growing.

Figure S1.4 indicates that there are reasonably strong relationships between fertility rate and per capita income and between fertility rate and childhood mortality in different countries (Gapminder, 2019). Here fertility is measured by the number of children per woman and childhood mortality is measured as mortality for ages 0–5 per thousand born. Each country in the chart is indicated with a colored circle, with an area proportional to the national population. Countries with higher income tend to have lower fertility rates while those with higher childhood mortality tend to have higher fertility rates. Population predictions often rely on trends in explanatory variables such as income and childhood mortality to forecast fertility rates.

Figure S1.4 Effects on fertility (number of children per woman) of a) per capita income and    
b) childhood (0–5 year mortality per thousand born), in 2012 (Gapminder, 2019)

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References:

UN Population Division. 2019. Department of Economic and Social Affairs, Population Dynamics. World Population Prospects 2019. 

Mirella Salvatore, Francesca Pozzi, et al. 2005. Mapping Global Urban and Rural Population Distributions. Environment and Natural Resources Working Paper 24, FAO, Rome, 2005. 

Gapminder Tools site. 2019.

The following charts, based on data from Gustavsson et al. (2011), compare food losses for cereals, fruits and vegetables, and meat in different geographical regions. The charts are based on “edible food mass”, which is equivalent to the “wet weight” value used in Alexander et al (2017).

Figure S2.1 FAO estimated food loss and waste by crop type and region (Gustavsson et al., 2011)

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These estimates indicate that consumption losses are the largest fraction of the total in the developed countries of Europe and North America while pre-consumer production losses are largest in the developing countries of sub-Saharan Africa and South Asia. This probably reflects greater consumer waste in developed countries and poorer storage and transportation in developing countries. In both cases, total losses for cereals are generally over 20%. Vegetables and fruit losses can be much higher than 30%.

References:

Peter Alexander, Calum Brown, et al. 2017. “Losses, Inefficiencies and Waste in the Global Food System.” Agricultural Systems, 153, no. 2017: 190–200.

Jenny Gustavsson, Christel Cederberg, et al. 2011. “Global Food Losses and Food Waste: Extent, Causes and Prevention.” Report commissioned by Food and Agriculture Organization (FAO) of the United Nations.

FAO’s Food Balance Sheets provide helpful information on the supply and utilization of different food products as well as information on per capita energy, protein, and fat consumption. Data from these balance sheets have been used in some of the calculations provided elsewhere in Supporting Information.

The food balance sheets are available at the FAOSTAT website under “Food Balance”. There are two versions: “Food Balances (old methodology and population)” for the years 2013 and earlier and “New Food Balances” for years after 2013. The data can be reported as global, regional or national totals by year. They can also be downloaded to a spreadsheet. An example is the excerpt from the 2013 global summary given below. This summary was obtained with the “Report” option in the “Food Balances (old methodology and population)” version. The “Report” option is, unfortunately, not available for years after 2013 but similar tables for these years can be constructed outside of FAOSTAT from downloaded raw data.

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The entries for each crop and group of crops listed in the Food Balance Sheets are organized as follows:

Domestic supply (1000 metric tonnes):           

  • Domestic production (harvested)
  • Imports
  • Additions or withdrawals from stock
  • Exports
  • Net supply

Domestic utilization (1000 tons):

  • Human food
  • Processing
  • Animal feed
  • Seed
  • Post-harvest losses
  • Other uses

The columns on the far right give the nutritional contributions of each crop in terms of per capita mass (Kg person-1 year-1), energy (kcal person-1 day-1), protein and fat (both in g person-1 day-1).

As an example, the “Grand Total” row near the top of the above chart indicates that the global average per capita energy consumed in 2013 was 2884 KCal person-1 day-1 and the global average per capita protein consumed was 81.23 g person-1 day-1. The “Cereals” row just below indicates that 1.029x109 tonnes of global cereal production in 2013 was consumed as human food while 0.873x109 tonnes was consumed as animal feed. Cereals provided, on average, 1292 KCal person-1 day-1 of energy and 31.8 g person-1 day-1 of protein. 

The Food Balance entries should be interpreted as rough estimates. The data are obtained from national sources that vary greatly in quality. Also, be careful of label formatting errors in the headings on the “Food Balances (old methodology and population)” reports for years through 2013 (these have been corrected in the chart provided above).

The FAOSTAT website provides access to many other data bases relevant to agriculture, including information on production, trade, investment, prices, inputs, emissions, and land use.

Figure S4.1 shows how global food production, agricultural inputs, and total population have changed over the period 1960–2010, with all variables expressed as multiples of their 1960 values. The figure was constructed for this class and is based on UN Population Division Data and FAOSTAT data (see S1 and S3). Global population is projected to 2050 in the left plot to provide context. This plot shows that global cereal and meat production grew significantly faster than global population, water diversions, and total cropland. The implication is that per capita global food energy and protein derived from cereals and meat have increased, as well as water use efficiency and aggregate cereal yield. 

The plot on the right (note the expanded scale) superimposes trends in nitrogen fertilizer and pesticide use, which have grown much faster than cereal production. This result suggests that global nitrogen and pesticide use efficiencies have decreased substantially since 1960. Overall, these results reflect both the success and environmental impact of the twentieth century ‘Green Revolution’, which has made it possible to feed a growing global population through higher yields driven partly by increased inputs and partly by the development of better crop cultivars.

Figure S4.1 Trends in global food production, agricultural inputs, and population

Another picture emerges in Figure S4.2, which compares meat, cereal, and population averages for the globe and for East Africa (note the change in the vertical scale on the right due to much higher population growth in East Africa). East African cereal production has barely kept up with population growth and meat production has lagged behind. Also, it appears that domestic production in East Africa will need to increase much faster in the future if it is to meet the demand of the rapidly increasing population.
 

Figure S4.2 Comparison of global and East African trends

Figure S4.3 from FAOSTAT shows the marked differences between conditions in developed and developing countries. These are illustrated in comparisons per capita calorie and protein consumption (total consumption before food losses and waste) from 1960 to 2010. The plots clearly reveal how much higher the USA and EU consumption is than the global average.
 

Figure S4.3 Food consumption trends (energy and protein) in different regions (FAOSTAT, 2019)

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Although the global average has been above the UN recommended minimum for some time, the average energy and protein values for the UN’s “least developed country” group have only risen above this threshold since 2000. Figure S4.4 shows that a significant fraction of residents in these countries is still undernourished (FAO, IFAD and WFP, 2014).

Figure S4.4 Malnutrition trends and distribution (FAO, IFAD and WFP, 2014)

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The chart in the upper left of Figure S4.4 shows an approximate distribution of global energy consumption (in calories), with undernourished, overweight, and obesity percentages indicated. The trend plot in the upper right shows a gradual decline in the absolute numbers and percentage of undernourished people but the number given for 2011 is still above 800 million. The lower chart maps the percentages of undernourished in national populations. It gives estimates of around 30% in much of sub-Saharan Africa. Many of the undernourished in this region are children who suffer long term effects from shortages in critical nutrients.

Figure S4.5a (Gapminder, 2019) shows the strong correlation between calorie intake and per capita income, by country (shown with colored circles). The population of each country is indicated by the area of its circle. Figure S4.5b shows a similar plot of calorie intake vs. the water availability, which is chosen as an example of a natural resource that might limit food production. Note that low income countries with plentiful water can still have low calorie intake while high income countries with scarce water can still have high calorie intake. In short, income seems to be a stronger determinant of access to food than availability of natural resources, because richer countries can afford to import food.

Figure S4.5 Per capita calorie intake for various countries vs. a) income and b) water availability (Gapminder, 2019).

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References:

FAO, IFAD and WFP. 2014. The State of Food Insecurity in the World 2014. Strengthening the Enabling Environment for Food Security and Nutrition (PDF - 3MB). Rome, FAO.

Gapminder Tools site. 2019.

Food Energy and Protein Demands

The following calculations provide estimates of the 2050 food energy and protein demands for the globe and for East Africa, based on data from FAOSTAT and the assumptions listed below.

Global food demand is of interest because a necessary (but not sufficient) condition for universal food security is that the global supply of food energy and protein must meet the global demand. If this condition is not met some portion of the global population will not have adequate nutrition. Of course, some groups may not have adequate nutrition due to an uneven distribution of supply, even if the global supply exceeds the global demand. That has been the case in the recent past, as illustrated by plots in S4.

East African food demand is of particular interest because this region has an unusually high rate of population growth as well as widespread undernutrition. Taken together, these two factors require an especially large increase over current values in the food energy and protein needed to provide adequate nutrition to the entire population. In regional analyses such as the one done here for East Africa the estimated 2050 energy and protein demands could be supplied by a mix of locally grown food and imports.

The calculations given below for the globe and East Africa are expressed in terms of the ratio of demand in 2050 to the demand in 2010, which serves as a baseline date. The following assumptions are made:

  • The population growth ratios are based on the median UN estimates for 2010 and 2050:

Global = 9.6 109/ 6.9 109 = 1.4                       (persons)
East Africa = 0.9 109/0.35 109 = 2.6               (persons)

  • Energy and protein growth ratios are based on increasing 2050 per capita consumption levels to those reported in 2010 for a typical southern European diet:

                   Global energy= 3300/2900 =  1.15                      (Kcal person-1 day-1)
                   East Africa energy = 3300/2100 = 1.6                 (Kcal person-1 day-1)
                   Global protein= 105/80  = 1.3                             (g person-1 day-1)
                   East Africa protein = 105/60 = 1.8                      (g person-1 day-1)

  • The global food loss ratio is assumed to be 0.9, reflecting a significant decrease in food waste, primarily at the consumer end. The East Africa food loss ratio is also assumed to be 0.9, primarily reflecting a decrease in food loss at the producer end. These food loss figures are just illustrative and are difficult to predict.

The expressions for the increases in energy and protein are:

Global demand:
  2050 Energy =  (2010 Energy) (Pop Δ) (Calorie Δ) (Loss Δ)
     1.5                          1               1.4          1.15          0.9
  2050 Protein  =  (2010 Protein) (Pop Δ ) (Protein Δ) (Loss Δ)
     1.7                          1               1.4          1.3            0.9

East Africa demand:
  2050 Energy  =  (2010 Energy) (Pop Δ) (Calorie Δ) (Loss Δ)
     3.7                          1                2.6          1.6            0.9
  2050 Protein  =  (2010 Protein) (Pop Δ ) (Protein Δ) (Loss Δ)
     4.2                          1                 2.6          1.8           0.9

These simple calculations reveal the relative roles played by population growth and diet and also show the difference between global and East African conditions. The increase in global food demand is a significant concern but the very large increase in East African demand presents a major challenge for the region. It is also consistent with the UN’s prediction that most of the global increase in food demand will occur in developing countries in Africa and parts of Asia, where population growth will be high and diets need to be improved to provide adequate nutrition. This conclusion can be investigated further with analyses similar to the one presented above by comparing average growth rates for two groups: all developed countries vs. all developing countries.

Natural Resources Required to Meet Food Demands with a Given Technology

The resource demands implied by the above food demand estimates depend on 1) the mix of crops included in the diet, 2) their nutritional (e.g. energy and protein) content and 3) a number of technological factors that determine how much land, water, and nutrients are needed to meet specified energy and protein demands. The following expressions give the land area, water volume, and mass (dry weight) of a particular nutrient (e.g. reactive nitrogen) needed to grow a given mass (dry weight) of a particular grain crop:

\begin{eqnarray*} Land(m^2) = \frac{Production(kgG)}{Yield(kgG \;m^{-2})} \end{eqnarray*} \begin{eqnarray*} Water(mW^3) = \frac{Production(kgG)}{HI(kgG \;kgB^{-1})WUE(kgB \;mW^{-3})} \end{eqnarray*} \begin{eqnarray*} Nutrient(kgN) = \frac{Production(kgG)}{HI(kgG \;kgB^{-1})NUE(kgB \;kgN^{-1})} \end{eqnarray*}

where:
\(Yield =\) crop yield \(= kg\) grain dry weight harvested per \(m^2\) of cropland
\(HI =\) crop harvest index \(= kg\) grain dry weight harvested per \(kg\) plant biomass dry weight
\(WUE =\) water use efficiency \(= kg\) total plant biomass dry weight produced per \(m^3\)water applied to the field
\(NUE =\) nutrient use efficiency \(= kg\) total plant biomass dry weight produced per \(kg\) nutrient dry weight applied to the field

These are macroscopic relationships that do not account, for example, for temporal differences in plant water or nutrient requirements over the season. However, they do show that resource requirements depend not only on the production required but also on various input efficiencies that depend on management practices and technology, such as the method and schedule used to apply water or nutrients to the field. In most applications of these efficiency relationships the production value is the mass of crop harvested after losses to pests. If pest losses can be reduced the production increases and the yield and other efficiency factors increase proportionately. Care should be taken to confirm units and calculation assumptions when using the efficiencies introduced above, since definitions vary.

To explore the consequences of these relationships, suppose that :

  1. The yields, harvest indices, and water and nutrient efficiencies in the above resource requirement expressions remain fixed (indicating current management practices and technology apply)
  2. Production of each crop in the average diet increases equally to meet the 1.5X increase in global calorie demand estimated earlier.

Then we will require an increase of 1.5X in the land, water, and nutrients needed for agriculture to meet caloric requirements. A similar calculation applies for protein. The readings from Classes 3 and 4 suggest that such large increases in land and water inputs are not likely. This implies that we will need to raise crop yields and efficiencies to meet demand. We consider this challenge further in Sections 3 and 4.

Figure S6.1 shows how water in its various physical forms moves on, above, and below the earth’s surface in a cycle that drives, among other things, crop production (Trenberth et al., 2007):

Figure S6.1 The global water cycle. Storages in 103 km3, fluxes in 103 km3 yr-1 (based on Trenberth et al., 2007).

Hydrologists tend to quantify the water cycle in terms of liquid or vapor water fluxes between various compartments that are defined according to application. The above global water cycle diagram includes the following compartments (with water storage noted in 103 km3):
            Ocean                                      1.34 106
            Ice                                           2.63 103
            Groundwater                           1.53 103
            Rivers and lakes                      1.78 102
            Soil moisture                           1.22 102
            Permafrost                               2.20 101
            Atmosphere                             1.27 101

The primary fluxes between compartments are (with flux rate noted in 103 km3yr-1).
            Precipitation
                        Land                            113
                        Ocean                          373
            Evapotranspiration
                        Land                              73
                        Ocean                          413
            Runoff
                        Surface + subsurface    40
            Lateral atmospheric
                        Ocean to land (net)       40

These flux and storage estimates, which collectively form a water budget,  are generally compatible with the fluxes provided in Fig 1 of Postel et al. (1996). It should be noted that the global water budget gives only a generalized view of the water cycle since local and regional fluxes vary considerably from the average global value.  This is apparent in the global climate maps shown in S8.

Some researchers distinguish two types of water fluxes relevant for the production of agricultural or other goods:

  • Blue water is water that flows into and through streams, rivers, lakes, and aquifers. Most irrigation water is diverted blue water.
  • Green water is the portion of soil moisture that originates from direct precipitation. It is either evapo-transpired or incorporated into plant biomass but does not include runoff, which is blue water. Green water is the primary water source for rainfed crops.

Figure S6.2 shows the part of the water cycle that describes the conversion of precipitation over land into evapotranspiration and runoff (surface plus subsurface). It is based on Figure 1 of Postel et al. (1996) but uses the same flux values as Figure S6.1 above. The figure shows in color the division between blue and green water. The portion of precipitation falling on agricultural land (crops, pasture, and forestry) and subsequently evapotranspired as green water (18 103 km3) is significantly larger than the amount diverted from runoff as irrigation and then evapotranspired (3.5 103 km3).

Typically, blue irrigation water is used to make up part or all of the deficit that occurs when a crop’s water needs are not met by green water alone. Although green water deficits may occur for relatively short periods during the growing season, they may stress the crop sufficiently to reduce yield. Irrigation generally improves yield by providing a more reliable water supply. Rosa et al. (2020) discusses distinctions between blue and green water and defines water scarcity indices for both (Class 3).

Figure S6.2 Water appropriated for human uses, showing how total precipitation on land divides into green and blue fractions.
Based on Postel (1996), Rost (2008), and FAO (2014).

References:

FAOSTAT  http://www.fao.org/faostat/en/#data, accessed 2019.

Sandra L. Postel, Gretchen C. Daily, and Paul R. Ehrlich. 1996. “Human Appropriation of Renewable Fresh Water.” Science. 271, no. 5250: 785–788.

Lorenzo Rosa, Davide Danilo Chiarelli, et al. 2020. “Global Agricultural Economic Water Scarcity.” Science Advances. 6, no. 18: eaaz6031.

S. Rost, Dieter Gerten, et al. 2008. “Agricultural Green and Blue Water Consumption and Its Influence on the Global Water System.” Water Resources Research, 44, no. 9.

Kevin E. Trenberth, Lesley Smith, et al. 2007. “Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data.” Journal of Hydrometeorology, 8, no. 4: 758–769.

Lambin and Meyfroidt (2011) from Class 2 and many others believe that rain forests should not be included in an inventory of available cropland. Nevertheless, rain forest is still being converted to cropland at high rates.  

These figures from Achard et al. (2002) show that the lost forest area in some areas of the Amazon and southeast Asia was up to 50% of total forested land over one decade, 2000–2010. The gross loss of forest cover appears in orange circles while gross loss from other woodland areas appears in yellow circles. The range is 0–100% loss over the decade, indicated by the size of the circles.

Figure S7.1 Lost forest area in the tropics (Achard et al., 2014)

Maps from Achard et al. 2014. “Determination of Tropical Deforestation Rates and Related Carbon Losses
from 1990 to 2010.” Global Change Biology. 20, no. 8: 2540–2554. © The Authors Global Change Biology.
All rights reserved. This content is excluded from our Creative Commons license.
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

The following charts from Hansen et al. (2013) give estimated annual forest loss totals for Brazil and Indonesia from 2000 to 2012. The annual forest loss increment is the slope of the estimated trend line in each chart.  Although Brazil’s losses decreased over the plotted period, they are still substantial. Tropical forest loss rates change substantially over time, depending on government policies. It is likely that Brazil’s rates increased at times after 2012.

Figure S7.2 Deforestation trends in Indonesia and Brazil (Hansen et al., 2013)

Figures from Hansen et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.”
Science. 342, no. 6160: 850–853. © AAAS. All rights reserved. This content is excluded from
our Creative Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Deforestation is not confined to the tropics. This image from Hansen et al. (2013) shows losses in the US and Russia as well.

Figure S7.3 Images showing loss of forest cover in a) Paraguay, b) Indonesia, c) United States, and d) Russia (Hansen et al., 2013)

Figures from Hansen et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.”
Science. 342, no. 6160: 850–853. © AAAS. All rights reserved. This content is excluded from
our Creative Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/.

References:

F. Achard, H. D. Eva, et al. 2002. “Determination of Deforestation Rates of the World’s Humid Tropical Forests.” Science. 297, no. 5583: 999–1002.

M. C. Hansen, P. V. Potapov, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science. 342, no. 6160: 850–853.

Eric F. Lambin, Patrick Meyfroidt. 2011. “Global Land Use Change, Economic Globalization, and the Looming Land Scarcity.” Proceedings of the National Academy of Sciences. 108, no. 9: 3465–3472.

Several of the maps provided here summarize the spatial and seasonal variability of climate variables relevant to crop production. Additional maps show the distribution of actual agricultural land (both crop and grazing land), land estimated to be suitable for some crop production, and the cereal yield attained in major cropland regions. Although the factors that influence agricultural suitability are complex some correlation is apparent in temperate latitudes between regions with favorable climate and intensive higher yield agriculture. The lower intensity of agriculture in tropical regions is due, at least in part, to less favorable soils.

Climate Classification

The Köppen climate classification scheme gives a general picture of the variability of climatic conditions important for agriculture, reflecting primarily temperature and moisture. The subsequent figures give more detailed information on particular climatic variables.

Figure S8.1 Köppen global climate classification, 1997.Source: FAO Climate impact on agriculture program

© FAO. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Precipitation

Total Annual Precipitation

Note the relatively high annual rainfall in temperate mid-latitude areas of Europe and North and South America and in much of the tropics.

Figure S8.2 Multi-year average annual total global precipitation. 
Source: Leemans and Cramer (1991) and FAO Climate impact on agriculture program

© FAO. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Monthly Precipitation Comparison (seasonality)

Note the high seasonal variability in the subtropics (India and China, East and West portions of sub-Saharan Africa), and parts of temperate North and South America.

Figure S8.3 Multi-year average monthly total global precipitation for Dec, March, June, and September. 
Source: Leemans and Cramer (1991) and FAO Climate impact on agriculture program

© FAO. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Growing Degree Days

The Growing Degree Day (GDD) value is the integral over time of daily temperatures above the baseline value. Different crops have different baseline temperatures and minimum GDD requirements. The baseline temperatures used in this map are described in Licker (2010). Growing seasons are generally longer and warmer where GDD is higher.

Figure S8.4 Crop growing degree days for a baseline temperature of 8oC. Source: Licker et al. (2010)

© John Wiley & Sons, Inc. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Soil Moisture Availability

The soil moisture index mapped here is the ratio of estimated actual (water limited) evapotranspiration to the estimated potential (not water limited) evapotranspiration averaged over the year for ca. 2010 land use. Note that more soil moisture is available in the tropics and in temperate areas in North America, Europe, and East Asia. Agricultural areas with lower soil moisture availability include India, and parts of southern Africa, Patagonia, and the southwestern US.

Figure S8.5 Multi-year average soil moisture availability. Source: Licker et al. (2010)

© John Wiley & Sons, Inc. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Distribution of Agricultural Land

This map superimposes estimated predominantly cropland (green) and grazing land (brown) area fractions with cropland/grazing mosaic (yellow), ca 2000. The concentration of cropland in the northern hemisphere is apparent.

Figure S8.6 Global distribution of agricultural land, indicating primarily cropland, primarily grazing land, and a mosaic of both. Source: Adapted from Global Environment Outlook (GEO) and UN Environment Program (UNEP, 2010). Based on data from Ramankutty et al (2008)

Courtesy UN Environment Program.

Crop Suitability

In this map suitability implies that it is possible to grow at least one rainfed or irrigated crop from an extended list of candidate crops. The Zabel et al. approach considers soil properties, terrain, rainfall, and temperature when determining suitability for any given crop.

It is interesting to compare Zabel’s potentially suitable cropland with the cropland portion of the UNEP map. The tropical rainforests of South America, Africa, and parts of tropical Australia and Indonesia had relatively little cropland ca. 2010. Zabel classifies some of this land as low suitability. However, some of it, especially in Indonesia and Australia, is classified as sufficiently suitable to be attractive for agricultural development, at least in the short term. Such assessments should be viewed with some skepticism, considering the simplicity of the Zabel suitability analysis and the uncertainty in soils data from these regions.

Figure S8.7 Global crop suitability map. Source: Zabel et al. (2014) (see Class 4)

© PLOS. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

Crop yield

This figure from the Mueller et al. (2012) reading of Class 5 illustrates global yield gaps for staple grains (maize, wheat, and rice) ca. 2000. The pixels of this gridded map show the ratios (in percent) of the weighted average observed staple grain yields to the corresponding attainable yield values. The attainable yield (or yield potential) for each pixel is obtained by averaging 95 percentile yields in all pixels that have similar climates (annual precipitation and growing degree days) but may have different fertilizer and irrigation inputs. An average-to-attainable ratio of 100% in a given pixel indicates that the yield gap there is zero. Ratios less than 100% indicate non-zero yield gaps.

The map shows that the major “breadbasket” regions in central North America, eastern South America, western Europe, India, and eastern China are closer to attainable values while parts of Africa, Mexico, and Asia are significantly below attainable values, indicating large yield gaps. Similar displays can be generated for other crops (Licker et al, 2010).

Figure S8.8 Global map of percentage of attainable yield obtained for a weighted average of cereal crops. 
Source: Mueller et al. (2012) (see Class 5)

© Springer Nature Limited. All rights reserved. This content is excluded from our Creative Commons license. 
For more information, see https://ocw.mit.edu/help/faq-fair-use/.

References:

R. Leemans and W. Cramer. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid (PDF - 3.69MB). Research Report RR-91-18. November 1991. International Institute of Applied Systems Analysis, Laxenburg, pp. 61.

Rachel Licker, Matt Johnston, et al. 2010. “Mind the Gap: How Do Climate and Agricultural Management Explain the ‘Yield Gap’of Croplands Around the World?” Global Ecology and Biogeography, 19, no. 6: 769–782.

Nathan Mueller, James Gerber, et al. 2012. “Closing Yield Gaps Through Nutrient and Water Management.” Nature, 490, no. 7419: 254–257.

Navin Ramankutty, Amato T. Evan, et al. 2008. “Farming the Planet: 1. Geographic Distribution of Global Agricultural Lands in the Year 2000.” Global Biogeochemical Cycles, 22, no. 1.

Florian Zabel, Birgitta Putzenlechner, and Wolfram Mauser. 2014. “Global Agricultural Land Resources—A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions.” PloS One. 9, no. 9.

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