A crop production ecology (CPE) approach to ... - SAGE Journals

A crop production ecology (CPE) approach to sustainable production of biomass for food, feed and fuel A.J. Haverkort, P.S. Bindraban, J.G. Conijn and F.J. de Ruijter Abstract: With the rapid increase in demand for agricultural products for food, feed and fuel, concerns are growing about sustainability issues. Can agricultural production meet the needs of increasing numbers of people consuming more animal products and using a larger share of crops as fuel for transport, electricity and heat, while still sustaining the natural resource base? In addition to economic models and learning from statistics and trends, there is a perceived need for decision support tools at global, field and plant levels and for the certification of best practices based on crop production ecology (CPE). This paper illustrates the need for and availability of a generic approach to sustainability principles, criteria, indicators and norms to ensure maximum efficiency in the use of resources such as land, water, chemicals and energy in crop biomass production at various levels of scale. The authors propose a method based on a transportable CPE approach, covering ranges of commodities and environments, to address choices in agricultural production: which crop to promote where, how it should be grown to optimize the efficient use of resources, how to certify the best practices and which crop properties need genetic improvement to make the best use of scarce resources in adverse conditions. Keywords: sustainability criteria; land use planning; agroecosystems design; crop design; indicators The authors are with the Wageningen University and Research Centre, Plant Research International, PO Box 16, 6700AA Wageningen, The Netherlands. E-mail: [email protected].

Drivers and trends of agricultural production The long-term global availability of food (Koning et al, 2008) is a matter of increasing concern for economists and agriculturalists, who base their analyses on economic models and statistical trends of production and yield increases. Over the last decades (Table 1), food production has shown a strong increase. Cereal production increased by about 50%, from 1.57 Gt in 1980 to 2.27 Gt in 2004. Meat production almost doubled over that same period, and fruit and vegetable production more than doubled.

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The human population increased from 4.44 billion in 1980 to 6.07 billion in 2000, and is close to 6.5 billion today – an increase of 46% in 27 years. This shows that food production barely grew faster than the population increase. The amount of resources needed for this production increase, however, showed a smaller increase. Table 2 shows there to have been an increase in fertilizer use of about 20%; only 6% more land was taken into production as arable land or pastures; and irrigated land area increased by 30%, showing that the increase in food

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A CPE approach to sustainable biomass production

Table 1. Global agricultural statistics (billion tonnes produced fresh weight per year). Commodity

1979–1981

1989–1991

1999–2001 2003

2004

Cereals Meat Fruit and vegetables

1.57 0.136

1.90 0.180

2.08 0.235

2.09 2.27 0.253 0.261

0.630

0.813

1.208

1.345 1.383

Source: FAOSTAT data.

Table 2. Global changes in land use (billion hectares) and fertilizer use (billion tonnes). Item

1980

1990

2000

2004

Total land area Arable land Permanent crops Pastures Total agricultural land Irrigated land Fertilizer

– 1.35 0.102 3.24 4.69 0.210 0.121

– 1.40 0.120 3.37 4.89 0.244 0.148

13.0 1.40 0.136 3.44 4.98 0.275 0.146

– – – – – 0.147

Source: FAOSTAT data.

production was mainly made possible through higher yields per hectare. Crop protection and water management are the main factors contributing to increased yields and fertilizer use efficiency. The agricultural land availability was 1.05 ha/person in 1980, as against 0.82 ha/person in 2000, a decline of 22%. The present world population growth and a rapidly changing diet (especially in developing countries) towards more meat consumption require a doubling of the world biomass (food + feed) production in the coming decades. The additional use of food-grade sugars, starch and vegetable oil as feed stock for fuel for transport and electricity further increases the pressure on the natural resource base. The first-generation technologies, such as fermentation, combustion and transesterification, presently tend to increase the demand for agricultural products. Second-generation technologies such as enzymatic hydrolysis and biomass gasification followed by conversion into liquid fuel allow the use of (ligno)cellulose from agricultural material (such as straw) rather than sugar or starch. Several millions of hectares are currently dedicated to biofuel production, especially in Brazil and the USA, where sugar cane and corn starch are converted into alcohol for mandatory mixture with petrol. Similar directives of the European Union further increase the demand for biofuels and the subsequent requirement for land and other resources. The prices of certain commodities such as sugar, corn and palm oil are being affected by the demand for bioenergy. The secondgeneration bioenergy technology will still take many years to develop, but will make better use of waste flows. If (ligno)cellulosic material is cropped in agricultural systems, however (which is foreseen), it also will require access to the same resources, land, water, chemicals and energy as are required for food and feed production.

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The production of biomass for food, feed and bioenergy should make the most efficient use of natural and economic resources, while improving the socioeconomic conditions of living and preserving biodiversity. Additionally, in the case of biomass for bioenergy, a positive contribution to the greenhouse gas balance should be achieved. To ensure that these conditions are fulfilled, sustainability criteria are being developed for the production and processing of biomass for food (for example, palm oil), construction materials (for example, the Forest Stewardship Council [FSC] label for wood) and bioenergy (see Cramer, 2007). Developments in sustainable biomass production should be systematically reviewed with regard to the advantages and disadvantages of large-scale bioenergy production as promoted by governments and induced by high energy prices. Their efficiency in terms of resource use should be determined, their contribution to reductions in greenhouse gases should be unambiguously established, the creation of opportunities for socioeconomic improvements in people’s lives ought to be clarified, and their competition with food and feed production eliminated. It is debatable whether large-scale bioenergy production can be sustained in the long run in terms of optimal use of resources – notably land, water and minerals – and whether the carrying capacity of the earth is adequate to produce sufficient food, feed and fuel for the world population without negative effects on the greenhouse gas balance and biodiversity.

Analytical framework The market for biofuels is strongly influenced by policies of national and international authorities. Measures such as biofuel directives and market interventions immediately translate into industrial activities and investments in primary production (farms) and the processing industry. Biofuel production then draws upon resources from the regional environment (land, water, biodiversity) and emits, inter alia, agrochemicals and minerals into the environment. These immediate consequences for resources and the environment, coupled with others such as increased wealth and meat consumption worldwide and increased energy prices, also have socioeconomic repercussions, which become subject to the attention of policy makers and the general public because they do not necessarily follow a planned path. An ex ante analysis of production possibilities at various scale levels could govern a smooth development path with the least possible adverse effects. The linkage between the various components of resource use and decision making is schematically presented in Figure 1. The relatively recent developments seem to call for: • the development of a decision support system for policy makers and industries – which crop to grow for what purpose, in which environment and at what volumes; • the design of biomass production systems that make optimal use of resources; this may require the introduction of a new crop in a particular environment, or improved design of existing crop–environment combinations;

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Markets’ claim on resources requires planning of land use at local to global scale: decision support is needed on the identification of optimal crop–environment options.

On-farm production of biomass needs decision support for the design of agroecosystems with highest resource use efficiency and minimal emission to the environment.

Implications for socioeconomy, environment, certification and labelling: indicator values.

Crops and varieties needed that make optimal use of scarce resources. Decision support needed for crop design for breeders and geneticists.

Figure 1. Decision making at global, field and plant levels and interaction, with certification of sustainable biomass production for food, feed and fuel purposes.

• improved crops that meet new demands for food, feed or fuel, or for a combination of these, and are able to grow under resource-poor conditions (marginal land); and • a certification and labelling system for the production of biomass for feed, food and/or fuel. Agriculture is challenged to feed the world population, to prevent loss of biodiversity, to increase the efficiency of resource use (primarily land, water and nutrients), to maintain the production capacity of soils, to prevent high emissions of nutrients and chemicals, to keep rural areas worth living in and, more recently, also to provide bioenergy and biochemicals. As all these objectives and functions might not be achieved simultaneously, sustainability implies a search for the right balance in realizing these objectives for the current generations while maintaining the ecological, social and economic functions and structures for future generations. Whereas economic and statistical models indicate the magnitude of change needed to meet such objectives in terms of commodity volumes produced and inputs required, they scarcely address ecological sustainability. For that, an approach is required that links crop production to necessary inputs and environmental side effects based on ecological principles. Models based on ecological approaches to production explain and integrate the processes that are involved in resource capture, crop growth and production (Van Ittersum and Rabbinge, 1997) and they are able to serve the various goals shown in Figure 1, that is, land use planning as well as the design of agroecosystems and crops, and will also generate quantitative indicators of sustainability to be used for certification. The objectives of this paper are to explain the crop production ecology (CPE) approach and how this methodology complements tools such as trend analysis that estimate future resource requirements and resulting production levels of food, feed and fuel. It also


complements economic models of changing demands in agricultural commodities. The CPE approach is a quantitative information tool that is used to assist: • policy makers to make balanced decisions regarding investments for suitable commodity–environment combinations; • farmers and farming companies to optimize resource use efficiencies while meeting desired levels of sustainability; • plant breeders and geneticists to explore the impact of specific genetic traits on production and resource use, with an emphasis on suboptimal growing conditions; and • supply chain stakeholders to develop and monitor sustainability indicators for each crop–environment combination. The strong interdependence and functional relations between the Genotype (consisting of crop type and genetic make-up – variety), Environment (choice of the site and season) and Management options (making resources available to the crop) call for an integrated assessment of their impact on the ultimate performance of systems and resource use efficiency. These G × E × M interactions are effectively integrated in the CPE approach.

The CPE approach Many eco-physiological processes affect crop performance, but relatively few (those related to light, water and minerals) have a major impact on growth. The efficiency of radiation use by plants for their growth tends to be quite stable for crop groups such as C3 (including wheat, rice, potato, bean, most vegetable and fruit crops, but also many weed species) and C4 (including the important food crops, maize, millet, sugar cane and sorghum, many pasture grasses and also many weed species). This

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Transpiration CO2 Radiation

O2

Temperature

Rain

Volatilization

Evaporation

Irrigation

Fertilization

Carbon Nitrogen

Uptake

Uptake H2 O

Mineralization

Immobilization

Leaching

Drainage

Capillary rise

Figure 2. The influence of abiotic factors on crop growth.

efficiency might be reduced by inadequate availability of water and nutrients, thus limiting maximum growth (Bindraban et al, 2000). These principles have been used to develop simulation models for estimating crop production as a function of weather conditions, available water, soil fertility and external inputs. Crop growth models (De Wit, 1978) are mathematical algorithms that calculate yields of crops in their environment, for example, sugar beet in a particular location in Germany or sugar cane in Uganda. Such models – LINTUL (Spitters, 1990) among others – consist of formulae resembling the following: Y = RAV × Prec × RUE × HI : DMC where Y = fresh yield of the harvested plant part (tubers, grains, fruits) expressed as grams of fresh produce per m2 of land area RAV = the amount of resources available for crop uptake per m2 of land area, for example, grams of nitrogen, MJ of solar energy or litres of water from rainfall or irrigation Prec = the proportion of resource available to the crop that is actually taken up or recovered RUE = resource use efficiency indicating how many grams of dry crop matter (roots, stems, leaves and harvested product) are produced per unit of resource intercepted or taken up by the crop (expressed as gram per MJ light intercepted (light use efficiency, LUE) or gram dry matter per gram of nitrogen taken up (nitrogen use efficiency, NUE) and gram of dry matter produced per litre of water transpired by the crop (water use efficiency, WUE)

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HI DMC

= the harvest index: that is, the proportion of total dry matter produced that is allocated to the harvested part = the dry matter concentration of the harvested product.

In such models – schematically represented in Figure 2 – temperature, and in some crops also day length, are the driving forces behind the phenological development of the crop. In general, higher temperatures lead to earlier crop emergence and a more rapid initial leaf growth, resulting in higher interception of solar radiation at the early stages of crop growth. The phenological development also determines the timing of flowering and the formation of reproductive organs (fruits, grains or tubers) and the length of the growing cycle from planting or sowing until harvest. Different varieties of a crop species always react in a similar manner to temperature and day length, so the model has to be specifically parameterized for crop varieties. The amount of solar radiation intercepted by the foliage determines the daily growth rate (Monteith and Moss, 1977). About half the amount of total solar radiation is photosynthetically active radiation (PAR, with wavelengths between 400 and 700 nm), while the rest is mainly heat (with wavelengths above 700 nm). A crop may produce on average up to 3.5 grams of dry matter per MJ intercepted PAR. The amount of carbon dioxide also determines the growth rate: higher levels lead to faster growth. In the long term, the rising CO2 levels will affect crop growth, though a simultaneous increase in temperature and possible change in rainfall distribution might undo these gains. Knowing the long-term (or realtime) temperature and solar radiation data in an area allows the calculation of potential yields under field



conditions, that is, yields when growth is not hampered by lack of water, minerals or occurrence of diseases, pests and weeds. Usually, crop varieties are applied that make maximum use of the duration of the growing season, either in single or multiple cropping systems within one year. Potential yield – among other factors – allows the comparison of the potential yielding ability of different agroecological regions (for example, see Geurts and Van den Berg, 1998). Knowing the long-term (or real-time) rainfall and soil/water-holding capacity – as determined by the texture and depth of the rooting zone – the rainfed production of crops can be calculated. Such calculations are extensively used in decision support systems for irrigation scheduling, such as for sugar cane (Singels and Smith, 2006). Crop growth models (Figure 2) also contain routines that calculate the dynamics of nutrient availability for the crop: for example, mineralization and immobilization, volatilization, leaching and uptake of nitrogen. The efficiency of their use depending on supply is then calculated. These models are used in decision support systems to assist farmers in optimizing fertilization (Ten Berge et al, 1997). Soil carbon balances can also be calculated by using the amount of nonharvested biomass (1 – HI) as input in organic matter models. Besides those factors determining positive growth, growth-reducing factors are also taken into account. The amount of damage a crop suffers is calculated from the pressure of diseases and pests. The relationship between, for example, the number of nematodes, aphids or fungi spores and the expected yield level of many crops and their varieties is known (see, for example, Collins et al, 2002). Sometimes this knowledge is based on empirical relationships, sometimes on the mechanism of the crop– disease relationship, such as that between disease pressure and crop productivity (Rabbinge et al, 1993). Usually, monitoring (counting) takes place, followed by reference to expected population dynamics inferred from weather data (especially temperature and humidity). These models are used in decision support systems for the control of pests, diseases and weeds, and allow the calculation of biocide use efficiency.

Decision support at three levels of scale The increasing claim on natural resources and their limited availability demand their judicious use and allocation. Choices about resource allocation have to be made at three levels of scale: global/regional, farm/field and plant (Figure 1). Policy makers and multinational enterprises operate at global, national or regional scales and take the wishes and concerns of consumers and non-governmental organizations into account. These may concern food safety, social and economic aspects, environmental protection and/or reductions in greenhouse gas (GHG) emissions. Farmers make tactical decisions (for example, which variety to plant) to organize the activities of their farms and operational decisions to manage their fields. Farmers can also make strategic decisions that go beyond their farm gates, such as by migrating. The link between multinationals, policy makers and farmers is becoming


stronger as policies and consumers demand multinationals to comply with sustainability criteria that affect the tactical and operational decisions of farmers. Decision support at the plant level is needed for geneticists and plant breeders to design crops and varieties with desirable properties regarding yield, quality, resistance to pests and diseases, tolerance to abiotic factors and optimal resource use efficiencies. Most countries have a food safety authority that regulates food safety issues, but as yet none has a sustainable agriculture authority with a similar mandate to assure a pre-competitive level playing field for sustainable primary production of biomass for processing into food, feed and/or fuel. Haverkort et al (2007, 2008) signalled the need for a transition to good agricultural practice schemes to include both food safety and sustainability guidelines. A putative sustainable agriculture authority would address support for decision making within three major themes: for policy makers to decide on effective regulations, for companies and farms to design and set up efficient production systems, and for breeders to design new crops. The authority would direct an agency charged with the certification of national or imported biomass for food, feed and/or fuel. Agencies would address questions (such as which values of indicators are sustainable) directly to the authority. At each of the decision levels, the production ecological approach – among others – can create valuable information for informed decision making. The examples shown at each decision level in Figures 3–5 were taken from ongoing explorations in the authors’ various research projects.

Support in land use planning Governments often impose policy measures on agricultural production for food, feed or fuel to meet certain goals and regulations such as self-sufficiency, improvement of the environment, export levies or mandatory mixtures of biofuel. Stimulating sustainable agricultural production requires management of the production process at the various levels of scale. Sustainability of energy cropping systems, for instance, should comply with direct effects such as reduced use of fossil fuel, but also with indirect effects such as land use changes on local and global scales, depletion of water reserves, nutrient emissions to the environment, depletion of mineral reserves (notably phosphate), biocide emissions to the environment, altered landscape and biodiversity and effects on the local and national socioeconomic situation. Policy makers should therefore be well informed about the relevant factors that determine the sustainability of biomass production, such as spatial allocation of production systems, the use of water and minerals, and claims on land. The present rapid demand for agricultural biomass and its claims on natural resources call for ex ante explorations of decisions. Questions that are relevant relate, for instance, to the crop–environment combination that makes best use of resources such as land, water, soil, minerals, the displacement of crops that can be expected, the additional competing claims that might result from biomass production for feed, food and fuel on existing claims for

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A CPE approach to sustainable biomass production 2.5

500

2 400

DM yield increase (ton/ha)

Gross biomass energy (EJ y–1)

600

300

200

100

0 Crop-+ grass-+ Crop-+ grass-+ shrubland+ shrubland forest

Crop-+ grassland

Cropland

Actual crop production

Land use option

2.0

80.0 1.5 60.0

____ greenhouse gas reduction

1.0

- - - - gross energy yield …… net energy yield

20.0

0.5

Net GHG reduction (ton CO2 eq/ha)

Energy yield (GJ/ha)

100.0

0.0 100

150

200

250

N application (kg/ha)

Figure 4. Gross and net energy yield and net greenhouse gas reduction at various levels of nitrogen fertilization of an oilseed rape crop.

resources, and the enhancement of the use of land for multiple functions. Quantitative crop production ecology facilitates the assessment of direct and indirect ecological effects and the investigation of possibilities for improvement of current land use. The production potential of global biomass can be calculated, while the availability of suitable land areas can be surveyed. By analysing the gap between actual and potential production levels, input requirements can be quantified along with the emissions of nutrients and chemicals and possible depletion of reserves. Also, the feasibility of taking idle/degraded/marginal land into production can be assessed. Figure 3, as an example, illustrates the trade-off between the production potential of biomass and claims on land. If all suitable land on the entire African continent could be cultivated under rainfed conditions and applied

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0.5

1980 –0.5

1985

1990

Irrigated

1995

2000

Not irrigated

Year Figure 5. Increase of rape seed yield in the Netherlands if the crop has a 30-day longer growth cycle.

2.5

120.0

0.0 50

1

0

Figure 3. Production of biomass for energy (gross) under rainfed conditions with fertilizer inputs and crop protection for different land use options compared to actual production for the African continent.

40.0

1.5

with adequate amounts of fertilizers and biocides so that growth would not be limited, a total amount of more than 500 EJ of gross energy contained in the biomass could be produced. Presently, some 10 EJ biomass equivalents are produced on the cropland, while approximately 50 EJ would be required for food and feed if a European diet were assumed for the current population. Correcting for other uses and losses of food, an estimated amount of less than 450 EJ of biomass would remain for energy. Net energy production would be lower, however, because energy is used in agricultural production, transport, processing and distribution. The production potential declines drastically, from a biodiversity point of view, if conversion of natural lands to forest, savannas and grasslands is not permitted. The entire production potential on the current croplands is then required for food and feed, and therefore leaves little space for the dedicated production of biomass for energy. Energy might be obtained from manure (biogas) or partly from plant residues (for example, for bioelectricity), but only after fulfilling the needs for soil organic matter to sustain soil quality. Total bioenergy production from cropland without additional land conversion is therefore insignificant if domestic production for a European diet has the first priority for the African population.

Support in the design of production systems Once a decision is made on a crop–environment combination or when an existing situation needs to be improved, decision support is needed to design a production system for food, feed or fuel that makes best use of available resources with minimum emissions of chemicals to the environment. State-of-the-art modelbased technologies and decision support systems, integrated management of pests and diseases and precision farming techniques are available (see Kropff et al, 2001) to design such production systems. In each crop–environment combination, the resource



use efficiency may be expressed in various ways: depending on the production of food, feed or fuel, the efficiencies can be expressed in terms of quantity of dry matter, starch, sugar, protein or energy produced per unit of resource (for example, g/MJ, g/m3). Viewed from an energy perspective, it includes: • the energy use efficiency (MJ/MJ), that is, the amount of energy it costs to produce a unit of energy; if it costs 0.5 MJ to yield 1 MJ, the energy use efficiency (EUE) is 2.0; the net energy production of a field in this case is 50% of the gross energy; the energy costs per hectare are related to field production, transport and processing; the energy yield per hectare is in the amount of fuel, electricity and/or usable heat; • the land use efficiency (MJ/ha), that is, the net amount of MJ from biomass after conversion to energy as heat, fuel or electricity per m2 or per hectare; • the water use efficiency (MJ/m3), which is the net amount of energy from biomass after conversion per m3 of water transpired by the crop and evaporated from the soil during the growth period; and • nitrogen use efficiency, phosphate use efficiency and so on (MJ/g) are calculated as the amount of energy produced per g input of minerals. The resource use efficiencies realized by farmers should approach the values derived from the model. If they are substantially lower, they reveal a situation that needs attention. The CPE approach can be applied to assess energy and GHG balances (whole chain/system approach) and to quantify input use and use efficiencies. Moreover, it quantifies soil organic matter dynamics and has feedback loops to compare alternatives and system improvements, allowing ex ante evaluation of sustainable production levels. We calculated, for example, what happens to the nitrogen use efficiency when irrigation takes place. The analyses reveal that trade-off between environmental goals can occur: for example, higher land use efficiencies (yield) are attained at high nitrogen application levels, and maximum CO2 reduction is achieved at lower levels. Figure 4 shows that nitrogen fertilization of oilseed rape should be modest (not exceeding 150 kg/ha) when biofuel is produced from rapeseed and maximum greenhouse gas emission reduction is sought. If maximum replacement of fossil fuel is the aim, farmers will strive for maximum gross energy and about 220 kg N/ha will have to be applied. Optimal net energy yield is obtained with an intermediate nitrogen dressing. We conducted similar exercises regarding soil organic matter conservation in soils with varying concentrations of organic matter and found that, on peat soils, agriculture is hardly possible when a positive CO2 balance is a prerequisite.

Support to design better crops The additional demand for agricultural products for food, feed, but certainly also for fuels, makes higher-yielding crops a necessity, especially those that better tolerate adverse abiotic conditions occurring in areas presently still too marginal for adequate crop production. Recent insights in genetics following genomics, gene sequencing and genetic modification have the ability to boost crop breeding. It has become possible to design crops that


perform better under adverse conditions such as heat, drought, salinity, flooding and cold (see Dixit, 2008). Moreover, the new biotechnological approaches allow the development of crops suited to biorefinement, whereby the most valuable parts or compounds are first separated for use as food, chemicals, fibre or pharmaceuticals, and the remainder used for (second-generation) biofuel. Ideotyping (Boote et al, 2001; Haverkort and Kooman, 1997) through the CPE approach allows the exploration of an altered genetic make-up of a crop in a specific environment. Suppose a crop roots 20 cm deeper than conventionally, then it is calculated how much more water it will acquire and, with the known water use efficiency, the additional yield can be calculated. If crops hitherto not resistant to frost were, through breeding, able to withstand, for example, –3ºC, the CPE approach could calculate the additional length of the growing cycle and the additional amount of dry matter produced. Such additional growing time may lead to a greater depletion level of the available water. Then yields can be enhanced even further if additional irrigation takes place. Figure 5 gives an example of calculations for rapeseed (canola) grown in the Netherlands with weather data from 1985 to 2000. Winter rapeseed is sown in autumn, it flowers in spring and is harvested when the leaves have turned brown and the pods are mature in August. The crop does not benefit from sunshine (or rain) between mid-August and mid-September. Lengthening – in the simulated explorations – the growth cycle by 30 days so as to make better use of the available growing season increased yields by 0 (in 1995, for example, when there was hardly any rainfall in the lengthened period) to almost 2 t/ha in the rainy year 1998. If a precipitation deficit and resulting soil moisture deficit was eliminated by putative irrigation, yield increases would be >1 to >2 t/ha.

Certification of biomass used for food, feed and fuel Because of consumer concerns following the striking increase in current production, many countries feel the need to set norms for certification, to provide labels and to be able to trace and track agricultural commodities. Farms that produce biomass for various purposes (food and/or feed and/or fuel) will have to show that they have complied with certain norms. It is expected that procurers of food and feed and fuel processing industries will increasingly want to (or have to) buy certificates proportionate to the amount of raw materials they buy on the world free trade market. A number of certification schemes are operating or are being initiated, such as: • Palm oil sector – Roundtable on Sustainable Palm Oil: (RSPO): http://www.rspo.org/Review_of_RSPO_ Principles_and_Criteria_for_Sustainable_Palm_ Oil_Production.aspx. • Forestry sector – Forest Stewardship Council (FSC): http://www.fsc.org/en/whats_new/fsc_certificates. • Agricultural certification – Sustainable Agricultural Network – GLOBALGAP:http://www.globalgap.org. • Organic certification – for example, the International Federation of Organic Agriculture Movements

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Table 3. Examples of principles, criteria, indicators and norms for reporting. Principle

Criterion

Maintenance of carbon reservoirs in vegetation and soil

Maintenance of above-ground carbon Loss of carbon compensated with time Maintenance of below-ground carbon Amount of below-ground carbon

Indicator

Norm 10 years No biomass production on peat soils

Maintenance of biodiversity No trespassing against national laws No production in protected areas

Laws on land use, protection of nature… Compliance Designation of High Conservation No production Value areas

Optimal water use and quality

No trespassing against national laws

Laws on irrigation, use of groundwater, sanitation...

Compliance

Contribution to well-being of employees

No negative effect on labour conditions

Declarations of International Labour Organization (ILO)

Compliance

Principle

Criterion

Reporting

No endangerment of local supply of food Maintenance of biodiversity Maintenance of soil quality

Changes in land use Changes in prices of food and land Presence of biodiversity management Application of best agricultural practices

Report of changes in land use Report of changes in prices Good agricultural practices regarding ecological corridors Reports on erosion, nutrient balance, soil organic matter

Source: Cramer (2007).

•

• • •

(IFOAM): http://www.ifoam.org/about_ifoam/ standards/index.html. Fair trade criteria – for example, the Fairtrade Labelling Organizations (FLO): http://www.fairtrade.net/ labelling_initiatives.html. Coffee sector – 4Cs (Common Code for Coffee Community): http://www.sustainable-coffee.net. Round Table on Responsible Soy: http:// www.responsiblesoy.org/eng/index.htm. Round Table on Sustainable Biofuels: http:// www.its.berkeley.edu/sustainabilitycenter/ RSB_Intro.pdf.

These schemes have many aspects in common. A recent scheme that we elaborate slightly here as an example that attempts to certify sustainable biomass for biofuel purposes only, is the approach developed in the Netherlands (Cramer, 2007). As the Netherlands government expected that large new areas would be planted with energy crops, it initiated a framework to test the sustainability of biomass. This framework consists of nine principles or objectives for the production and processing of biomass. These practices: (1) should lead to a positive balance of greenhouse gases; (2) will not reduce carbon reservoirs in vegetation and soil; (3) will not endanger local supply of food and other biomass applications; (4) will maintain or improve biodiversity; (5) will maintain or improve soil quality; (6) will not exhaust and pollute soil and surface water; (7) will maintain or improve air quality; (8) will contribute to local prosperity; and (9) will contribute to the well-being of employees and the local population. Most of these principles are formulated in an abstract (but not measurable) way. For establishing the concrete

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requirements that biomass has to meet, these principles are translated into more specific criteria that can be monitored, with indicators as minimum requirements according to indicator values, standards or norms. Table 3 shows – as an illustration – some principles, criteria, indicators and norms. Where (as yet) no norms exist, the framework requires the reporting of relevant information. The Dutch indicators and their required values are at an initial stage of development. The transportability from one commodity to the other (corn versus sugar cane or palm oil versus oil from rapeseed) is not well established (what should the indicator values be?) Nor are they unambiguously transportable from one region to another (for example, from West Africa to Malaysia).

Contribution of the CPE model to sustainability criteria The CPE model that is used to calculate the design of an optimal production system and the accompanying optimal levels of resource use efficiencies also reveal which determined efficiencies are too low and need decision support (trajectories) to increase efficiencies to their intended levels. The efficiency levels are usable as indicator values (norms) for certification purposes. Figure 6 presents this approach schematically. As an example, suppose the water use efficiency under best agricultural practice is calculated to be 26 MJ per m3. Suppose then that policy makers tolerate some losses and accept an efficiency of, say, 22 MJ per m3 under the specific conditions of a particular environment. Policy makers may decide that an efficiency that is less than 85% of the one under best agricultural practice, which in this example is 18 MJ per m3 of water supplied to the crop, is not sustainable. The approach shows the relevance of the criteria, multiple goals and exchange of goals (for example, water for energy) and as such, allows policy makers to underpin their decisions.


A CPE approach to sustainable biomass production Indicator value

Table 4. Examples of sustainability and unsustainability thresholds of ethanol produced from sugar beet in the Netherlands.

Sustainable

Resource

Grace period

Sustainability threshold Not yet sustainable Unsustainability threshold Unsustainable

1

2

3

4

5

6

7

8

Year

Figure 6. Various sustainability indicator values and movement from an undesirable to a desirable sustainability level in a grace period of, for example, six years.

An energy crop company (farm) producing below the sustainability level will be suspended and not granted a licence to deliver. A company delivering between the two sustainability indicator levels will have to improve within a number (to be decided by the policy makers) of growing seasons. The threshold levels may vary according to area, as in dry areas – with a high evaporative demand – the best technical means will show lower water use efficiency than in moister, cooler, more cloudy areas. Here the example of water use efficiency is given, but similarly, depletion levels of phosphate or contributions to greenhouse gases (CO2, N2O, CH4) are expressed with threshold levels derived from the CPE approach. Similar to the efficiencies to produce energy (MJ), efficiencies can be calculated to produce, for example, starch, sugar or protein or whatever the final food, feed or fuel product that is processed from the biomass. Often trade-offs exist between the various goals and the CPE approach is able to reveal those trade-offs for better decision making. This is not only applicable to the field level, but also on larger scales, such as a watershed level where water consumption upstream affects availability downstream. Integration of fields with respect to their input use and consumption of resources can be used for sound planning of agricultural activities in the region. Table 4 gives an example of efficiencies of energy (ethanol + electricity via fermentation of factory residues) production from sugar beet grown on a sandy soil without irrigation in the Netherlands. The crops were grown under best agricultural practices (BAP) based on 1985–99 weather data. These calculations yielded efficiencies as shown in the second column. We assume here – but policy makers may decide differently according to arguments they may have – that the crops are produced in a sustainable way when they reach an efficiency level that is at least 85% of BAP. When reaching levels below 70% of BAP, production from such crop/environment combinations should be rejected. Above that unsustainable level, an improvement trajectory should be implemented until at least 85% is reached. Soil organic


Water transpired Water from rainfall Land Energy Phosphorus Nitrogen CO 2 Soil organic matter

Best agricultural Sustainpractices ability level threshold

Unsustainability threshold

26 MJ/m3

>22 MJ/m3

15 MJ/m3 >68 GJ/ha >1.6 MJ/MJ >3.1 GJ/kg P >0.5 GJ/kg N >43 kg CO2/GJ