The Biofuel Delusion: The Fallacy of Large Scale Agro-Biofuel Production

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The amount of grazed biomass was calculated from grazing land statistics [48] , the actual NPP of grazing systems, and grazing intensity, i. The amount of total regional roughage supply was used to calculate the amount of ruminant meat and milk production in each region based on the input-output ratio of the livestock systems.

From regional ruminant meat and milk production, the regional market feed demand of ruminants was derived and added to the total market feed demand. The gap between regional supply and demand in , for meat as well as for cropland products, was assumed to be balanced by international trade: for example, regions where the demand for primary products e.

Discrepancies result from the usage of global average factors. In order to use the model to calculate bioenergy potentials for the year , we modified the original model for the year as described in section 2. With respect to population growth, we used the UN medium variant in which world population is forecast to be 9. For the year we used data on food supply as compiled by the FAO [58] , averaged over the period — in order to avoid climate or other fluctuations, and aggregated to the food categories described in section 2. The diet projected for is compared to that of in Table 3.

This business-as-usual BAU scenario is quite similar to the business-as-usual demand growth scenarios of the FAO for [39] , despite the difference in methodology [59]. In order to test the sensitivity of the bioenergy potential in to diets, we performed an alternative model run, assuming a global food supply of It is included here to demonstrate the dependency of bioenergy potentials on future changes in diets. We used the UN population forecast [57] to derive an estimate of the additional area needed for urban areas and infrastructure as follows.

We assumed that rural infrastructure areas are mostly driven by the need to transport agricultural inputs and produce and by the need to house agricultural population and machinery. We therefore calculated the area of rural infrastructure as a percentage of cropland area in each region, using factors derived from prior work [48]. Urban areas are much smaller than rural infrastructure. We estimated urban areas in by assuming that the per-capita amount of urban area would stay constant from to Globally, urban population is forecast to increase from 2. For East and South-East Europe, the UN forecasts a shrinking urban population; in this region we kept the urban areas constant.

We are aware that such simple assumptions can only serve to derive first-order approximations that might be too low; that is, the results are likely to be conservative. This is not much when compared with existing cropland areas Table 2 , so the ensuing errors introduced by our estimation method will also be small. We used FAO forecasts [39,60] to derive estimates for cropland area change and crop yields until for reference see [54].


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The FAO provides projections of crop production and its drivers yields, area, cropping intensity for selected important food crops cereals, oil crops, sugar crops for industrialized countries and five regional groups of developing countries [39,60]. FAO projections are not based on a formal model, but use expert judgements, mostly of FAO in-house experts, to derive estimates of demand for food, feed, non-food uses, seeds and wastes as well as regionally specific projections of yields and cropped areas. The projections have to fulfil consistency criteria and are improved in an iterative process that involves several stages of revision, ensuring that sectoral and regional knowledge can be incorporated [60].

When these were available, we applied annual growth rates of crop production and its drivers area, yield, cropping intensity as reported by the FAO to our data [48,49] to derive total production volumes and area changes for crops and regions explicitly covered in the relevant reports [39,60] yields were cross-checked, and slightly modified, using GAEZ data [61].

Land-use yields are calculated by multiplying harvest yields and cropping intensity; i. Results are shown in Fig. The FAO does not report projections for fodder crop production.

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Cropland production scenario until Material flow data are reported in metric tons of dry-matter biomass. For sources and details, see text. The results are plausible compared with current crop yields at the national scale [50] and alternative yield forecasts [62].

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Our assumptions deviate from FAO projections only marginally, especially when compared to the level of uncertainty in such a projection. Most global agricultural scenarios assume that growth in agricultural production will depend mostly on increases of yields and only to a smaller extent on a growth of cropland areas [63,64]. With respect to cropland area, we also ran a scenario in which growth of cropland area was doubled in all regions and held constant in all regions where FAO forecasts shrinking cropland areas.

We assumed that the area expansion of cropland and infrastructure consumes the best grazing areas, i. North Africa and Western Asia. The biomass-balance model calculates grazing intensity on grazing land i. Our pattern of cropland expansion Table 3 is comparable to other studies on global cropland potentials [65] and cropland suitability maps [66].


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Based on statistical data reported by the FAO and standardized according to methods described elsewhere [49] , we derived trajectories of the input-output ratios of livestock for the time period from to at the regional level which we projected until based on data on feeding efficiencies of different livestock rearing systems see [54]. Data for — and our projection for are shown in Fig. Development of livestock input-output ratios — Feed demand of A Grazers cattle and buffalo, sheep, goats , B Non-grazers pigs, poultry. Dots indicate the weighted global average, whiskers the ranges between regions.

For details, see text. We calculated bioenergy potentials by distinguishing three fundamentally different production pathways: 1 bioenergy crops on cropland, 2 bioenergy crops on other lands i. We calculated gross potentials for bioenergy supply by assuming that the entire aboveground NPP of bioenergy crops can be used to produce bioenergy, assuming a gross calorific value of dry-matter biomass of The calculation did not take conversion or production losses into account.

In order to calculate the area available for producing bioenergy on cropland, we subtracted the area required for food, feed, and fibre calculated with the biomass-balance model described in section 2. We calculated the bioenergy potential by assuming that the NPP of bioenergy crops is equal to potential NPP [68,69] and that the entire aboveground biomass can be harvested and used to produce bioenergy. To calculate the potential to grow bioenergy crops on other land i. This allows using a significant fraction of the area in grazing land of quality class 1 for bioenergy crops without reducing regional roughage supply.

On the area that becomes available for bioenergy crops through intensification, the bioenergy potential is estimated to be equal to aboveground NPP act taken from [38] ; that is, we assume that bioenergy crops produce the same amount of aboveground biomass per year as the current vegetation [69,68]. The energy potential from unused residues on cropland was calculated by applying harvest indices and usage factors described in section 2. Crop residues are used as feed and for bedding.

The bedding requirement was estimated by calculating the amount of manure produced by livestock and applying factors to estimate bedding demand from indoor manure production derived from [49].

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We are aware that this is a crude assumption and that higher or lower shares of the residues might be required to maintain soil fertility in different regions, depending on soil and climate conditions [70]. We employed the LPJmL model [40] to estimate the effects of changes in temperature, precipitation and CO 2 fertilization on yields of major crops globally at a spatial resolution of 0. Yield calculations were based on process-based simulations of 11 agricultural crops in a mechanistic coupled plant growth and water-balance model for reference, see [40].

We calculated percent changes in agricultural productivity between two year periods: — and —, representing the average productivity of the years and Management intensity was calibrated to match national yield levels as reported by FAO statistics for the s [71]. National and regional agricultural productivities were based on calorie- and area-weighted mean crop productivity of wheat, rice, maize, millet, field pea, sugar beet, sweet potato, soybean, groundnut, sunflower, and rapeseed.

LPJmL simulations were used only to estimate the possible magnitude of the climate-change effect on agricultural yields. In these simulations we assumed constant management intensities and cropping patterns as of the year Changes in management, breeding and cropping area were covered by other data and assumptions as described in sections 2. We did not consider feedbacks between climate change, CO 2 fertilization, and management. Still, our results provide a sound estimate of possible impacts of climate change on agricultural yields with and without CO 2 fertilization effects.

Climate data for these GCM-projections were generated by downscaling the change rates of monthly mean temperatures and monthly precipitation to 0. Since there is no information about the number of wet days in the future, we kept these constant after at the year average of — Considerable uncertainty exists how CO 2 fertilization might influence future crop yields. This is due to both modelling uncertainties and to the fact that it seems likely that there are interrelations between management e.

In the latter case, yield changes are only driven by the modelled changes in precipitation and temperature and the limited adaptation of management as described below , whereas in the first case the full effect of changes in temperature, precipitation, and atmospheric CO 2 levels is taken into account. Relative management levels were kept static, but sowing dates were assumed to be adapted to climate change as described by [40] and for wheat, maize, sunflower, and rapeseed but not for all other crops we also assumed adaptation in selecting suitable varieties.

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Yield data were originally calculated at a spatial resolution of 0. We then calculated the arithmetic mean of the change rates in all 15 scenarios with and without CO 2 fertilization effect.

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These country-level results were then used to calculate the area-weighted average deviation of the crop yields in each region from the yield levels projected by the FAO. Our estimates of changes in crop yields resulting from climate change are presented as region-specific percent change rates in Table 5. Modeled climate impact on cropland yields in with and without CO 2 fertilization.

We found that the global aggregate primary bioenergy potential in the year without climate change amounts to More than half of that potential comes from primary crops on other grazing land, i. Almost half of the potential comes from only two regions, namely Sub-Saharan Africa and Latin America and the Caribbean. Two other regions, Northern America and South-Eastern Asia contribute another quarter, whereas the other regions are only minor contributors.

Climate change could result in changes in cropland yields Table 5 and in the productivity of grazing areas that would have a considerable effect on the modeled bioenergy potential, as shown in Fig. This implies that the global bioenergy potential on cropland and grazing areas is highly dependent on the uncertain effect of climate change on future global yields on agricultural areas. We found that the potential of primary bioenergy on cropland is most sensitive to climate change, whereas the potential on grazing areas and the residue potential is less affected by climate change.

Note, however, that the distinction between primary bioenergy crops on cropland and grazing land is to some extent arbitrary in the sense that assuming a larger extension of cropland until increases the potential for primary bioenergy crops on cropland at the expense of the potential for primary bioenergy crops on grazing land, under ceteris paribus conditions see below.

The reason is the following: Cropland expansion would allow to produce more bioenergy on cropland, but less bioenergy on grazing land, as the expansion of cropland would reduce the area of grazing land and therefore the potential to grow bioenergy there without jeopardizing feed demand. If we assume higher yields and a BAU diet , the bioenergy potential rises to In the first case, the available cropland area is just about sufficient to produce enough food, so bioenergy could in that case only be derived from residues and grazing areas, and the potential drops to In the second case, the bioenergy potential is even higher than under BAU conditions and amounts to The results of this study are based on the FAO projections which describe a world of improved food supply and rapid agricultural intensification.

Note, however, that such area potential studies have been criticized [80] and that it might be difficult to cultivate the soils prevailing in these regions with currently prevailing technologies [64,81]. In particular, in Western Europe and North America, cropland yields reach very high levels. It is difficult to judge whether such yield gains can be realized.

It has been argued that in some regions, most options to achieve yield gains have already been implemented and yields are therefore approaching physiological limits, that the best agricultural lands are already in use and area expansions may result in the use of less well-suited land, and that soil erosion and depletion of nutrient stocks in soils may pose challenges for future yield growth [82—84]. However, improved management could help to sustain yield growth; e.

Substantial investments will be indispensable for maintaining growth in crop yields [85]. Lower rates of yield growth would result in a lower bioenergy potential, as shown in Fig. Achieving high yield gains might, however, result in substantial detrimental environmental impacts such as soil degradation, air and water pollution, biodiversity loss and others [64].

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