Modeling the productivity of energy crops in different agro-ecological environments
Highlights
► A model is developed to simulate energy crops across agro-ecological regions. ► New modules are integrated to make this model applicable on a global spatial scale. ► Both site specific and globally gridded data can be used as input data for LINPAC. ► LINPAC satisfactorily reproduced the biomass production of four energy crops.
Introduction
World energy consumption is projected to grow by 44% in the next two decades [1], with a continued supply from fossil fuels. The finiteness of fossil fuels and related price instability, the insecurity of supply because of geo-political instabilities, and the large emissions of CO2 when fossil fuels are used, have stimulated the development of renewable energy sources, including biofuels. Over the past decade biofuel production from food crops like corn, sugarcane, soybean, and palm oil has more than quadrupled for biodiesel and tripled for bio-ethanol [2]. As a result, natural resources like agricultural land are currently used for the production of energy crops for biofuels, thereby directly competing with the demand for food that is projected to increase with 60% in the next two decades [2], [3], [4]. The increased competition for natural resources for the production of food, feed and fuel, and the effects on (in)direct land use change, GHG emissions and biodiversity, have raised much debate about the sustainability and global production potentials of biofuels [5], [6].
Biofuels from inedible crops are presumed to reduce the competition between food and fuel production, as well as to reduce the emissions of GHG more effectively. Grasses, shrubs and trees are considered to be most suitable for this purpose because of low establishment costs, long cultivation duration up to 10–20 years, with minimal nutrient inputs, and high productivity. These conditions favor positive energy and GHG balances with low energy input per output compared to food crops [7], [8]. If used for biofuels production, these crops are grouped under ‘second generation’ biofuel crops and differ from food or so-called ‘first generation’ energy crops [9]. In past decades, various crops have been investigated and identified as second-generation biofuel crops [10], [11], [12], [13], [14]. The most extensively studied species are Miscanthus (Miscanthus spp.), Switchgrass (Panicum virgatum L.) and Reed canary grass (Phalaris arundinacea L.), and woody species of Willow (Salix spp.), Eucalyptus (Eucalyptus spp.) and Poplar (Populus spp.).
Global renewable energy production is expected to grow by 1.6% per year from 2005 to 2030, most of it to be provided by biomass [15]. Reliable information on global crop biomass production potentials and related natural resource use is essential for prudent biofuels policy making, to prevent adverse effects on e.g. food security and the environment. However, worldwide field experiments to explore the crop production potentials are costly, and do not allow easy extrapolation to other regions/periods with different agro-ecological conditions. Validated crop growth models in combination with global environmental data on climate and soils can be used to extrapolate results of field experiments and calculate crop biomass production across the globe. Production of biomass should be quantified following production ecological principles, i.e. crop growth should be determined by the availability of natural resources including light, water and nutrients. Local, regional and global crop biomass volumes can then be calculated based on resource availability, and generate information about the competition for natural resources between food and biofuels production and the implications for the environment.
A plantation of perennial crops can be established using seeds, seedlings or rhizomes, mostly with a low production at the beginning of plantation establishment due to relatively low absorption of resources (such as water and light). The ceiling levels of annual biomass yield will be realized 1–5 years after establishment, and remain rather stable for 10–20 years (stable yield period), after which plantations may be reestablished if yields start to decline. These general characteristics apply to the presented energy crops Miscanthus [16], Reed canary grass [17], Willow [18] and Eucalyptus [19]. A relatively stable yield of perennial crop after plantation establishment [16], [18] makes it possible to predict the perennial production through simulating their annual stable productivity using the light use efficiency (LUE) approach, such as LINTUL (Light INTerception and UtiLization simulator) [20].
Clifton-Brown et al. [21] and Stampfl et al. [22] developed a model to simulate the productivity of Miscanthus in Europe, however the major share of the future global bioenergy supply may come from other continents with different agro-ecological zones [11], and with different species adapted to these zones. Therefore, in this study a more generic model is developed that is able to simulate the production of a number of energy crops across the world and also enables the comparison between species used for food and bioenergy production. In our study, crop growth is analyzed for both potential conditions, i.e. when crops are optimally provided with water and nutrients and protected against pest, diseases and weed, and for water-limited (rain-fed) conditions, i.e. comparable to potential conditions, but considering water from rainfall and local soil water conditions only. In many parts of the world water availability is an important reducing factor for crop growth, and is difficult to supplement in large parts of the world.
The objectives of the present study are: (1) to develop a generic growth model LINPAC (LINTUL model for Perennial and Annual Crops) by adding new modules for perennial crops to an existing LINTUL model for annual crops to simulate the annual growth of important energy crops during their phase with stable biomass productivity on large spatial scales; (2) to calibrate and validate LINPAC against data from field experiments of perennial crops in different agro-ecological zones; (3) to compare key physiological characteristics and production of these energy crops.
Section snippets
Crop selection
There are numerous species promoted as potential energy crops [9], [12], [13]. Out of the most promising candidates, we selected four typical species that can be categorized according to their photosynthetic pathway (C3 or C4), to their adaptation to temperate or tropical climates, and to their growth structure (grasses or shrubs/trees), viz. Miscanthus, Willow, Reed canary grass and Eucalyptus. Miscanthus represents a C4 grass species with a high potential yield and good cold tolerance that
Model sensitivity analysis
Simulated biomass yield was most sensitive to both LUEmax and harvest index (HI), whereas simulated LAI was most sensitive to maximum LAI (LAImax) and temperature sums Tsm2 and Tsm3 (Table 4).
Model calibration
The calibrated value for LUEmax of Miscanthus (C4) is much higher than the LUEmax of the other three (C3) species (Table 5), indicating Miscanthus’ different photosynthetic pathway to use solar energy more efficiently. The range of reported base temperatures is larger for Miscanthus than for any of the
Discussion
Global debates on food and biofuels require reliable information about the terrestrial production potentials of food and biofuels crops. To this aim, LINPAC for global simulation of crops follows an aggregate methodology for calculating the biomasses of energy crops on the basis of light interception and utilization. Such an approach is useful for modeling tasks on large spatial scales, such as the estimation of global terrestrial carbon balance [122]. The concept is proven to be valuable for
Conclusions
A relatively stable yield of perennial crops after plantation establishment allows calculation of total biomass yield in a perennial growth cycle of crops through its stable annual biomass yield. LINPAC simulates the growth, the development (including LAI dynamics) and the biomass of variable species (trees/shrubs and grasses) by differing crop generic parameters.
The ability of LINPAC to generate site-specific input parameters for crop growth simulation makes it applicable at regional and
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2016, Renewable and Sustainable Energy ReviewsCitation Excerpt :For this purpose the models dedicated directly to energy crops are used as well as the adapted existing models of plant yield. For instance such models as EPIC – [24], ALMANAC – [25], MISCANMOD – [26], MISCANFOR – [27], WIMOWAC – [28] or Agro-Ibis – [29] are used to calculate the biomass yield from giant miscanthus plantations, whereas the biomass yield of basket willow is calculated with the use of e.g. model 3PG or LINPAC [30–33]. Most of these models require a lot of good quality and difficult to collect input data regarding, for instance, phenological phases, dynamics of leafage growth, selected meteorological data, such as radiation rate, air temperature, precipitation, and data on tillage and plant care procedures applied, that information being often difficult to acquire.
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2016, Industrial Crops and ProductsCitation Excerpt :Degree day factor (°C/days) was derived using the data from a Finnish study (Sahramaa, 2004) with 75 introductions of RCG (both cultivated and wild) cultivated for 5 years in Jokioinen, Finland. Extinction coefficient was used from the study of Jing et al. (2012). Base temperature (BT) for RCG was set to 5 °C which is commonly used value for cold season grasses in plant growth modelling.