Farming Systems Design 2007 - International Environmental ...

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Farming Systems Design 2007 Field-farm scale design and improvement Tab. 2 - Normalized impact values in Cynara crop Cynara cardunculus Impact categories First two year average 4 th year I 75 I 25 N 100 N 50 no input Energy resources use 115.1 91.5 118.5 88.1 5.8 Global warming -3538 -2750 -3064 3225 -946 Ozone depletion 788.5 521.8 808.3 502.0 1.9 Atm. acidification 236.1 236.1 282.7 189.5 5.2 Water eutrophic. 534.9 455.3 589.3 400.9 -17.9 Summer smog 35.4 25.3 32.5 28.1 2.4 Human toss. vs. air 35.6 25.7 33.1 28.1 2.2 Global warming depends on yields, being lower in Arundo compared to Miscanthus and Cynara; negative values indicate the CO 2 balance results favourable for the environment. Ozone depletion, atmosphere acidification and water eutrophication are strictly linked to fertilizers application, and so they resulted low in no input treatment in all studied crops. Summer smog and human toxicity vs. air, in the average, resulted equal to 37.8 and 37.0, respectively (tab. 1 and 2). Tab. 3 – Net CO 2 sequestred by crops (%) In table 3 the Net CO 2 sequestred by crops (%) percentage of net CO 2 2 nd and 3 rd years average Fourth year sequestred by crops is I 75 N 100 I 75 N 50 I 25 N 100 I 25 N 50 no input shown: low differences Arundo donax 96.0 97.1 96.2 97.4 99.7 are recorded within Miscanthus x giganteus 93.1 95.2 92.7 95.5 99.7 Cynara cardunculus 93.8 96.1 94.2 93.5 99.4 studied treatments and within crops: in the second and third years the percentage was higher in Arundo (96.7%), followed by Cynara (95.0%) and Miscanthus (94.1%). In the fourth year, 99.6% of net CO 2 sequestred by crops in the average. In table 4 percentage of agricultural phase in the respect to biofuel chain are indicated: the lowest values are obtained in low input treatment for each crop. As far as water and fertilizer treatments are concerned, in Miscanthus higher values than the other crops were obtained. In particular, its crop management affected energy resource use (52.7%), ozone depletion (52.3%), global warming (49.7%) and water eutrophication (48.5%). Tab. 4 - Percentage of agricultural phase respect to biofuel chain (%) Arundo Miscanthus Cynara Impact categories treatment no input treatment no input treatment no input (average) (average) (average) Energy resources use 43.7 5.2 52.7 6.7 35.9 9.1 Global warming 38.6 2.7 49.7 3.4 33.0 5.4 Ozone depletion 37.5 0.3 52.3 0.4 36.6 0.6 Atm. acidification 17.5 0.6 30.5 0.8 15.1 1.3 Water eutrophic. 32.2 48.5 29.0 Summer smog 11.4 0.8 20.0 1.1 5.6 1.7 Human toss. vs. air 3.4 0.2 7.4 0.3 1.6 0.4 Conclusions Under no input condition, crop management may lead environmental benefits. Irrigation and fertilization strongly affected the environmental impacts. In general, more than 90% of CO 2 sequestred by crops is available for further uses (heat, electricity, bio-ethanol). References Brentrup F. et al. 2001 Application of the Life Cycle Assessment methodology to agricultural production: an example of sugar beet production with different forms of nitrogen fertilizers. European J. od Agronomy, 14: 221-233. Cosentino S. et al. 2005. Confronto tra impatti ambientali di biocombustibili fossili per mezzo della “Life Cycle Assessment” (LCA). Agroindustria, 4, 1: 109-128. Reinhardt G. et al. 2000 Final Report. Bioenergy for Europe: which ones fit best? – A comparative analysis for the community. Heidelberg. pp.178. - 26 -
Farming Systems Design 2007 Field-farm scale design and improvement MODELLING THE AGRO-ENERGY FARM Francesco Danuso 1 , Franco Rosa 2 , Loris Serafino 1 , Federico Vidoni 2 1 Dipartimento di Scienze agrarie e Ambientali (DISA) – Univ. of Udine. Email: francesco.danuso@uniud.it 2 Dipartimento di Economia Agro-industriale (DIEA) – Univ. of Udine. Email: franco.rosa@uniud.it Introduction The increasing oil price due to the shortening oil reserves, the political turmoil in the oil producing countries and the relatively low prices of farm commodities have spurred the search for new agribusiness opportunities offered by the ethanol, biodiesel and biogas productions. Nevertheless, the bio-energy production efficiency at farm level is still questionable, depending on the commodity used, agronomic practices, climate variability and other unpredictable events. Some researchers assess that the energy balance is still negative (Pimentel and Patzek, 2005); other studies (Hill et al., 2005), suggest that the energy produced with the oil and co-products by using energy saving techniques is significantly superior to the energy spent. In this framework, in order to improve the managing and planning skills of agro-energy farms, a modeling approach seems to be mandatory. Hence, for these purposes, a farm dynamic simulation model (X-Farm) to manage sustainable farming systems, taking in specific account the production of energy, has been developed. The model simulates the whole “agro-energy farm”, that uses fossil and sun energy to produce and sell the renewable energy exceeding the energy used for farming activities. Methodology X-Farm has been implemented with SEMoLa (version 5.6; Danuso, 2003) a simulation framework allowing for the management of multiple objects (e.g., the different fields of the farm) by the concept of “group”. Farm processes and activities are described using the concepts of state, rate, parameter and event. Activities (crop production, livestock production, energy production, etc.) are characterized by starting and ending events, temporal window, priority in accessing resources, prerequisites. The X-Farm model is formed by eleven interconnected modules (fig. 1) grouped into five blocks: production, resources, accounting and management. The time simulation step is daily. Main farm productions are represented by crop yields and livestock productions. Sunflower seeds production is the beginning step of the oil chain, while the animal residuals (sludge, slurries, manure) represent a step of the livestock chain to produce biogas. All these processes involve the use of resources in terms of capital (land, buildings, machinery, livestock), labour and managerial skills for the farm organization. For each tractor and machine of the farm, fuel consumption, energy use and labor requirement are computed in the Machinery module whereas workforce characteristic are specified in the Labour module. The Crops module simulates the crop biomass growth and yield under different conditions, depending on climate, soil characteristics, manure applications, machinery utilization and other management choices. In the Oil module, the entire oil production chain is developed as it follows: 1) mechanical extraction with seed crushing; 2) chemical oil extraction with solvent and a co-product as a cake containing protein and residual oil. The Livestock module simulates a livestock enterprise, using the cake obtained after oil extraction to feed the cattle and producing milk and calves. It considers cows in different conditions, in terms of age, weight, number of pregnancy and lactation stages. The milk production of every cow is obtained from the specific Figure 1. The modules of X-Farm and their interrelations - 27 -
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