> Methodology

Several methods have been used for the purpose of analyzing the energy costs of agricultural systems. Each of these has its own advantages and disadvantages, and the choice of methodology necessarily depends upon the objectives of the analysis. Pimentel et al. (1974) discussed "total aggregation", input/output analysis, and process analysis, and opted for the latter when possible. Fluck (1980) substituted the term "statistical analysis" for total aggregation, and also identified a number of problems and pitfalls in energy analysis. Among these, the main problems (and recommendations for their solution) were:

- Nonhomogeneity of energy sources; reduction of energy costs to consistent units for analysis, with due caution in interpretation of data regarding quite different fuels
- Multiple or joint outputs; apportionment of energy among outputs on the basis of weights, prices or enthalpies, as appropriate to the objectives
- Attributability of energy inputs to outputs; caution in specifying boundaries of the system under analysis
- Assignment of total value to energy; invalid, since other resources (e.g., materials, land and capital) are not totally substitutable for energy

Additional recommendations by Fluck (1980) were that a) all inputs to production systems be quantified on the basis of their sequestered primary energy, disallowing the practice of using dietary energy inputs to account for the costs of labor and draft animal power; and b) energy productivity (e.g., kg·MJ-1), not ratios (J·J-1), be used to indicate efficiency.

In this analysis, the following conventions were adopted. All inputs are expressed on a gross energy basis, that is as the amount of fossil fuel energy (in J) required for each step of the process. Originally, inputs of labor and draft animal power were calculated based on the additional work done by the individual, multiplied by the embodied fossil fuel energy coefficient associated with the dietary energy consumed by that individual. Eventually, human and animal draft animal power were omitted from the calculations, due to the lack of coherent data for all processes. As data become available, inclusion of labor or animal power would enable analyses of the effect of substituting work for fuel.

Another decision was made regarding the assignment of energy costs to processes with multiple outputs. As discussed by Fluck (1980), this problem is common to all energy analyses, and a variety of solutions have been adopted. In some cases, workers have ignored secondary products (e.g., Pimentel, 1980b ignored the straws produced during cereal production, and assigned all inputs to the grain product), which effectively reduces the cost of byproduct formation to zero. Others have allocated inputs to several products based upon their respective monetary values. This approach also has flaws, since there are marked variations in price structures among locations and at different times. For example, Cook (1976) assigned 16% of costs of sheep production to wool, and 84% to meat, whereas McChesney et al. (1982) used values of 43 and 57%, respectively. In this study, energy costs have been allocated among outputs on a mass basis, although is some cases it might be preferable to estimate costs on an energy basis. For example, crop residues represent a large proportion of the total production (Table 2). By allocating energy inputs to both the grain and the straw on a mass basis, the fossil fuel energy component of the byproduct (as a feed ingredient) is also accounted for. In some cases, such as soybeans, the main product may be the oil used for human consumption. Since fats and oils have a much higher energy content relative to other organic components, they probably should be assigned a proportionately higher share of the energy inputs (see box).

A less conspicuous example of multiple outputs relates to the wastes produced by animals themselves, and the processing of their products. Farmers around the world recognize the value of animal wastes to improve soil fertility and physical properties. In some cases, nutrients in animal manure may be recycled even more through their use as feed ingredients foe other livestock. For example, accounting for the flow of energy from fossil fuels, into N fertilizer, into a grain crop and its residues, into poultry meat, with some losses in excreta and processing waste which can be fed to cattle to produce meat and milk with some further waste which is then recycled onto the land to help support another crop, is truly a daunting task.. Moreover, the long series of assumptions and approximations needed would likely render such a calculation unreliable to the point of uselessness. A very simplistic example is given below (see box). The point, however, is that simple input-output analyses are likewise fraught with errors of omission and commission, and should be taken with more than a grain of salt.

Example: soybean oil production Production: whole beans 2,100 kg/ha trash 2,100 kg/ha Primary product: 2,100 kg/ha x 16.7% oil = 350 kg oil/ha Byproduct: 2,100 – 350 = 1,7540 kg/ha extracted soybean meal (SBM) In this case, the crop residue has negligible feeding value (although it is a valuable soil amendment). Therefore, the entire energy input is allocated to the harvested crop, as follows: 350 kg oil/ha x 38.9 MJ/kg = 13,615 MJ/ha 1,750 kg SBM x 18.0 MJ/kg = 31,500 MJ/ha Total 13,615 + 31,500 = 45,115 MJ/ha 13,615 ¸ 45,115 = 30% of energy output in primary product (oil) 31,500 ¸ 45,115 = 70% of energy output in byproduct (soybean meal) Therefore, 70% of the fossil fuel energy inputs would be allocated to the production of soybean meal, when the primary product is oil for human consumption.

Having expressed the foregoing caveats, the following sections describe the development and use of a spreadsheet model of several crop and livestock production systems. This model was developed for the purpose of quantifying the fossil fuel energy inputs required by these systems, and to identify potential areas for improvement of energy efficiency. A comment regarding the scope of this exercise is in order. Accurate representation of all possible production systems, species and locations would be an impossible task indeed. Instead, this exercise is focused on several representative species and systems, with some flexibility built in so that users may adapt the model to their own particular situation. Specifically, production of meat and milk by cattle (both extensive and intensive), meat and eggs by poultry, meat and fiber by sheep, and meat by swine, are included. Also included are estimates of the costs of transportation, processing, distribution and preparation of food products of animal origin. Default values are given for all parameters, but extreme variability in all the included processes argues for the use of actual local data for analyses to produce meaningful results.

Example: N in broiler waste A broiler ration might contain 73% corn and 27% soybean meal. Using approximate ratios to account for crop residues and the use of soybeans for oil, the embodied energy in broiler rations (18% crude protein or 2.9% N) might be: Corn: 4.49 MJ/kg x 0.63 = 2.83 MJ/kg grain SBM: 6.08 MJ/kg x 0.7 = 4.26 MJ/kg SBM (2.83 x 0.73) + (4.26 x 0.27) = 3.22 MJ/kg ration Add 10% for costs of feed processing: 3.54 MJ/kg ration At 60% N digestibility and 2:1 feed conversion, 1 kg of ration would produce 1,000 g ¸ 2 x 70% meat yield = 350 g chicken meat plus 1,000 g x 2.9% N x 40% apparent indigestibility = 12 g N (excreta) plus 500 g x 25% processing waste x 3.2% N = 4 g N (processing waste) Fed to finishing cattle (if it were the sole source of N), this would translate into 833 g of a 12% crude protein ration. At a 6:1 feed conversion, this would produce 833g ¸ 6 x 50% meat yield = 69 g beef That is, a 20% increase in the recovery of N (and its embodied energy) in edible product.

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