18.3 Hypothetical Modelling Data from Europe
In Hawaii, Baker (2010) calculated the break-even price of aquaponics lettuce and Tilapia production based on a hypothetical operation. The study estimates that the break-even price of lettuce is $3.30/kg and tilapia is $11.01/kg. Although his conclusion is that this break-even can potentially be economically viable for Hawaii, such break-even prices are much too high for most European contexts, especially when marketing through retailers and conventional distribution channels. In the Philippines, Bosma (2016) concluded that aquaponics can only be financially sustainable if the producers manage to secure high-end niche markets for fish and large markets for fresh organic vegetables.
Aquaponics on tropical islands (Virgin Islands and Hawaii) and warm, frost-free zones (Australia) contrasts highly to locations further away from the equator. The advantages in warm locations are the lower costs of heating and the seasonally even availability of daylight, thus allowing for potentially low-cost systems to economically survive. A frost-free location close to the equator with little to no seasonal differences makes it cheaper and easier to set up and operate a system year-round, which allows for semi-professional family business setups in those regions. Additionally, local production in these areas is valued higher since leafy green crops are either hard to store (e.g. Australia/heat) or difficult to transport to the customers (Islands) and generally have a much higher contribution margin than in locations such as Europe and Northern America.
Aquaponics can have several advantages in an urban context. Yet, advantages are only effective if the specific urban framework conditions are taken into account and if additional communication efforts are put in place. Peri-urban agroparks are presented by Smeets (2010) as a technically and economically viable solution for urban agriculture, offering synergy potential with existing industry through residual heat and suitable logistics as well as alternative inorganic and organic materials streams, for example, COsub2/sub, from cement production. Rooftop aquaponics utilises “empty” spaces in urban areas (Orsini et al. 2017). Rooftops are often assumed to be free of cost “because they are there”. Yet every space in the city is of high value. An owner of a building will always seek revenue for the space they offer, even the utilisation of vacant rooftops. A rooftop farm carries a high economic risk and changes may have to be made to the building (vents and logistics). Rooftops are also interesting for solar energy production with less risk to the operator (see also Chap. 12).
Whilst aquaponics is often explicitly touted as a production technology suitable for urban environments and even areas with contaminated soil, the real estate cost is often completely underestimated. For example, official real estate prices in Germany can be examined via the online tool BORISplus (2018), revealing a significant gap between inner city limit prices and agricultural land prices. For example, peri-urban real estate within city limits in Dortmund, Germany, is in the 280 €/msup2/sup—350 €/msup2/sup range, whereas agricultural land outside of the city limits is in the 2 €/msup2/sup—6 €/msup2/sup range. In addition to that German building codes grant the privilege to farmers to erect agricultural buildings outside of the city limits. This legal and financial situation renders agricultural land in proximity to economic zones attractive for larger-scale aquaponic farms, leading to the above-mentioned concept of agroparks. The placement of aquaponic farms raises challenges with customer perception. Citizens who have been interviewed about their preference of different urban agriculture concepts for inner city public land use showed a preference for usage that keeps the space accessible for citizens as well as a low acceptance levels for agroparks (Specht et al. 2016). The research results on the acceptance of aquaponics revealed a larger variance than the other potential utilisations, suggesting a citizen ambivalence due to a lack of information on the production method. Additional communication efforts are required as aquaponics is a highly complex and new production system unknown to most people in society including urban populations.
The potentials and risks of aquaponics in an urban context become clear from the paragraph above. Distinct strategies and contingency plans have to be developed in an urban context when planning to implement an aquaponics production facility.
Most of the data currently collected on commercial farmers is focused on locations outside of Europe. A sound economic assessment of aquaponics facilities in European latitudes and climates is difficult, because on the one hand only very few commercial plants exist in Europe and on the other hand technical equipment, scale and business models are very different in other parts of the globe, where commercial aquaponics is more widespread (Bosma et al. 2017). Whilst Goddek et al. (2015) and Thorarinsdottir (2015) provide a very good overview of European commercial plants and their challenges, they present only a few economic parameters such as (targeted) consumer prices, statements on “potentially” achievable income or break-even prices for production. Since these are only valid under the specific conditions of the investigated facilities, only limited statements can be transferred to other locations, even within Europe.
Whilst there are some specific assessments of productivity (e.g. Medina et al. 2015, Petrea et al. 2016), full market potential analysis and well-founded costeffectiveness assessments are not known at the present time. In addition, there are initial studies on technical dynamic models using the methodology system dynamics such as Goddek et al. (2016) and Körner and Holst (2017). This illustrates how essential the availability of comprehensive data is in order to conduct sound profitability analysis.
One of the very few hypothetical modelling cases created with data from within Europe is Morgenstern’s et al. (2017) model. They provided technical data from the pilot plant of the University of Applied Sciences of South Westphalia, which consisted of a commercial fish farm and a standard horticulture system. In this case, investment and full-cost calculations with comprehensive detailed technical data for systems in three different scales were modelled. Model calculations for operational costs for a start-up period of 6 years and investment costs as well as a simplified cost performance difference calculation have been conducted for three differently sized aquaponic farms rearing European catfish (Silurus glanis) and producing lettuce. The calculated sizes were derived from the pilot plant located at the University of Applied Sciences of South Westphalia and the aquaculture scale of the project partner. Modelled aquaculture sizes were 3 msup3/sup, 10 msup3/sup and 300 msup3/sup. A couple of general assumptions and simplifications were made for the calculations, which illustrate the above-presented critiques on the limitations of hypothetical modelling:
Less than average production quality and production losses within the first 5 years have been considered. The profitability calculations are based on a matured and stable production process starting in year 6.
Constant hydroculture production. The complete nutrient stream from the process water was calculated to be consumed by the hydroculture production of lettuce, regardless of seasonal differences and regardless of nutrient availability from the aquaculture.
The hydroculture grow bed size has been calculated to be 60 msup2/sup, 200 msup2/sup and 5.500 msup2/sup.
Heating demand for hydroculture and aquaculture has been approximated with aslightly modified methodology of KTBL (2009). The modelled location of the farm is Düsseldorf, Germany.
Energy costs per kWh have been approximated for production with a combinedheat and power (CHP) system with 15 ct/kWh (electricity) and 5,5 ct/kWh (heat), respectively. For simplicity, a CHP system has not been modelled.
Direct marketing of the products was assumed. Fairly optimistic, but not overlyoptimistic, market prices have been calculated for the products. No extended marketing costs have been included in the calculation, since the marketing effort required to build a customer base and stable market have not been addressed in the project. Neglecting marketing costs assumes that market prices in direct marketing come at no costs and therefore constitute a major simplification of the calculation.
No costs related to the real estate required for the farm have been included in thecalculations. The rationale for this simplification is the vastly different costs for space depending on location and project context.
Labour cost has been calculated at minimum wage, which is a strong assumptionwith regard to high levels of human capital required to run complex aquaponics systems.
Mortality losses of 5% in the aquaculture system are compensated byoverstocking at the start of each production cycle.
An analysis of the cost structure of the modelled production-sized aquaculture system shows that labour, fish feed and juveniles and energy are the main cost drivers, contributing roughly one third of the main costs each. At this point, it has to be emphasised that labour costs are calculated on a minimum wage basis and that costs for the occupied area of the farm have not been considered in the calculations (Fig. 18.1).
Electricity and heating costs offer potential for optimization. Pumps have a lifetime between 2 and 5 years. Inefficient pumps can be replaced with more efficient pumps in the natural machine life cycle. Cost efficiency gains for these kinds of optimizations are simple to calculate, and efficiency gains are also easy to monitor after implementation. Similar measures to reduce heating costs are relatively easy to calculate. For example, the costs and effects of additional insulation panels can be calculated, and also here the gains can be easily monitored.
Labour costs emerge as the main cost driver that shows significant optimization potential with upscaling. Larger-scale systems allow for the usage of labour-saving devices, for example, automated graders or automated feeder-filling machinery. Profitability of these kinds of optimizations has to be calculated on a per-project basis.
Fig. 18.1 Cost structure for aquaculture side of an aquaponics system, hypothetical model from technical data from the pilot plant of the University of Applied Sciences of South Westphalia. (Based on Morgenstern et al. 2017)
Likewise, a cost analysis has been performed for the hydroculture part of the modelled systems. The main cost drivers are labour, seedlings and energy costs for lighting and heating. A higher operational maturity of the production, when the initial start-up learning curve has been mastered, can make room for in-house seedling production. The integration of this production step can offer cost optimization potential. Regarding cost reduction potential of the other cost drivers, energy and labour, the above-described situation is applicable for the hydroculture part as well (Fig. 18.2).
A cost performance difference analysis has been performed for the three system sizes, showing that the microsystem and the small system are not economically viable. There is no exploitable automation and rationalisation potential because of the extremely small aquaculture sizes and the small hydroculture sizes resulting in prohibitively high labour costs. Minimum quantity surcharge and transportation fees for the fish feed and similar effects for other cost categories put an additional financial burden on these two systems.
The production size system has a positive cost performance difference when real estate costs or tenures for the required land are not taken into account (Table 18.1).
Fig. 18.2 Fig. 18.2 Cost structure for hydroponics side of an aquaponics system, hypothetical model from technical data from the pilot plant of the University of Applied Sciences of South Westphalia. (Based on Morgenstern et al. 2017)
Table 18.1 Cost performance analysis of model calculation
table thead tr class=“header” thCost performance difference/th th Unit /th th Micro /th th Small /th th Production /th /tr /thead tbody tr class=“odd” tdContribution margin aquaculture/td td €/a /td td -4173 /td td -2566 /td td 114.862 /td /tr tr class=“even” tdContribution margin hydroculture/td td €/a /td td 691 /td td 13.827 /td td 541.087 /td /tr tr class=“odd” tdSum contribution margins/td td/td td -3.483 /td td 11.260 /td td 655.948 /td /tr tr class=“even” tdLabour cost aquaculture/td td €/a /td td 3.705 /td td 8.198 /td td 45.000 /td /tr tr class=“odd” tdLabour cost hydroculture/td td €/a /td td 3.148 /td td 8.395 /td td 179.443 /td /tr tr class=“even” tdSum labour costs/td td €/a /td td 6.853 /td td 16.593 /td td 224.443 /td /tr tr class=“odd” tdReal estate costs tenure/td td/td td n.a /td td n.a /td td n.a /td /tr tr class=“even” tdDepreciation/td td €/a /td td 7.573 /td td 15.229 /td td 185.269 /td /tr tr class=“odd” tdInterest rate 2%/td td €/a /td td 1.515 /td td 3.046 /td td 37.054 /td /tr tr class=“even” tdCost performance difference/td td €/a /td td -19.424 /td td -23.607 /td td 209.183 /td /tr /tbody /table
Source: Morgenstern et al. (2017)
Table 18.2 Job creation potential
table thead tr class=“header” th/th th Unit /th th Micro /th th Small /th th Production /th /tr /thead tbody tr class=“odd” tdSum labour cost/td td €/a /td td 6.853 /td td 16.593 /td td 224.443 /td /tr tr class=“even” tdSum labour time/td td Days/a /td td 46 /td td 111 /td td1.496/td /tr tr class=“odd” tdNumber of jobs/td td/td td 0,21 /td td 0,5 /td td 6,8 /td /tr /tbody /table
Source: Morgenstern et al. (2017)
The analysis additionally sheds light on the job creation potential of the respective systems. The model calculation was performed under the assumption that all the required overhead tasks of the enterprise are handled by regular employees, an assumption that is rather optimistic with regard to the fact that the minimum wage has been used for the calculation.
One further assumption was made regarding the separation of jobs: The employees work on both parts of the sytem, the aquaculture and the hydroculture parts, in accordance to the work that is needed by the respective system. This requires an elevated skill set which puts another question mark behind the minimum wage calculation.
Even in the larger-sized production system, the number of jobs created is limited. The calculated number of jobs is congruent with experience from horticultural companies working with hydroponics, which usually employ between five and ten workers per hectare of greenhouse (Table 18.2).
Data on initial investments in aquaponics is on the one hand very difficult to come by and on the other hand even more difficult to compare. Some of the preliminary data collected from other sources on the initial investment needed to set up an aquaponics farm (see Table 18.3) below shows high differences between the initial investments in the systems, either real or in hypothetical modelling. Since the systems differ in the extreme amount of factors, it is highly problematic to draw any conclusions regarding the necessary initial investments. However, the initial investment in aquaponics does seem to be relatively high, which is reflective of the early stage of the industry. We estimate that an initial investment in a commercial aquaponics system in Europe starts
Table 18.3 Estimated investment costs on aquaponics, various sources
table thead tr class=“header” thLiterature source/th th Total investment [approx. per msup2/sup of growth area] /th th Location /th th Aquaculture size and type /th th Hydroponic size and type /th /tr /thead tbody tr class=“odd” tdBailey et al. (1997)/td td $ 22,642 [$ 226/ msup2/sup] /td td Virgin Islands, USA /td td 4 tanks Tilapia No heating /td td 100 msup2/sup Lettuce DWC No greenhouse /td /tr tr class=“even” tdAdler et al. (2000)/td td $ 244,720 [$ 240/ msup2/sup] /td td Shepherdstown, WV, USA /td td 19,000 l 239 msup2/sup Rainbow trout No heating ($ 122,80) /td td cca. 120 msup2/sup Lettuce NFT ($ 17,150) Polyethylene greenhouse with heating and lights ($ 78,770) /td /tr tr class=“odd” tdTokunaga et al. (2015)/td td $ 217,078 [$ 190/ msup2/sup] /td td Hawai’i, USA /td td 75.71 msup3/sup Tilapia /td td 1142 msup2/sup Lettuce DWC /td /tr tr class=“even” tdMorgenstern et al. (2017)/td td € 151.468 [€ 1067/ msup2/sup] /td td Model location: Düsseldorf /td td 3 msup3/sup European catfish /td td 59 msup2/sup grow bed area 83 msup2/sup greenhouse Lettuce DWC /td /tr tr class=“odd” tdMorgenstern et al. (2017)/td td € 304.570 [€ 650/msup2/sup] /td td Model location: Düsseldorf /td td 10 msup3/sup European catfish /td td 195 msup2/sup grow bed area 274 msup2/sup greenhouse Lettuce DWC /td /tr tr class=“even” tdMorgenstern et al. (2017)/td td € 3.705.371 [€ 302/msup2/sup] /td td Model location: Düsseldorf /td td 300 msup3/sup European catfish /td td 5.568 msup2/sup grow bed area 6.682 msup2/sup greenhouse Lettuce DWC /td /tr /tbody /table
with at least 250 EUR / msup2/sup of growth area but can easily require a much higher investment, depending on the outside conditions, the system size and complexity and the length of the growth season aspired to (Table 18.3).
The experimental and pioneering status of commercial aquaponics is one reason why the financing of larger commercial-scale projects can be a challenge. Most aquaponic systems have been financed through research grants or through aquaponics enthusiasts. Personal communication with German banks that are traditionally strong in financing agricultural investments and that are therefore familiar with the intricacies of crop production and animal rearing revealed that they would not finance an aquaponics project due to lack of a proven and established business model (Morgenstern et al. 2017).