Model responses to changes in model inputs

The application of the proposed sustainability assessment approach to the organic kiwifruit and organic apple systems in New Zealand will help answer the third research question posed in this research: „How well does the proposed approach work for assessing sustainability?‟ Whether the model worked well for assessing sustainability was identified in two ways (Sargent, 1998). Firstly, by comparing the results of sustainability assessment of organic kiwifruit and organic apple production systems with other similar published studies; and secondly, by identifying how well the model responds to changes in model inputs. The primary data (management practices) from growers and the data taken from the published literature (model parameters) constituted the model inputs. Model response to changes in the key management practices and model parameters was undertaken through management scenario analyses and sensitivity analyses respectively, as described below.

4.4.2.1 Model responses to changes in key management practices

Various production practices are possible within official organic certification standards. Environmental impacts will vary with changes in management practices. To determine the effects of key variations in the management practices on sustainability results, a management scenario analysis was carried out. The different management scenarios were determined from the information collected from the growers and they are detailed in the kiwifruit and apple systems sustainability analysis chapters (Chapters 5 & 6), respectively. Management scenario analysis was also carried out for key scenarios identified from the literature. Management scenario analysis was undertaken using the same computer modelling tools, as those used for estimating the sustainability indicators.

4.4.2.2 Model responses to changes in key model parameters

In this research (independent of data collected from the growers) information was also gathered on various energy and matter coefficients, which were taken from the published literature. These data were used to convert primary data into appropriate energy and matter equivalents, in order to estimate sustainability indicators. The aim in undertaking the sensitivity analysis was to identify whether the model result changed, in response to the uncertainties associated with parameter values taken from the published literature. This analysis was not exhaustive in nature and no attempt was made to quantify the sensitivity of the model to all model parameters. Instead, the sensitivity analysis was undertaken for those model parameters which were either highly influential, or those that had great deal of variation and therefore they could be anticipated to have significant implications for the model results towards sustainability. Parameters were considered highly influential when they either had relatively higher embodied energy content, were used in large amounts or which directly influenced the value for sustainability indicators. Five parameters were considered to be highly influential in the model. These were: the energy content of the fruit; fruit yield; energy content in diesel; carbon sequestered in the vine/tree; and the soil CO2 emission coefficient from the decomposition of organic matter. The energy content in the fruit and the fruit yield directly affects the output energy, and therefore the energy ratio. The carbon sequestered in vine/tree and the rate of soil CO2 emission from decomposition of soil organic matter directly affects the CO2-equivalent emissions and therefore the CO2 ratio. Diesel is the main type of fuel which is anticipated to be used in large amounts for running the machinery, in the majority of the orchard production practices: therefore, diesel energy content is considered an influential model parameter. The sensitivity analysis was also carried out for those parameter values which could vary greatly. For example, the embodied energy content of organic materials such as organic fertilisers and biostimulants may vary greatly, depending upon how energy-intensive were their production processes (Edwards-Jones & Howells, 2001).

For the sensitivity analysis, model parameters were varied one at a time, whilst all the other input variables and parameters were held constant. The same computer modelling tools, used for estimating sustainability indicators, were also used to

undertake the sensitivity analyses. Model parameters were hypothetically varied, to the extent that it changed the model result (e.g., from sustainable to unsustainable) and the percent variation in the model parameter was recorded. If this variation in the parameter value was within the existing uncertainty level associated with that parameter, then the model was considered sensitive to the uncertainty associated with that parameter. For example, the known uncertainty, associated with the energy content in diesel, is ±15% (Biodiesel board, 2007). If the variation of ±15% in the energy content in diesel changed the model response (from sustainable to unsustainable, or vice versa), then the model was considered sensitive to the uncertainty associated with energy content in diesel.

The lack of known uncertainties, associated with other model parameters, is considered as one of the challenges when undertaking sensitivity analysis. For example, known uncertainty for some model parameters could not be found in the literature. These included the uncertainty associated with CO2 sequestration by vine/tree, embodied energy content of organic material and the CO2 emission coefficient for the decomposition of organic matter. These parameters were varied individually and the percent variation in the model parameter, which was required to change the model response, was recorded. A decision was made, based on logic, as to whether variation in that parameter value, to the extent that was required to change the model response, was possible in a real situation or not. The model was considered insensitive to the variation in that parameter, if it was unlikely that the parameter value would vary to the extent that was sufficient to change the model response towards sustainability.

4.5 Summary

In this chapter, the modelling approach, using computer tools, is proposed, in order to assess the sustainability of organic kiwifruit and organic apple systems. Sustainability modelling, at the orchard systems level, is carried out in two steps. Firstly, the model orchard system is described, based on the information gathered from growers, to typify the most significant energy and material flows at the organic orchard systems level. In the second step, sustainability indicators are estimated using two computer software tools, namely, Stella® and Overseer®. The

output from Overseer®, in the form of CO2-equivalent emissions from orchard soils, forms an input to Stella®, in order to estimate the CO2 ratio.

At a spatial level, the sustainability indicators are expressed on per ha basis and are estimated over one calendar year. Whether the differences in key management practices affect the results of sustainability is determined by carrying out management scenario analyses. Whether the uncertainties associated with key model parameters affect the results of sustainability is undertaken through sensitivity analyses.

The sustainability assessment approach presented in this chapter is applied to the organic kiwifruit and organic apple systems, the results of which are presented in the following chapters.