The GIS modelling and projections for the three farming conversions across the case study area indicated a range of soil carbon stock changes with commonly marked decreases for dairy and sheep and beef farming as well as considerable decreases in soil carbon stocks for arable scenarios (Figure 5-6 and Table 5-6). The aggregated results indicated that intensification through irrigation, fertilizer application and tilling can have unwarranted consequences on soil carbon stock and, consequently, could have an impact on overall the soil carbon stocks region-wide. The approach used a weighted-factor model and the estimates were broad extrapolations that will require further investigation using more process based and local evidence modelling. On the other hand, they provided pertinent indications in
comparison with the scenarios and frameworks used. Indeed, Kelliher et al. (2014) estimated the carbon stock under dairy cattle to be between 78 - 100 t C ha-1. In terms of magnitude, this value was comparable to our soil carbon stocks under different land uses in 2014 for both soil carbon data sets. The soil carbon stocks for 2014 were also in agreement with other research quantifying soil carbon stocks on a conventional dairy farm (Appendix A).
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Furthermore, these stocks were similar to the global averages of soil carbon stock, which ranged between 3.1 – 80 t C m-2 (Batjes, 1996). However, if some of the scenario weighted- factors would be applied, certain areas of New Zealand may be at risk of quickly decreasing soil carbon stocks at regional scales, in particular, Waikato, Canterbury and Southland. The latter ones have been intensified considerably over the past 15 years through irrigation schemes, increase stocking rate and fertilizer application to achieve high producing pastures or crop production, whereas the Waikato has been farmed with dairy for much longer. In other words, changing to more intensive harvests could have detrimental consequences. Soils would fix less carbon due to the loss of part of this carbon stock, as indicated by the results. The scenario projections for soil carbon stocks indicate an increasing diversion between pastoral farming and arable farming operations potential in soil carbon storage and or loss per hectare.
On average 10%, 6%, 34% of the soil carbon stocks were lost for the alternative dairy, sheep and beef and arable scenarios, respectively compared to the MfE model default
scenarios (D 1, SB 1 and A 1; Table 5-6). Changing the ‘factor value’ had the biggest impact on projected soil carbon stocks. This was interesting and dangerous, as these small changes in factor values for this case study have shown to have dramatic effects on the predicted soil carbon stock. Dangerous, also, in regard to the potential to pre-empt the desired result with arbitrary values rather than based on actual country, regional or locally supported values, which may show an undesired reality. The data sources used (LCDB, Agribase, and soil carbon data) did not represent any real issue in regard to the model outcomes presented. On the whole, these results highlighted the benefits of combining both spatial data and soil data in carrying out spatial analysis using weighted-factor model frameworks in combination with GIS analyses of soil carbon across regions in order to provide guidance about the changes of soil carbon stocks and variability over time. At the same time, using the evidence-based inputs was equally important.
The literature informed the factor values for the alternative scenarios which suggested that intensification (i.e. irrigation, fertilizer application) can decrease soil carbon stocks to varying degrees and depending on combination with fertilizer or land cover. At the same time it was obviously very dependent on the input factor values in this case. The reduction in carbon stocks and changes in farm management can have a negative effect on a farm’s soil and decrease its nutrient availability (Douglas & Crawford, 1998). This could lead to a reduction in site fertility and pasture growth (Soussana & Lemaire, 2014), thereby reducing
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carbon stocks in the soil in the long-term. At sites where soil carbon stocks were inherently low, intensive farming practices should, consequently, be discouraged to prevent productivity decline from occurring. Otherwise, the negative effects of land cover and management should be mitigated by reducing the overall impact on the soil or increase the area of semi-
permanent woody vegetation biomass pool and reduce the soil carbon disturbance with shelterbelts for example (Nair et al., 2010; Schoeneberger, 2009).
5.5.3 Land use and land cover change in Canterbury
The quantification of land use and land cover change over the past decade revealed minor changes across Canterbury. The analysis showed that it was, and is, still dominated by four major land uses (dairy, beef, sheep, arable) and a variation in land cover (i.e. high producing grass, low producing grass and tussock) (Figure 5-2). Pastoral farming with higher production grassland outputs has been consistent. This land use and cover combination ultimately modified moisture, energy budgets and ecological processes across farms and landscapes (Mahmood et al., 2014; Pielke et al., 2011). Evidence suggested these land use and cover changes were associated with changes in soil carbon stocks when replacing native vegetation and forests, tussocks or grasslands with crops and high-producing pastures (Guo & Gifford, 2002). Replacement of woody vegetation by agriculture production (i.e. cropping or pastures) was related to changes in local ecosystems and reductions in soil carbon (Guo & Gifford, 2002; McAlpine et al., 2007). This might indicate that tracking agricultural landscape changes were important when trying to understand the effect the changes have. How local change can affect the nearby regions and influence neighbouring and remote areas was still inconclusive (for example, see Pielke et al., 2011). This showed that developing better monitoring through high resolution remote sensing techniques (Verma et al., 2014) and landscape scale data sets on changes in the landscape were vital to understanding the impact future changes can have on soil ecosystems. On the whole, this highlighted the benefits of carrying out future projections of changes across regions in order to provide better
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5.5.4 Spatially-explicit case study area and oportunities for above-ground