Case study 1: combining available and new soil data at local scale

3.3.1 Introduction

Costa Rica is one of the main banana exporters with one of the highest productions worldwide (≈ 50t bananas/ha/yr; FAO, 2016). However, because of intensive use of agro-chemicals and large monoculture plantations, the Costa Rican banana sector is under pressure to produce bananas in a more sustainable way. In a wide range of

73 initiatives, the sector aims to make the production more environmentally friendly (Stoorvogel et al., 2004). The production of bananas coincides with the production of large quantities of crop residues. The crop residues are left on the field (stems and leaves) or returned to the field in a later stage (mainly bunch stalks from the packing plant). As such, the crop residues recycle large amounts of nutrients to the soils, and maintain soil organic matter stocks. However, with the increasing attention for biofuels and other secondary products, the crop residues of the banana plants are also seen as a valuable asset of raw material. Crop residues can be used in various ways like fibre for paper and biomass for biofuel. A recent development is the use of banana fibres for the production of ecologically friendly pallets by the Dutch Limited company Yellow Pallet B.V (www.yellow-pallet.com). For a proper business plan, it was important to know whether crop residues can be removed from banana plantations while sustaining soil fertility and crop productivity. The location specific repercussions for soil management had to be analysed and included in the business plan. The study was implemented on two banana plantations in the humid lowlands in the northeast of Costa Rica: the Banana Tica plantation (10°20’10″ N, 83°40’38″ W) with Eutropepts and Dystric Vitrudands the San Pablo plantation (10°6’45″ N, 83°22’53″ W) with Eutropepts and Humitropepts (soil classifications based on the Soil Survey Staff (1992) by Wielemaker and Vogel, 1993).

3.3.2 Research implementation

The long-term effects of management changes on soil organic matter stocks in a perennial crop can be analysed in different ways. One could do long-term experiments, but in the case of Yellow Pallet, the available resources were limited and commercial interests required answers within a year. Alternatively, various modelling approaches are available that one could make use of since soil organic matter dynamics have been studied intensively (Shibu et al., 2006). However, although one could rely on existing studies and data, it became apparent that most of these studies did not focus on the banana crop. As a result, it was decided to combine available data with a simple soil organic matter model and use field studies to collect very specific data for the banana crop. The organic matter model is described in

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Figure 3.3. It deals with a single soil organic matter pool, organic matter inputs through crop residues, and a decay of soil organic matter through mineralization.

Two conversion factors are important in the model, the humification rate of crop residues into the soil organic matter pool, and the decay rate from soil organic matter towards CO2, i.e., the mineralization rate.

Data for model calibration were collected from different sources:

• Available soil data showed that soil organic matter contents in banana plantations under current management are stable (Fig. 3.4).

• Crop residue production in banana plantation was measured in the field.

• The soil organic matter pool was measured at different locations in the field.

• A field experiment was done in which soil organic matter contents were monitored during one year on plots that did not receive any crop residues and on plots that received normal crop residues.

• Literature data provided insight in specific elements of the system like crop residue production (Vargas and Flores, 1995) and decomposition (Geissen et al., 2009).

Figure 3.3. The soil organic matter model simulates changes in the soil organic matter stock of a banana plantation.

Figure 3.4. Long-term soil organic matter contents in two Costa Rican banana plantations in San Pablo (sedimentary soils) and La Rebusca (volcanic soils), annual measurements at the end of the year.

75 With the above information, the model was calibrated under normal management conditions in such a way that a steady state was obtained. Measurements showed that the banana plantation produced 26.2 t/ha of crop residues (dry weight). The soil organic matter pool was found to be 121 t/ha. The calibration resulted that only 11.2% of the crop residues ends up in the soil organic matter pool through humification and that annually 2.4% of the soil organic matter pool is mineralized and lost. Subsequently, the model was run for a 20 year period under a situation in which 75% of the crop residues are removed. It was assumed that production did not decline and that potentially reductions are compensated by proper fertilizer management. The results show that in a 20 year period, the soil SOM stock would be reduced by 10% to 110 t/ha.

3.3.3 Discussion

The results of the study provided a quick answer to the questions being asked by Yellow Pallet. The expected changes in soil organic matter stocks can be interpreted to assess the required changes in soil management and the repercussions in terms of costs for soil fertility maintenance. The case study is a good example of how available and new soil data complement each other. The analysis was facilitated by the soil organic matter model that integrated all the data. Currently, Costa Rican banana growers do not remove crop residues in banana plantations. Consequently, data on the impact of this management strategy simply were not available. Long-term trials were no option due to Yellow Pallet’s urgent need for a business plan. Literature and models lacked basic knowledge on the banana production system. Therefore, it was impossible just to carry out the data analysis without data collection. The combination of literature, field data, and models proved to be an efficient procedure to provide the required answers for the company.

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3.4 Case study 2: combining available and new soil data at