1 Introduction
1.1 Life cycle assessment of cocoa value chains
1.2 The Ecuadorian cocoa value chain
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An agro-industrial “volume” sub-chain that seeks economies of scale on volumes: It is structured around collection centres of collectors/brokers (practicing thermal drying) supplied by small producers, focuses on large volumes of commodity cocoa (i.e. cocoa beans of industrial quality, often under-fermented, dominated by CCN-51 but including as well “commodified” CFA), makes blends, and supplies national agro-exporters and transnationals. Transnationals seek to integrate the supply of raw materials with their international links (value addition outside Ecuador) and in principle seek traceability.
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A “quality” sub-chain based on CFA: It is structured around private or corporate collection centres, mostly provided by large producers who carry out fermentation in crates; it focuses on moderate volumes of CFA to be exported as beans by national agro-exporters. Produces smaller quantities of semi-processed products.
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A “semi-processed” production sub-chain: It is structured around a small group of primary, industrial processors, which use cocoa blends to produce semi-processed products (i.e. liquor, butter, powder) mainly for the international market. They are mainly sourced from small producers.
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A “premium” sub-chain: It is structured around medium-sized producers who produce very high-quality CFA, in very small volumes, traded (after careful post-harvesting) at very high prices on the international market. Batches of grain sold at up to > 12 000 USD·t−1 have been documented.
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Transversally to these four sub-chains, an “organic” sub-chain can be identified: Linked in many cases to Fairtrade certification (and therefore similar to the “premium” sub-chain), it is structured around a handful of associative collection centres or with cooperative statutes. This sub-chain represents a very low weight in terms of volume and value, not due to a lack of demand, but of supply capacity (as the organic price differential does not compensate for the certification costs).
2 Material and methods
2.1 Goal and scope
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The main secondary source, especially of quantitative information, from which the data on the cocoa value chain (in its primary link) was obtained, is the statistical datasets of the National Institute of Statistics and Census (INEC). Every year, INEC carries out a field investigation of the agricultural and livestock sector, through which it collects information on the different agricultural and livestock activities carried out in the country, in order to publish the Continuous Agricultural Surface and Production Statistics during the first quarter of each year. For this purpose, it carries out field operations in the last quarter of each year, where it applies the so-called Survey of Continuous Agricultural Surface and Production (ESPAC, https://www.ecuadorencifras.gob.ec/estadisticas-agropecuarias-2/), which provides information on the production of an annual period and was designed with different reference periods. The resulting databases focus on the agricultural phase only, excluding agro-industry. The ESPAC datasets for 2018 and 2019 were retrieved at a higher level of detail than the officially published data (V. Bucheli, INEC, 04/2020, pers. comm.). The ESPAC 2019 dataset includes ~ 5 500 data points (i.e. individual farms).
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Another important source of data was the repository of the Agricultural Public Information System (SIPA, http://sipa.agricultura.gob.ec/) of the Ministry of Agriculture and Livestock (MAG). This repository includes a land use map that includes details on the types of crop associations (absent in the ESPAC data).
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The Technical Assistance Project for Post-Earthquake Productive Reactivation (Gobierno del Ecuador 2017) funded by the European Union (1.77 USD million) contracted in 2018 an International Technical Assistance with the objective of designing a Competitive Improvement Plan for the Cocoa—Chocolate chain, which contributes to “boosting agro-industrial, inclusive, differentiated and competitive development”. The results of this technical assistance include a participatory diagnosis of the value chain (Henry et al. 2018) and a competitive improvement plan for the year 2025 (Salgado et al. 2019). The primary data obtained during this technical assistance were used here as secondary data.
2.2 Inventories
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The database was manipulated to select the 5 495 records representing cocoa farms (plots producing different varieties are represented separately in the database) and to obtain averages and standard deviations for each type of producer, by region and by variety of cocoa produced.
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The database presents data reported in terms of production of “wet” beans (with pulp) and dry beans, as well as wet:dry beans conversion factors per farm. Using these coefficients, farm data were standardised in terms of dry beans. Using the resulting aggregated and normalised data, preliminary LCIs were constructed, but some inputs required additional manipulations.
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Pesticide use data are reported in ESPAC in terms of label colour, which in Ecuador corresponds to toxicity levels defined by the Agency for Plant and Animal Health Regulation and Control (AGROCALIDAD, https://www.agrocalidad.gob.ec/). Based on the literature, the most commonly applied products were identified and average products were constructed, assuming identical proportions between products in each pesticide category (Supplementary Material).
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The percentage of farms (by type, region and variety) that practiced pruning (i.e. thinning) was obtained from the ESPAC 2018, as this datum was not included in the ESPAC 2019.
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An assumption on mechanisation levels (e.g. use of motor mower/trimmer) was applied to estimate the number of mechanised labour hours associated with each type of producer: small producers at 0%, medium producers at 50%, and large producers at 100% mechanisation.
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The water needs of cocoa plants, used to estimate irrigation levels in combination with the percentages of irrigated area by type, region, and variety, were determined between 1500 and 2 500 mm·ha−1·year−1 (Gaibor Pozo 2017).
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Nitrogen requirements of cocoa plants, used to estimate direct emissions, were determined at 400 kg N·ha−1 for plants aged between 5 and 12 years, based on data from Applied Agricultural Resources (http://www.aarsb.com.my/cocoa-fertilizer-requirements). Other data needed to inform the direct emissions estimation models used—Indigo-N v3 (Bockstaller et al. 2022), IPCC 2019 (Ogle et al. 2019) and ecoinvent (Nemecek and Schnetzer 2012)—were obtained from different sources (Nemecek and Schnetzer 2012; Koch and Salou 2016; Barraza et al. 2017; Ogle et al. 2019; Galland et al. 2020). The choice of direct field emission models for N was based on the discussion in Avadí et al. (2022).
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Key parameter values required to determine the total C inputs to the soil and the C sequestered in the cocoa plants during the life cycle of the plantations were extracted from the literature. They include the biomass and C in the cocoa plant (see above), the biomass and C in pruning residues (average of pruning types: maintenance, light, drastic) (Engracia Manobanda 2018), and the biomass and C in harvest residues (Martínez-Ángel et al. 2015; Estrada León 2018). C content of cocoa biomass was estimated at 0.475 of dry matter (Galarza Ferrín 2019). Estimations of C in agroforestry systems’ biomass was obtained from various sources. Biomass C sequestration in Amazonian agroforestry cocoa systems was estimated at 2.89 t C·ha−1·year−1 for cocoa trees and 4.20 t C·ha−1·year−1 for all other species, based on data in Torres et al. (2014). For Andean agroforestry systems, 5.34 t C·ha−1·year−1 were estimated for cocoa trees plus all other species, based on data in Schneidewind et al. (2019). Both estimates were integrated into the inventories per ha of agroforestry systems. For Amazonian systems, the accumulated necromass reaches 0.59 t·ha−1·year−1 for agroforestry systems and 0.56 for monoculture systems (Torres et al. 2014). Necromass represents an additional contribution of organic matter to soil organic carbon (SOC) sequestration.
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Yields were amortised to account for non-productive years, based on MAG data suggesting that CCN-51 systems start to be productive from the 3rd year and CFA systems from the 3rd or 4th year, and following suggestions from the literature on the importance of such amortisation (Bessou et al. 2013, 2016). The resulting amortisation factors were 69% (of annual yield) for CCN-51 and 62% for CFA. The World Food LCA Database (WFLDB) inventories of perennial systems (Nemecek et al. 2020) also take non-productive years into account, but not the ecoinvent (Wernet et al. 2016) or AGRIBALYSE inventories (Koch and Salou 2016; Asselin-Balençon et al. 2020).
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It was assumed that 100% of applied pesticides end up in the soil compartment (Nemecek and Schnetzer 2012), although more complex modelling has been shown to be necessary (Gentil-Sergent et al. 2021). Such modelling is onerous, and less necessary for cocoa in Ecuador, which is generally a low pesticide use system.
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Median values were retained for impact computations, instead of means, due to large standard deviations and to the non-normal nature of the data (results based on mean values for the agricultural phase are presented in the Supplementary Material). Triangular distributions based on minimum, median, and maximum values for each parameter were used for uncertainty propagation (with Monte Carlo).
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Finally, the inventories by type, region, and variety were further disaggregated to differentiate between monoculture and cultural association systems (agroforestry and other association systems).
Inclusion rates in chocolate products | |||
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Ingredients | Industrial/couverture | Consumption—dark | Consumption—milk |
Cocoa liqueur | 69% | 42% | 25% |
Sugar (local, imported) | 20% | 14% | 40% |
Milk powder (imported) | 11% | 0% | 20% |
Cocoa butter | 28% | 15% | |
Cocoa powder/cake | 16% | 0% |
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Beans (dry equivalent), large producer [CCN-51, CFA, total].
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Beans (dry equivalent), medium producer [CCN-51, CFA, total].
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Beans (dry equivalent), small producer micro-entrepreneur [CCN-51, CFA, total].
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Beans (dry equivalent), small subsistence producer [CCN-51, CFA, total].
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Beans (dry equivalent), national average [CCN-51, CFA, total].
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Ecuadorian chocolate, national average [production-weighted mean of cocoa origins and varieties, and chocolate types as defined in Table 1]; from secondary processing.
2.3 Impact assessment
3 Results and discussion
3.1 Life cycle inventories
CCN-51 | CFA | ||||||
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Items | Units per ha per year | Amazonia | Coast | Highlands | Amazonia | Coast | Highlands |
Sample size | u | 401 | 2046 | 701 | 114 | 917 | 76 |
LUC from oil palm | ha | 0.01 | 0.07 | 0.01 | 0.01 | 0.07 | 0.01 |
C in cocoa tree biomassa | t CO2 | 4.4 (3.3) | 3.9 (4.0) | 4.0 (3.7) | 2.1 (4.6) | 2.0 (2.5) | 3.3 (4.6) |
Additional C sequestered by associated (A) and agroforestry (AF) systemsb | t CO2 | AF: 26.0 (28.2) | A: 4.2 | AF: 15.4 (19.9) | |||
Dry grain yield equivalent | t | 0.24 (0.14) | 0.53 (0.35) | 0.48 (0.34) | 0.22 (0.17) | 0.19 (0.14) | 0.21 (0.20) |
Pruning (average of pruning typesc) | t | 2.08 (1.28) | 2.47 (1.70) | 2.47 (2.08) | 3.01 (3.01) | 4.37 (4.92) | 4.37 (4.70) |
Harvest residues (empty cob: ~ 68%d) | t | 0.70 (0.41) | 1.57 (1.04) | 1.43 (1.02) | 0.64 (0.51) | 0.58 (0.42) | 0.61 (0.58) |
Total biomass contributed to the soil | t | 2.78 (1.69) | 4.04 (2.73) | 3.90 (3.10) | 3.65 (3.52) | 4.95 (5.34) | 4.99 (5.29) |
Crop density | u | 714 (625) | 909 (1111) | 833 (909) | 714 (625) | 769 (769) | 714 (625) |
Irrigatione | ha | 0.002 | 0.286 | 0.106 | 0.009 | 0.077 | 0.053 |
Mechanised pruning (motor mower) | hr | 1.90 | 1.87 | 2.00 | 1.31 | 1.40 | 1.63 |
Mechanised weeding | hr | 8.14 | 8.14 | 8.14 | 8.14 | 8.14 | 8.14 |
Pesticide application (portable sprayer) | hr | 2.54 (0) | 2.16 (0) | 2.06 (0) | 1.88 (0) | 0.56 (0) | 1.29 (0) |
Fertilisation – solids | hr | 5.42 (0) | 14.20 (0) | 22.39 (0) | 1.72 (0) | 3.24 (0) | 19.44 (0) |
Fertilisation – liquids | hr | 0.01 (0) | 0.03 (0) | 0.05 (0) | 0 (0) | 0.05 (0) | 0.05 (0) |
Manure | kg | 7.78 (0) | 2.05 (0) | 72.71 (0) | 0.44 (0) | 4.66 (0) | 81.33 (0) |
Fermented organics | kg | 1.36 (0) | 1.46 (0) | 17.05 (0) | 0.97 (0) | 0.54 (0) | 3.22 (0) |
Liquid organics | kg | 0.06 (0) | 0.19 (0) | 0.31 (0) | 0.002 (0) | 0.30 (0) | 0.30 (0) |
NPK | kg | 20.17 (0) | 62.22 (0) | 35.40 (0) | 7.29 (0) | 10.92 (0) | 30.11 (0) |
N | kg | 2.44 (0) | 15.78 (0) | 6.33 (0) | 1.59 (0) | 3.18 (0) | 1.99 (0) |
P | kg | 0.60 (0) | 0.24 (0) | 2.20 (0) | 0 (0) | 0 (0) | 0 (0) |
K | kg | 0.19 (0) | 3.44 (0) | 0.63 (0) | 0 (0) | 0.12 (0) | 0.01 (0) |
Organic pesticides | kg | 0.08 (0) | 0.04 (0) | 0.05 (0) | 0 (0) | 0.02 (0) | 0 (0) |
Chemical herbicide | kg | 0.79 (0) | 0.78 (0) | 0.62 (0) | 0.63 (0) | 0.20 (0) | 0.31 (0) |
Chemical insecticide | kg | 0.39 (0) | 0.35 (0) | 0.29 (0) | 0.45 (0) | 0.08 (0) | 0.28 (0) |
Chemical fungicide | kg | 0.26 (0) | 0.12 (0) | 0.26 (0) | 0.04 (0) | 0.04 (0) | 0.19 (0) |
Other chemical pesticides | kg | 0.001 (0) | 0.01 (0) | 0.01 (0) | 0.004 (0) | 0.002 (0) | 0.001 (0) |
N in mineral fertilisers | kg | 5.47 (0) | 25.11 (0) | 11.64 (0) | 2.69 (0) | 4.82 (0) | 6.50 (0) |
N in organic fertilisers | kg | 0.05 (0) | 0.02 (0) | 0.48 (0) | 0.01 (0) | 0.03 (0) | 0.42 (0) |
Total N inputs (from mineral and organic fertilisers and crop residues) | kg | 56.85 (31.33) | 96.14 (48.24) | 81.24 (55.68) | 71.78 (67.28) | 100.25 (104.12) | 102.83 (102.10) |
Nitrates: IPCC 2019, without irrigation | kg NO3-N | 1.32 (0) | 6.03 (0) | 2.91 (0) | 0.65 (0) | 1.16 (0) | 1.66 (0) |
Nitrates: IPCC 2019, with irrigation | kg NO3-N | 0.003 (0) | 1.73 (0) | 0.31 (0) | 0.01 (0) | 0.09 (0) | 0.09 (0) |
Ammonia | kg NH3-N | 1.18 (0) | 4.86 (0) | 2.46 (0) | 0.53 (0) | 0.92 (0) | 1.60 (0) |
Nitrous oxide | kg N2O-N | 0.70 (0.38) | 1.20 (0.59) | 1.01 (0.68) | 0.88 (0.82) | 1.23 (1.28) | 1.27 (1.25) |
Nitrogen oxides | kg NOx-N | 0.15 (0.08) | 0.25 (0.12) | 0.21 (0.14) | 0.19 (0.17) | 0.26 (0.27) | 0.27 (0.26) |
Items | Units per t | Quality-oriented post-harvest with solar drying | Volume-oriented post-harvest with thermal drying |
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Area intervened with infrastructure | m2 | 1.40E-3 | 1.40E-3 |
Plastic tunnel | m2 | 1.19E-2 | |
Wooden crates | m3 | 5.00E-5 | |
Plastic bags (15 kg) | u | 5.51 | |
Wooden drawers | m3 | 2.75E-4 | |
Electricity | kWh | 76.41 | 76.41 |
Gas | MJ | 1.07E3 | |
Diesel | MJ | 1.30E3 | |
Petrol/gasoline for transport | kg | 4.59 | 4.59 |
Pallets | u | 2.5E-2 | 2.5E-2 |
Plastic barrels | u | 0.05 | 0.05 |
Scales | u | 0.01 | 0.01 |
Raw material: wet cocoa beans | t | 2.75 | 2.75 |
By-product: mucilage | t | 1.75 | 1.75 |
Items | Units per t | Primary processing | Secondary processing |
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Industrial infrastructure | p | 1.00E-4 | 2.00E-3 |
Electricity | kWh | 3510 | 3134 |
Raw material: fermented and dried cocoa beans | t | 1.20 | |
Raw material: products from primary processing | t | 0.40–0.86 | |
Raw material: sugar, milk powder | t | 0.14–0.60 | |
Packaging materials: cardboard, aluminium foil | kg | 136 | |
Waste: shells | t | 0.11 |
3.2 Absolute and relative impacts
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Irrigation is the single top contributing factor, followed in importance by the provision of infrastructure (which includes machinery and materials), of energy (transport, farm labour, energy embedded in industrial inputs) and of K-fertilisers, led by small entrepreneurial producers. Negative emissions associated with climate change play a key role in balancing and even over-compensating the impacts of the single score (Fig. 8a).
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For the small subsistence producers, the contribution of energy consumption and irrigation is less representative, yet irrigation remains is the main contributing factor, as well as negative direct emissions from both CCN-51 and CFA systems, but chiefly from CCN-51 (Fig. 8b).
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The contribution of these factors increases along the large-medium-smallholder axis (Fig. 8c).
3.3 Sensitivity and variability
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100% of the time, the (single score) impacts per ha of large producers (0.88 t·ha−1) are greater than those of smallholder subsistence producers (0.37 t·ha−1); i.e. the apparent differences between the impacts of these two productions (see Fig. 2) are significant. Only for climate change, 82% of the time the impacts of large producers are smaller than those of smallholder subsistence producers.
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91% of the time, the impacts per t of Amazonian production are lower than those of coastal production; i.e. the apparent differences between the impacts of these two productions (see Fig. 4) are barely significant. Only in the case of water use and land use, the relation between the two systems is inverse.
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100% of the time, the impacts per t of Amazonian CFA production in agroforestry systems are less than those of Amazonian CFA production in monoculture (in both cases, by small subsistence farmers); results driven uniquely by climate change (for all other impact categories contributing to the single score, the relation is inverse). A similar comparison, per ha, between subsistence smallholders growing CFA in cultural association vs. CCN-51 in monoculture, shows that 94% of the time the impacts of the former are lower than those of the latter. These comparisons demonstrate that the apparent differences between the impacts of these systems (see Fig. 5) are significant.
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72% of the time, the impacts per t of the volume sub-chain are lower than those of the quality sub-chain (i.e. a barely significant difference), while 74% of the time, the impacts per t of the premium sub-chain are less than those of the organic sub-chain (i.e. a significant difference).
3.4 Comparison with other world cocoa value chains
Source | Product | Brazil | Côte d’Ivoire | Cameroon | Ecuador | Ghana | Indonesia |
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WFLDB (Nemecek et al. 2020) | Dry beans, from agroforestry systems | − 3566 − 1672 | 26 999 28 911 | 16 868 18 782 | − 3 509 − 1614 | 6 122 8 018 | 36 697 38 608 |
Dry beans, from intensive systems | 556 2 456 | 287 2 178 | 14 637 16 549 | ||||
Dry beans, from improved practices systems | 20 451 22 351 | 13 382 15 282 | 6 250 8 139 | ||||
Dry beans, from extensive systems | 29 186 31 094 | 19 405 21 313 | 8 483 10 377 | ||||
Dry beans, from semi-intensive systems | 675 2 600 | 713 2 638 | 29 511 31 453 | ||||
Dry beans, national average | − 294 1617 | 27 263 29 172 | 17 562 19 474 | − 61 1848 | 7 075 8 968 | 25 004 26 928 | |
ecoinvent (Wernet et al. 2016) | Dry beans, national average | 8 909 10 802 | 14 103 16 011 | 40 413 42 338 | |||
This study | Commodity beans, small producers | − 14 887 3 222 | |||||
Differentiated beans, large producers | − 3 241 2 718 | ||||||
Premium beans, medium producers | − 6 458 2 521 | ||||||
Organic beans, small producers | − 9 957 2 788 |
Source | Product | Ecuador | Ghana | Indonesia | Peru | N/A |
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This study | Average of systems, Amazonia, CFA | − 4.57 3.78 | ||||
Agroforestry system, Amazonia, CFA | − 229 1.81 | |||||
Average of systems, Coast, CCN-51 | − 13.14 3.34 | |||||
Average of systems, Coast, CFA | − 22.76 6.40 | |||||
Average of systems, Highlands, CFA | − 30.82 6.49 | |||||
(Bianchi et al. 2020) a | National average, small producers | 1.51 | 1.25 | |||
Traditional monocrop | 3.10 | |||||
Agroforestry system | 2.00 | |||||
(Pérez Neira 2016) b | National average (traditional, technified) | 2.57 | ||||
(Recanati et al. 2018) c | National average | 2.62 | ||||
AGRIBALYSE (Asselin-Balençon et al. 2020) | Undetermined | 10.92 17.44 | ||||
WFLDB (Nemecek et al. 2020) | Undetermined | 13.65 16.30 |
Impact of land use change (mainly due to deforestation) | Brazil | Côte d’Ivoire | Cameroon | Ecuador | Ghana | Indonesia |
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Global warming potential associated with land use change annualised over 20 years | 23 486 23 495 | 35 473 35 487 | 20 636 20 645 | 83.4 84.4 | 15 786 15 793 | 28 781 28 792 |
3.5 Carbon sequestration (climate change)
Systems | Types | Computation | Sources | t CO2·ha−1·y−1 |
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All CCN-51, monocrop CFA and non-agroforestry associated CFA systems | CCN-51: all types | Total biomass accumulation/system age (cocoa only) | ~ 2.9a | |
Monocrop CFA: all types | ||||
Associated CFA: medium and large types | Total biomass accumulation: other crops, grasses, biomass residues | (Schneidewind et al. 2019) | 4.23 | |
Amazonia agroforestry CFA systems | Small type | Total biomass accumulation/system age (cocoa + other trees) | (Torres et al. 2014) | 25.99 |
Highlands agroforestry CFA systems | Small type | Total biomass accumulation/system age (cocoa + other trees) | (Schneidewind et al. 2019) | 15.35 |