Process Data set: acerola (barbados cherry) production | acerola

Process Data set: acerola (barbados cherry) production | acerola | Cutoff, U (en) en

Process information

Key Data Set Information
Location	BR
Geographical representativeness description	The inventory is modelled for the San Francisco valley, states of Pernambuco and Bahia, Brazil.
Reference year	2010
Name	acerola (barbados cherry) production \| acerola \| Cutoff, U
General comment on data set	Acerola, or West Indian cherry (Malpighia Emarginata), is a tropical fruit native to Central America and the Caribbean. It has spread throughout South America, adapting to cultivation in tropical and subtropical climates. Acerola is also cultivated in countries such as Mexico, China, and parts of Southeast Asia and India, but Brazil is one of the world's leading centers for the cultivation, consumption, and export of this fruit. The country has a wide variety of registered cultivars, catering to different processors/consumers. For example, some varieties are suitable for fruit processing, others for fresh consumption, the manufacture of dietary supplements, etc. (Miskinis et al., 2023; Olędzki, Harasym, 2024). Nationally, the Northeast region accounts for the largest volume and value of production, accounting for over 60% of national production. The state of Pernambuco (21,351 t) is the largest national producer in tons, followed by Ceará (7,578 t), Sergipe (5,427 t) and Piauí (4,690 t) (IBGE, 2017). The Sub-middle São Francisco Valley region is one of Brazil's largest fruit-growing hubs. It is located in the semi-arid region of Northeastern Brazil and covers an area of 125,755 km², extending from western Pernambuco to northern Bahia. The inventory details all inputs and emissions related to the establishment and maintenance of a acerola orchard over its 10-year lifespan, on a 1-hectare area, considering implementation, maintenance, and end-of-life. The stages from land clearing and preparation to harvest, for the year 2010, were included, encompassing aspects such as seeds, irrigation (with energy and water), transportation of inputs to the farm and on-farm transportation, and the treatment of waste and wastewater/effluents. This dataset represents the production of 1 kg of fresh acerola (Malpighia emarginata, types Costa Rica, Flor Branca, Okinawa, Sertaneja BRS 152 e Junco), unpackaged, at the farm gate. References: ALLVET QUÍMICA INDUSTRIAL LTDA. Ficha de Informações de Segurança de Produtos Químicos: Bertac aerossol. Londrina: 2004. Disponível em: https://pearsonsaudeanimal.com/wp-content/uploads/2021/12/bertac-aerosol-allvet-fispq.pdf. Acesso em: 13 jan. 2025. ALMAD AGROINDUSTRIA LTDA. Ficha de Informações de Segurança de Produtos Químicos: Óleo mineral USP. Diadema: 2016. Disponível em: https://labmixquimica.com.br/wp-content/uploads/2016/07/026-a_fispq_oleo_mineral_usp-2.pdf. Acesso em: 13 jan. 2025. BARBOSA, G. A. N. et al. Caracterização do resíduo de acerola visando a conversão termoquímica. Blucher Chemical Engineering Proceedings, v. 1, n. 4, p. 881-886, 2017. BARBOZA, S.B.S.C.; TAVARES, E.D.; MELO, M.B. de. Instruções para o cultivo da acerola. Aracaju: EMBRAPA-CPATC, 1996. 42p. (EMBRAPA-CPATC. Circular Técnica. 6). Disponível em: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/44461/1/CPATC-DOCUMENTOS-6-INSTRUCOES-PARA-O-CULTIVO-DA-ACEROLA-FL-13124.pdf. Acesso em: 4 jan. 2025. BARIZON, R.R.M. et al. PestLCI Model: parameterization for scenarios of Brazilian agricultural production. Jaguariúna: Embrapa Meio Ambiente, 2021. 30 p. Disponível em: https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/1137347/1/Barizon-PestLCI-model-2021.pdf. Acesso em: 12 fev. 2025. BORGES, A.L. Recomendações de calagem e adubação para abacaxi, acerola, banana, citros, mamão, mandioca, manga e maracujá. Cruz das Almas: Embrapa Mandioca e Fruticultura, 2021, 2. ed., 303 p. Disponível em: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/226951/1/livro-RecomendacaoCalagemAdubacao-AnaLuciaBorges-AINFO.pdf. Acesso em: 20 março. 2025. CALGARO, M.; BRAGA, M.B. A cultura da acerola. Brasília: EMBRAPA Semiárido, 2012. 3. ed. 144 p. (Coleção Plantar, 69). Disponível em: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128278/1/PLANTAR-Acerola-ed03-2012.pdf. Acesso em: 18 fev. 2025. CARVALHO, P.E.R. Circular técnica 134: Cerejeira da Amazônia (Amburana acreana). Colombo: Embrapa Florestas, 2007, 1. ed. Disponível em: https://www.infoteca.cnptia.embrapa.br/bitstream/doc/313872/1/Circular134.pdf. Acesso em: 16 jan. 2025. COMITÊ DE FOMENTO INDUSTRIAL DE CAMAÇARI (COFIC). Principais Serviços, Produtos e Aplicações das empresas do Polo Industrial de Camaçari. Camaçari, 2024?. Disponível em: https://coficpolo.com.br/pagina.php?p=98. Acesso em: 11 março. 2025. CUNHA, T. J. F. et al. Solos do Submédio do Vale do São Francisco: potencialidades e limitações para uso agrícola. Petrolina: Embrapa Semi-Árido, 60 p., 2008. Disponível em: https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/161560. Acesso em: 10 jan. 2025. DE OLIVEIRA FILHO, J. G. et al. Acerola (Malpighia emarginata) pulp: Characterization and stability of anthocyanins under different conditions. Food Science and Technology, v. 43, 2023. DOS SANTOS, I. A.; NOGUEIRA, L. A. H. Estudo energético do esterco bovino: seu valor de substituição e impacto da biodigestão anaeróbia. Revista Agrogeoambiental, 2012. DOS SANTOS, P.H.; VILLWOCK, A.P.S. Análise da viabilidade econômica do cultivo de acerola em propriedade familiar do estado de Sergipe. Extensão Rural, v. 31, p. e71667-e71667, 2024. EMMENEGGER, M.F.; DÉLERCE-MAURIS, C.; PORTÉ, C. Models integrated in Ecoinvent LCI calculation tool for crop production. Quantis e Ecoinvent Association, 2018. Disponível em: https://19913970.fs1.hubspotusercontent-na1.net/hubfs/19913970/Knowledge%20Base/Database/Sectors/Ecoinvent_Tool_Model_Description_20180130.pdf. Acesso em: 1 fev. 2025. EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA (EMBRAPA). Sistema Nacional de Pesquisa Agropecuária. In: EMBRAPA. Brasília, 1992. Disponível em: https://www.embrapa.br/snpa. Acesso em: 09 out. 2024. EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA (EMBRAPA). Método BRLUC (Brazilian Land Use Change) versão 1.3. 2020. Disponível em: https://brluc.cnpma.embrapa.br/. Acesso em: 07 jan. 2025. FAIST EMMENEGGER, M., REINHARD, J., ZAH, R. Sustainability Quick Check for Biofuels. Background report. Agroscope Reckenholz-Tänikon. Dübendorf, 2009. Disponível em: https://www.researchgate.net/publication/279440119_Sustainability_Quick_Check_for_Biofuels_Background_Report. Acesso em: 17 março 2025. FERREIRA, K. S. et al. Crescimento e acúmulo de nutrientes em mudas de aceroleiras em função da aplicação de diferentes doses de nitrogênio e potássio. In: Colloquium Agrariae. ISSN: 1809-8215. 2019. p. 37-50. FMC QUÍMICA DO BRASIL LTDA. Ficha de Informações de Segurança de Produtos Químicos: Marshal. Campinas: 2021. Disponível em: https://fmcagricola.com.br/Content/Fotos/FISPQ%20-%20Marshal%20400.PDF. Acesso em: 13 jan. 2025. IHARABRAS INDÚSTRIAS QUÍMICAS. Ficha de Informações de Segurança de Produtos Químicos: Iharaguen – S. Sorocaba: 2017. Disponível em: https://silo.tips/download/iharaguen-s-verificar-restioes-constantes-na-lista-de-agrotoxicos-do-estado-do-p. Acesso em: 10 jan. 2025. INSTITUTO NACIONAL DE METEOROLOGIA (INMET). Banco de Dados Meteorológicos. 2025. Disponível em: https://bdmep.inmet.gov.br/. Acesso em: 11 abril 2025. INSTITUTO BRASILEIRO DE INFORMAÇÃO EM CIÊNCIA E TECNOLOGIA (IBICT). Guia Qualidata: requisitos de qualidade de conjuntos de dados para o Banco Nacional de Inventários do Ciclo de Vida. Brasília: IBICT/MCT, 2017. 58 p. Disponível em: https://acv.ibict.br/wp-content/uploads/2017/05/Qualidata.pdf. Acesso em: 20 jan. 2025. INSTITUTO NACIONAL DE PROPRIEDADE INTELECTUAL (INPI). Ficha Técnica de Registro de Indicação Geográfica (IG200701). Petrolina: 2009. Disponível em: https://www.gov.br/inpi/pt-br/servicos/indicacoes-geograficas/arquivos/fichas-tecnicas-de-indicacoes-geograficas/ValedoSubmdioSoFrancisco.pdf. Acesso em: 19 fev. 2025. INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC). Guidelines for national greenhouse gas inventories. Geneva: National Greenhouse Gas Inventories Programme, 2006. Disponível em: https://www.ipcc-nggip.iges.or.jp/public/2006gl/. Acesso em: 10 abril 2025. MISKINIS, R. de A. S. et al. Bioactive compounds from acerola pomace: A review. Food chemistry, v. 404, p. 134613, 2023. MÜLLER CARNEIRO, J., DIAS, A.F., BARROS, V.d. et al. Carbon and water footprints of Brazilian mango produced in the semiarid region. Int J Life Cycle Assess, 24, 735–752 (2019). https://doi.org/10.1007/s11367-018-1527-8. NEMECEK, T. et al. Methodological guidelines for the life cycle inventory of agricultural products. World Food LCA Database (WFLDB). Quantis and Agroscope, v. 3.5, 2019. Disponível em: https://simapro.com/wp-content/uploads/2020/11/WFLDB_MethodologicalGuidelines_v3.5.pdf. Acesso em: 4 fev. 2025. NOVAES, R. M. L. et al. Estimating 20‐year land‐use change and derived CO 2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states. Global Change Biology, v. 23, n. 9, p. 3716-3728, 2017. OLĘDZKI, R.; HARASYM, J. Acerola (Malpighia emarginata) Anti-Inflammatory Activity—A Review. International journal of molecular sciences, v. 25, n. 4, p. 2089, 2024. QUEIROGA, V. de P.; GOMES, J. P.; MENDES, N. V. B. et al. Sistema produtivo da acerola (Malpighia emarginata Sessé & Mociño ex D.C). In: QUEIROGA, V. de P. et al. (ed.). Acerola (Malpighia emarginata Sessé & Mociño ex D.C.): tecnologias de plantio e utilização. Campina Grande: AREPB, 2023. p. 10-127. Disponível em: https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1162484/1/Sistema-produtivio-acerola-2023.pdf. Acesso em: 9 jan. 2025. SPITTI, A. M. D. S. Estudo de Impacto Ambiental - EIA da Fazenda Baixão do Vexame. Pimenteiras: Secretaria Estadual de Meio Ambiente e Recursos Hídricos (SEMARH), 2023. Disponível em: https://siga.semar.pi.gov.br/media/uploads/2023/12/13/6a3a0d90-cdd9-4f88-a3a2-44dea0b538aa.pdf. Acesso em: 07 jan. 2025. STEPAN. MAKON® TD-6. 2023. Disponível em: https://pt.stepan.com/content/stepan-dot-com/pt_br/products-markets/product/MAKONTD6.html. Acesso em: 13 jan. 2025. TEIXEIRA, A. H. C.; FILHO, J. M. P. L. Condições climáticas do Vale do São Francisco. Brasília: EMBRAPA, 2021. Disponível em: https://www.embrapa.br/en/agencia-de-informacao-tecnologica/cultivos/manga/pre-producao/caracteristicas/clima/vale-do-sao-francisco#:~:text=A%20precipita%C3%A7%C3%A3o%20pluvial%2C%20por%20sua,Juazeiro%2C%20%C3%A9%20de%20542%20mm. Acesso em: 10 jan. 2025. UNITED STATES DEPARTAMENT OF AGRICULTURE (USDA). FoodData Central Food Details: Acerola, (west indian cherry), raw. 2018. Disponível em: https://fdc.nal.usda.gov/food-details/171686/nutrients. Acesso em: 1 abril. 2025. VIONNET, S. et al. Quantis Water Database - Technical report. Quantis Water Database consortium. Quantis Switzerland, v. 1, 2012. Disponível em: https://quantis.com/wp-content/uploads/2017/02/wdb_technicalreport_2012-03-19_quantis-1.pdf. Acesso em: 11 jan. 2025.
Copyright	Yes
Owner of data set	Italo Emmanoel M. O. de Moura
Quantitative reference
Reference flow(s)	acerola (barbados cherry) production - 1.0 * 1.0 kg (Mass)
Time representativeness
Time representativeness description	Time period of the most of the data collected for this dataset.
Technological representativeness
Technology description including background system	Cultivation of acerola (barbados cherry)

Modelling and validation

LCI method and allocation

Type of data set

Unit process, black box

LCI Method Principle

Other

Data sources, treatment and representativeness

Completeness

Completeness of product model

No statement

Validation

Type of review
Dependent internal review

Reviewer name and institution
Elaine Aparecida da Silva

Type of review
Independent external review

Reviewer name and institution
Alexandre Nunes Cardoso

Administrative information

Commissioner and goal
Project	Doctoral dissertation by Italo Emmanoel Mesquita Oliveira de Moura, supervised by Elaine Aparecida da Silva, from the Postgraduate Program in Development and Environment - Networked at the Federal University of Piauí entitled "LIFE CYCLE MANAGEMENT OF THE FRUIT PULP VALUE CHAIN"
Data generator
Data set generator / modeller	Italo Emmanoel M. O. de Moura
Data entry by
Time stamp (last saved)	2025-10-10T23:59:59.517+02:00
Data set format(s)	ILCD format
Data entry by	Italo Emmanoel M. O. de Moura
Publication and ownership
UUID	629fdfb2-9162-4b6f-b494-b590e2c5ee7c
Date of last revision	2025-10-10T23:56:44.055+02:00
Data set version	00.00.048
Owner of data set	Italo Emmanoel M. O. de Moura
Copyright	Yes

Inputs and Outputs

Inputs

Type of flow

Classification

Flow

Mean amount

Resulting amount

Minimum amount

Maximum amount

Product flow

A:Agriculture, forestry and fishing / 01:Crop and animal production, hunting and related service activities / 013:Plant propagation / 0130:Plant propagation

fruit tree seedling, for planting

0.00401 Item(s)

Product flow

A:Agriculture, forestry and fishing / 01:Crop and animal production, hunting and related service activities / 016:Support activities to agriculture and post-harvest crop activities / 0161:Support activities for crop production

manure, liquid, cattle

0.698 kg

General comment

According to Calgaro and Braga (2012), the manure consumption data is 125 m3. The density was considered as 933 kg/m3 to convert it to kg (unit of measurement used in the OpenLCA software) (Dos Santos and Nogueira, 2012).

Product flow

C:Manufacturing / 20:Manufacture of chemicals and chemical products / 201:Manufacture of basic chemicals, fertilizers and nitrogen compounds, plastics and synthetic rubber in primary forms / 2012:Manufacture of fertilizers and nitrogen compounds

inorganic nitrogen fertiliser, as N

0.0165 kg

General comment

Product flow

inorganic phosphorus fertiliser, as P2O5

0.0237 kg

General comment

Product flow

inorganic potassium fertiliser, as K2O

0.0153 kg

General comment

Product flow

zinc oxide

1.81E-4 kg

General comment

Product flow

B:Mining and quarrying / 08:Other mining and quarrying / 089:Mining and quarrying n.e.c. / 0891:Mining of chemical and fertilizer minerals

boric acid, anhydrous, powder

4.01E-5 kg

General comment

Product flow

iron sulfate

4.81E-4 kg

General comment

For fertilizers (urea, single superphosphate, and potassium chloride), processes available in the Ecoinvent database (urea, single superphosphate, and potassium chloride) were identified, but all were available in Europe, China, or North America. Thus, similar processes were retrieved that are representative for Brazil. +++++++++++ Queiroga et al. (2023) recommend applying 50 g of FTE (micronutrients) per hole/plant every 3 months (total of 536 kg), with the composition being zinc (6.0%), boron (2.0%), copper (0.8%), iron (6.0%), manganese (3.0%), and molybdenum (0.1%). Quantities of micronutrients not covered by other fertilizers were included. It should be noted that the amount of micronutrients varies according to periodic soil and leaf analyses, so the total amount may vary.

Product flow

B:Mining and quarrying / 07:Mining of metal ores / 072:Mining of non-ferrous metal ores / 0729:Mining of other non-ferrous metal ores

manganese concentrate

2.41E-4 kg

General comment

Product flow

C:Manufacturing / 24:Manufacture of basic metals / 242:Manufacture of basic precious and other non-ferrous metals / 2420:Manufacture of basic precious and other non-ferrous metals

molybdenum trioxide

8.02E-6 kg

General comment

Product flow

dolomite

0.0192 kg

General comment

According to Borges (2021), the composition of dolomitic limestone used in agriculture is 25%-35% calcium oxide (CaO) and magnesium oxide (MgO) >12%, which is suitable with the composition of the selected process (Dolomite: CaMg(CO3)2).

Product flow

copper oxide

5.39E-4 kg

General comment

In the Ecoinvent database, version 3.7, copper oxychloride is only available as elementary flux emissions. Therefore, the fungicide copper oxide was selected due to its similarity to the process. Calgaro and Braga (2012) state that most pathogens that infect acerola trees can be controlled with copper-based fungicides, such as copper oxychloride.

Product flow

C:Manufacturing / 20:Manufacture of chemicals and chemical products / 202:Manufacture of other chemical products / 2023:Manufacture of soap and detergents, cleaning and polishing preparations, pe

ethoxylated alcohol (AE>20)

5.81E-5 kg

General comment

The process recovered in Ecoinvent, version 3.7, was similar to the Polyoxyethylene alkylphenol ether that makes up the Iharaguen-S spreader (selected as the basis for identifying the composition) (Iharabras Indústrias Químicas, 2017). Calgaro and Braga (2012) highlight the need for good spreader coverage and persistence for pest and disease control, given the climatic conditions of the Sub-Middle São Francisco, with irregular rainfall and high solar radiation. Thus, the selected formulation meets the requirements by forming a silicone film, which increases plant longevity. Calgaro and Braga (2012) indicate that the total spreader used in the orchard's 10 years is 10 liters, which was converted to kg (unit of measurement used in the OpenLCA software) considering a density of 0.97 kg/L (Stepan, 2023).

Product flow

C:Manufacturing / 20:Manufacture of chemicals and chemical products / 202:Manufacture of other chemical products / 2021:Manufacture of pesticides and other agrochemical products

pesticide, unspecified

1.43E-4 kg

General comment

Some pesticides were not available in the database (with the exception of mineral oil, which was only available as an elementary flow emission), so the selected process (pesticide, unspecified) encompasses the sum of all products. Calgaro and Braga (2012) indicate that the total amounts of trichlorfon, mineral oil, and carbamate used in the orchard's 10-year history are 29, 10, and 19 liters, respectively. These quantities were converted to kg (the unit of measurement used in the OpenLCA software), using a density of 0.822 g/mL, 0.87 g/cm3, and 1.0644 g/mL, respectively (Allvet Química, 2004; Almad Agroindustrial, 2016; FMC Química, 2021).

Product flow

irrigation

0.97 m3

General comment

Calgaro and Braga (2012) report the total amount of water used in the acerola orchard, which includes irrigation and dilution of pesticides applied through fertigation. 8,000 m3 are required in the first year, 12,000 m3 in the second year, 16,000 m3 in the third year, and 18,000 m3 annually from the fourth to the tenth year of the orchard. The process retrieved in the Ecoinvent database, version 3.7, already includes its respective emissions.

Elementary flow

Elementary flows / Resource / land

Occupation, permanent crop, irrigated

0.599 m2*a

General comment

1 hectare (10,000 m2) occupied for 10 years.

Product flow

A:Agriculture, forestry and fishing / 02:Forestry and logging / 022:Logging / 0220:Logging

harvesting, forestry harvester

1.995833333333333E-7 d

General comment

It was considered that the orchard was established on lands previously forested with Caatinga vegetation and the time required for the suppression of native Caatinga vegetation was estimated by Spitti et al. (2023).

Product flow

L:Real estate activities / 68:Real estate activities

land use change, perennial crop

5.23E-8 m2

General comment

Calculation based on the Brazilian Land Use Change method by Novaes et al. (2017) and Embrapa (2020), in which the percentage of expansion of acerola cultivation should be relativized by the functional unit. The method's own database contains the expansion percentages of different crops, for which acerola was not available. Therefore, the percentage of 14.6% was considered, as it is an average value for some important fruit crops in the region. Natural vegetation use conversion data from 2000 to 2019 for grapes (13.3%), passion fruit (13.56%), mango (15.20%), orange (16.33%), and guava (14.4%) were used to estimate acerola. Thus, the estimated conversion percentage was divided by the yield of 167,000 kg/ha (0.146/167,000).

Elementary flow

Elementary flows / Resource / land

Transformation, from permanent crop, irrigated

1.08E-12 m2

General comment

Calculation based on Novaes et al. (2017), using the following formula: TUT = (Transformed area) / (Harvest yield * Crop lifespan) Where, TUT = land use transformation (m3). Transformed area = area cultivated with acerola (10,000 m2 or 1 ha). Harvest yield = 167,000 kg/ha, obtained from Calgaro and Braga (2012). Perennial crop lifespan = 10 years, based on Barboza et al. (1996).

Elementary flow

Elementary flows / Resource / land

Transformation, to permanent crop, irrigated

1.08E-12 m2

General comment

Product flow

tillage, subsoiling, by subsoiler plow

0.201 m2

General comment

The choice of the deep plowing system was based on the recommendation of Calgaro and Braga (2012), which indicates the need for plowing with subsoiling. According to the process retrieved from Ecoinvent, version 3.7, the plowing time is 1.19 hours per 1 ha. According to data from Calgaro and Braga (2012), this soil preparation process for an acerola orchard requires 4 tractor hours (which occurs only in the first year). Using this data, a proportion calculation was performed to obtain the value in hectares to be included in this inventory. This is necessary to correct the data for Brazilian conditions due to the differences between the two processes, which include soil conditions, the tractor driver's technical skill, and weather conditions that influence the productivity of the tractor driver and the tractor.

Product flow

tillage, harrowing, by offset leveling disc harrow

0.15 m2

General comment

According to the process retrieved from Ecoinvent, version 3.7, the harrowing time is 0.6 hours per 1 ha. According to data from Calgaro and Braga (2012), this soil preparation process for an acerola orchard requires 1.5 tractor hours (which occurs only in the first year). Using this data, a proportion calculation was performed to obtain the value in hectares to be included in this inventory. This is necessary to correct the data for Brazilian conditions due to the differences between the two processes, which include soil conditions, the tractor driver's technical skill, and weather conditions that influence the productivity of both the tractor driver and the tiller.

Product flow

weed control, by brush cutter, pasture

7.9799999999999995 m2

General comment

According to the process retrieved from Ecoinvent, version 3.7, mechanical weeding takes 0.3 hours per 1 ha. According to data from Calgaro and Braga (2012), the aforementioned process of cultivating an acerola orchard requires 40 tractor hours (4 hours per year). Using this data, a proportion calculation was performed to obtain the value in hectares to be included in this inventory. This is necessary to correct the data for specific conditions in Northeast Brazil due to the differences between the two processes, which include soil conditions, the tractor driver's technical skill, and weather conditions that influence the productivity of both the tractor driver and the cultivator.

Product flow

application of plant protection product, by field sprayer

32.699999999999996 m2

General comment

According to the process retrieved from Ecoinvent, version 3.7, the spraying time is approximately 0.11 hours per 1 ha. According to primary data from Calgaro and Braga (2012), 60 tractor hours (6 hours per year) are required for an acerola orchard. Using this data, a proportion calculation was performed to obtain the value in hectares to be entered in this inventory. This is necessary to correct the data for Brazilian conditions due to the differences between the two processes, which include soil conditions, the tractor driver's technical skill, and weather conditions that influence the productivity of the tractor driver and the sprayer.

Product flow

fertilising, by broadcaster

10.3 m2

General comment

This process is different from the liming process mentioned earlier. According to the process retrieved from Ecoinvent, version 3.7, the time for lime application and incorporation is 0.21 hours per 1 ha. According to primary data from Calgaro and Braga (2012), 36 tractor hours are required (4 hours per year, except for the second year) for this process, when cultivating an acerola orchard. Using this data, a proportion calculation was performed to obtain the value in hectares to be included in this inventory. This is necessary to correct the data for Brazilian conditions due to the differences between the two processes, which include soil conditions, the tractor driver's technical skill, and weather conditions that influence the productivity of the tractor driver and the cultivator.

Product flow

H:Transportation and storage / 49:Land transport and transport via pipelines / 492:Other land transport / 4923:Freight transport by road

transport, freight, lorry 16-32 metric ton, EURO4

0.0381 t*km

General comment

It was assumed that all fertilizers, pesticides, and soil conditioners are produced in the Camaçari industrial complex, Bahia (505 km from the farms), and are transported by truck to their intended use and returned empty. According to the Camaçari Industrial Development Committee (COFIC) (2024?), some of the 76 companies located in the industrial complex produce the inputs used in the acerola orchard, including Bayer, Cibrafértil, Copenor, Flopam, Proquigel, Proquigel Candeias, Sulfabrás, Timac, and Vamtec. It was assumed that the seeds and organic fertilizer are produced within the irrigation complex itself, and thus, their transportation was disregarded.

Product flow

transport, tractor and trailer, agricultural

5.0E-4 t*km

General comment

Harvesting is done manually, with acerola cherries stored in polyvinyl chloride boxes, as observed during the fruit receiving stage at fruit pulp companies. According to Dos Santos et al. (2024), manual harvesting occurs through temporary labor, where payment is made according to the number of boxes harvested. Based on Müller Carneiro et al. (2019), it was assumed that planting occurs within the farm's boundaries, which is square (1000 m x 1000 m), and that workers place the boxes in its center. Thus, a tractor and trailer transport the acerola cherries from the central location to the shipping area, a 500 m (0.5 km) journey.

Product flow

C:Manufacturing / 22:Manufacture of rubber and plastics products / 222:Manufacture of plastics products / 2220:Manufacture of plastics products

packaging, for fertilisers

0.00311 kg

General comment

According to Nemecek et al. (2019), the calculation for standard fertilizer packaging assumes 0.002 kg of high-density polyethylene per kg of dry fertilizer. The authors assume 0.5 kg of plant nutrients per kg of dry fertilizer. Considering the fertilizers used in acerola production throughout the orchard's life (organic, chemical, and foliar fertilizers, as well as soil amendments), the total weight is 129,233.79 kg, which is the weight of the plant nutrients. The dry fertilizer mass was obtained using the assumption that 129,233.79 kg was divided by 0.5, resulting in 258,467.58 kg. From this value, the quantity of packages was calculated, multiplying the value of dry fertilizer by 0.002, being 516.93516 kg or 0.51693516 tons.

Product flow

C:Manufacturing / 22:Manufacture of rubber and plastics products / 222:Manufacture of plastics products / 2220:Manufacture of plastics products

packaging, for pesticides

1.1999999999999999E-4 kg

General comment

De acordo com Nemecek et al. (2019), o cálculo da embalagem padrão de pesticidas considera que há 0,058 kg de Polietileno de Alta Densidade por kg de agroquímico líquido. Os autores definem o pressuposto de que há 0,5 kg de ingrediente ativo por kg de produto líquido. Considerando os pesticidas utilizados na produção da acerola (Fungicida, espalhante defensivo, inseticidas e formicida), têm-se o total de 176,4616 litros. O volume de produto líquido foi obtido usando o pressuposto, em que foi dividido o valor de 176,4616 litros de ingrediente ativo por 0,5 obtendo-se o valor de 352,9232 litros. A partir desse valor, foi calculada a quantidade de embalagens, multiplicando o valor de produto líquido por 0,058, sendo de 20,4695456 kg ou 0,0204695456 toneladas.

Elementary flow

Resources / Resources from biosphere

Energy, gross calorific value, in biomass

1.34 MJ

General comment

Calculation performed considering that 100g of acerola contains 134 kJ (USDA, 2018).

Elementary flow

Resources / Resources from air

Carbon dioxide, in air

158.0 kg

General comment

Calculation based on Emmenegger et al. (2018), which defines the carbon content for the nutritional composition as: 440 g C/kg of carbohydrate, 530 g C/kg of protein, 750 g C/kg of fat, 440 g C/kg of fiber and 0 g C/kg of ash. Data on the nutritional composition of acerola were retrieved from USDA (2018), being 76.9 g of carbohydrate/kg of acerola, 4 g of protein/kg, 3 g of fat/kg, 11 g of fiber/kg and 0.2 g of ash/kg. The final values obtained were, respectively, 5,650,612 kg of C from carbohydrate, 354,040 kg of C from protein, 375,750 kg of C from fat and 808,280 kg of C from fiber, totaling 7,188,682 kg of C or 7,188.682 tons. In addition, the stoichiometric factor of 44/12 was applied to convert from C to CO2.

Elementary flow

Elementary flows / Emission to soil / agricultural

Calcium

1.0 kg

Product flow

limestone and gypsum application, by spreader

0.26 m2

General comment

De acordo com o processo recuperado no Ecoinvent (de acordo com Chagas et al. (2018)), versão 3.7, o tempo da calagem é de 0,46 horas por 1 ha. Conforme dados de Calgaro e Braga (2012), são necessárias 2 horas de trator neste processo para a preparação do solo de um pomar de acerola. Com os dados, foi realizado o cálculo de proporção para a obtenção do valor em hectare a ser inserido neste inventário. Isso é necessário para corrigir o dado para condições específicas do Nordeste do Brasil devido as diferenças entre os dois processos, que inclui as condições do solo, a habilidade técnica do tratorista e as condições climáticas que influenciam na produtividade do tratorista e do tratar.

Outputs

Type of flow

Classification

Flow

Mean amount

Resulting amount

Minimum amount

Maximum amount

Product flow

acerola (barbados cherry) production

1.0 kg

General comment

According to data from Calgaro and Braga (2012), the average productivity of the acerola tree in the Submédio São Francisco region is 12 t/ha in the 2nd year, 15 t/ha in the 3rd year and 20 t/ha from the 4th to the 10th year.

Elementary flow

Elementary flows / air / unspecified

Carbon dioxide, fossil

-0.0262 kg

General comment

According to data from Nemecek et al. (2019), for land use transformations (from caatinga to fruit-growing areas), above-ground biomass should be considered. Thus, it was assumed that 8% of this biomass is harvested and stored and 92% is emitted (20% is burned and 72% undergoes decomposition). ++++++++++++ Carbon dioxide (CO2) emissions were calculated based on the IPCC (2006), whose formulas were: For biomass decomposition: E = ((A*(C-avAgri)))/20*44/12 E = Carbon emission [t CO2/ha-1]. A = Transformed area [ha], which is 0.72 ha (72% undergoes decomposition) for the study. C = Carbon stock in biomass and dead organic matter [t C/ha-1], considering values of 14.9 and 38 t C/ha, respectively, for the caatinga, according to data from Müller Carneiro et al. (2019). avAgri = Carbon stock in the cultivated area [t C/ha-1]. The value of 47.85 t C/ha was considered, according to experimental data from Müller Carneiro et al. (2019). For soil carbon: Es=(A*∆Csoil*44/12) ∆Csolo= Csolo*[fc(t_(0 ) )-fc(t_f )]/20 fc (t)=fUT*fRG*fI Where: Es = net carbon emission [kg CO2]. A = transformed area [ha], which is 0.08 ha for the study (8% is harvested and stored). Soil C = soil carbon stock [kg C/ha-1], whose value for native caatinga vegetation is 42,820 kg C/ha-1 (Embrapa, 2020). fc (t) = carbon stock change factor in a period t, with a dimensionless value of 1, according to IPCC (2006). fUT = stock change factor due to LUC, with a dimensionless value of 1, according to IPCC (2006). fRG = stock change factor associated with management practices, with a dimensionless value of 1, according to IPCC (2006). fI = stock change associated with organic matter input, with a dimensionless value of 0.95, according to IPCC (2006). ++++++++++++ The CO2, N2O, CH4, NOx, and CO emissions from biomass burning were calculated based on the IPCC (2006), whose formula was: E burning = A*M_b*C_f*G_ef Where: E burning = gas emissions from biomass burning [kg gas ha-1]. The calculated values were: 13.63 kg CO2/ha-1; 1.77 x 10⁻³ kg N2O/ha-1; 1.94 x 10⁻² kg CH4/ha-1; 0.033 kg NOx/ha-1; and 0.549 kg CO/ha-1. A = burned area [ha], which is 0.2 ha for the study (20% of the biomass burned). Mb = mass of fuel available for combustion (tons.ha-1); and Cf = combustion factor (dimensionless). According to IPCC (2006), the Mb*Cf value is 42.2 kg/ha. Gef = gas emission factor (g gas.kg fuel-1), being 1,613 g GHG/kg (CO2), 0.21 g GHG/kg (N2O), 2.3 g GHG/kg (CH4), 3.9 g GHG/kg (NOx) and 65 g GHG/kg (CO).

Elementary flow

Elementary flows / air / unspecified

Dinitrogen monoxide

3.29E-4 kg

General comment

According to data from Nemecek et al. (2019), for land use transformations (from caatinga to fruit growing areas), above-ground biomass must be considered. Thus, it was considered that 8% of this is harvested and stored and 92% is emitted (20% is burned and 72% undergoes decomposition). ++++++++++++ Dinitrogen monoxide (N2O) emissions from organic matter mineralization were calculated based on the IPCC (2006), whose formula was: For direct emissions: N-N2O emissions = ((F_SOM )*EF1)*44/28 F_SOM=((〖∆C〗_soil*1/R)*1000) Where: N-N2O emissions = Direct emissions from organic matter mineralization [kg N2O/ha-1], calculated at 44.85 kg N₂O/ha. FSOM = amount of N mineralized in the soil due to land use changes [kg N/ha-1]. EF1 = emission factor for N2O, with a dimensionless value of 0.01, according to the IPCC (2006). ∆Csoil = change in carbon stock due to LUC [kg C], previously calculated at 42,820 kg C/ha. R = C:N = Ratio of carbon and nitrogen in soil organic matter, with a value of 15 kg C/kg N, according to IPCC (2006). For indirect emissions from leaching and runoff: N-N_2 O = (( F_SOM )*〖〖Frac〗_(leach-(H))*EF〗_5 )*44/28 Where: N2O-N = amount of N2O emission from leaching and runoff of N additions to soils [kg N2O/ha-1], calculated at 10.09 kg N₂O/ha. FSOM = amount of N mineralized due to land use change [kg N/ha-1], previously calculated at 2,854.67 kg N/ha-1. Frac Leach-(H) = fraction of all N that is lost through leaching and runoff, with a dimensionless value of 0.3, according to IPCC (2006). EF5 = emission factor, with a dimensionless value of 0.0075, according to IPCC (2006). ++++++++++++ The CO2, N2O, CH4, NOX, and CO emissions from biomass burning were calculated based on IPCC (2006), whose formula was: E burning = A*M_b*C_f*G_ef Where: E burning = gas emissions from biomass burning [kg gas ha-1]. The calculated values were: 13.63 kg CO2/ha-1; 1.77 x 10⁻³ kg N2O/ha-1; 1.94 x 10⁻² kg CH4/ha-1; 0.033 kg NOx/ha-1; and 0.549 kg CO/ha-1. A = burned area [ha], which is 0.2 ha for the study (20% of the biomass burned). Mb = mass of fuel available for combustion (ton.ha-1) and Cf = combustion factor (dimensionless). According to IPCC (2006), the value of Mb*Cf is 42.2 kg/ha. Gef = gas emission factor (g gas.kg fuel-1), being 1,613 g GHG/kg (CO2), 0.21 g GHG/kg (N2O), 2.3 g GHG/kg (CH4), 3.9 g GHG/kg (NOx) and 65g GHG/kg (CO).

Elementary flow

Elementary flows / Emission to air / unspecified

Nitrogen oxides, BR

6.93E-5 kg

General comment

According to data from Nemecek et al. (2019), for land use transformations (from caatinga to fruit-growing areas), aboveground biomass should be considered. Thus, it was assumed that 8% of this biomass is harvested and stored and 92% is emitted (20% is burned and 72% decomposes). ++++++++++++ Nitrogen oxide (NOx) emissions due to the mineralization of organic matter were calculated based on the IPCC (2006), whose formula was: 〖NO〗_x=0.21* N_2 O Where: N2O = total amount of N2O emitted [kg N2O/ha-1], previously calculated (topic 30) at 54.94 kg N2O/ha-1. NOx = amount of NOx emitted into the air [kg NOx/ha-1]. ++++++++++++ The CO2, N2O, CH4, NOx, and CO emissions from biomass burning were calculated based on the IPCC (2006), whose formula was: E burning = A*M_b*C_f*G_ef Where: E burning = gas emissions from biomass burning [kg gas ha-1]. The calculated values were: 13.63 kg CO2/ha-1; 1.77 x 10⁻³ kg N2O/ha-1; 1.94 x 10⁻² kg CH4/ha-1; 0.033 kg NOx/ha-1; and 0.549 kg CO/ha-1. A = burned area [ha], which is 0.2 ha for the study (20% of the biomass burned). Mb = mass of fuel available for combustion (tons.ha-1); and Cf = combustion factor (dimensionless). According to IPCC (2006), the Mb*Cf value is 42.2 kg/ha. Gef = gas emission factor (g gas.kg fuel-1), being 1,613 g GHG/kg (CO2), 0.21 g GHG/kg (N2O), 2.3 g GHG/kg (CH4), 3.9 g GHG/kg (NOx) and 65 g GHG/kg (CO).

Elementary flow

Elementary flows / air / unspecified

Carbon monoxide

3.29E-6 kg

General comment

According to data from Nemecek et al. (2019), for land use transformations (from caatinga to fruit-growing areas), aboveground biomass should be considered. Thus, it was assumed that 8% of this biomass is harvested and stored and 92% is emitted (20% is burned and 72% decomposes). ++++++++++++ The CO2, N2O, CH4, NOx, and CO emissions from biomass burning were calculated based on the IPCC (2006), whose formula was: E burning = A*M_b*C_f*G_ef Where: E burning = gas emissions from biomass burning [kg gas ha-1]. The calculated values were: 13.63 kg CO2/ha-1; 1.77 x 10⁻³ kg N2O/ha-1; 1.94 x 10⁻² kg CH4/ha-1; 0.033 kg NOx/ha-1; and 0.549 kg CO/ha-1. A = burned area [ha], which is 0.2 ha for the study (20% of the biomass burned). Mb = mass of fuel available for combustion (tons.ha-1) and Cf = combustion factor (dimensionless). According to the IPCC (2006), the value of Mb*Cf is 42.2 kg/ha. Gef = gas emission factor (g gas.kg fuel-1), being 1,613 g GHG/kg (CO2), 0.21 g GHG/kg (N2O), 2.3 g GHG/kg (CH4), 3.9 g GHG/kg (NOx), and 65 g GHG/kg (CO).

Elementary flow

Elementary flows / Emission to air / unspecified

Methane

1.16E-7 kg

General comment

According to data from Nemecek et al. (2019), for land use transformations (from caatinga to fruit-growing areas), aboveground biomass should be considered. Thus, it was assumed that 8% of this biomass is harvested and stored and 92% is emitted (20% is burned and 72% decomposes). ++++++++++++ The CO2, N2O, CH4, NOx, and CO emissions from biomass burning were calculated based on the IPCC (2006), whose formula was: E burning = A*M_b*C_f*G_ef Where: E burning = gas emissions from biomass burning [kg gas ha-1]. The calculated values were: 13.63 kg CO2/ha-1; 1.77 x 10⁻³ kg N2O/ha-1; 1.94 x 10⁻² kg CH4/ha-1; 0.033 kg NOx/ha-1; and 0.549 kg CO/ha-1. A = burned area [ha], which is 0.2 ha for the study (20% of the biomass burned). Mb = mass of fuel available for combustion (tons.ha-1) and Cf = combustion factor (dimensionless). According to the IPCC (2006), the value of Mb*Cf is 42.2 kg/ha. Gef = gas emission factor (g gas.kg fuel-1), being 1,613 g GHG/kg (CO2), 0.21 g GHG/kg (N2O), 2.3 g GHG/kg (CH4), 3.9 g GHG/kg (NOx), and 65 g GHG/kg (CO).

Elementary flow

Elementary flows / Emission to soil / agricultural

Manure, applied (N component)

0.326 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Manure, applied (P component)

0.326 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Urea

0.0165 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Phosphorus pentoxide

0.0161 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Calcium

0.00473 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Sulfur

0.00284 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Potassium

0.00886 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Magnesium

4.58E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Sulfur

4.58E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Chlorine

0.00504 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Zinc

1.81E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Boron

4.01E-5 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Iron

4.81E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Manganese

2.41E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Molybdenum trioxide

8.02E-6 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Calcium

0.00671 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Magnesium

0.0125 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Copper oxide

5.39E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Alcohols, C12-14, ethoxylated

5.81E-5 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Pesticides, unspecified

4.601E-4 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Cadmium

0.214 kg

General comment

Based on Nemecek et al. (2019) and Emmenegger et al. (2018), it was assumed that all fertilizers, pesticides, and the agricultural amendment (or its active ingredient) were fully emitted to the soil. According to Barizon et al. (2021), a project from the Technical University of Denmark, called OLCA-Pest Project, launched a platform that allowed the quantification of pesticide emissions in LCA studies for the global and Brazilian contexts, but it was discontinued due to inconsistencies (information obtained via email). +++++++++++ Heavy metal emissions to soil were calculated based on Nemecek et al. (2019), using the following formulas: M_(soil i)=(∑〖inputs〗_i-∑〖outputs〗_i)*A_i M soil i = total emissions of heavy metal i to the soil (mg. (ha.year)-1). The final value was converted to kg and considered the 10-year duration of the orchard. Ai = Allocation factor for the share of agricultural inputs in the total inputs for heavy metal. Calculated using the formula: A_i = M_(agro i)/((M_(agro i)+ M_(deposition i))) M agro i = Total heavy metal input from agricultural production (mg (ha.year)-1) (organic and chemical fertilizer), calculated as follows: The heavy metal composition in urea (46% N) is: Cd = 0.11 mg/kg N, Cu = 13.04 mg/kg N, Zn = 95.65 mg/kg N, Pb = 2.39 mg/kg N, Ni = 4.35 mg/kg N, and Cr = 4.35 mg/kg N (Nemecek et al., 2019). The total amount of urea applied over 10 years is 2,750 kg (1,265 kg of N), where the amounts of heavy metal are Cd=139.15 mg(ha.year-1), Cu=16,495.4 mg(ha.year-1), Zn=120,989.75 mg(ha.year-1), Pb=3,023.35 mg(ha.year-1), Ni=5,502.75 mg(ha.year-1) and Cr=5,502.75 mg(ha.year-1). The composition of heavy material in simple superphosphate (19% P2O5) is: Cd=52.63 mg/kg P2O5, Cu=121.05 mg/kg P2O5, Zn=852.63 mg/kg P2O5, Pb=578.95 mg/kg P2O5, Ni=105.26 mg/kg P2O5 and Cr=352.11 mg/kg P2O5 (Nemecek et al., 2019). The total amount of simple superphosphate, applied in 10 years, is 3,950 kg (750.5 kg of P2O5), in which the amounts of heavy metal are Cd=39,493.815 mg(ha.year-1), Cu=90,845.025 mg(ha.year-1), Zn=639,893.815 mg(ha.year-1), Pb=434,502.475 mg(ha.year-1), Ni=78,997.63 mg(ha.year-1) and Cr=264,258.555 mg(ha.year-1). The composition of heavy material in potassium chloride (60% K2O) is: Cd=0.10 mg/kg K2O, Cu=8.33 mg/kg K2O, Zn=76.67 mg/kg K2O, Pb=9.17 mg/kg K2O, Ni=3.50 mg/kg K2O and Cr=3.33 mg/kg K2O (Nemecek et al., 2019). The total amount of potassium chloride applied over 10 years is 2,550 kg (1,530 kg of K2O), where the amounts of heavy metal are Cd=153 mg(ha.year-1), Cu=12,744.9 mg(ha.year-1), Zn=117,305.1 mg(ha.year-1), Pb=14,020.1 mg(ha.year-1), Ni=5,355 mg(ha.year-1) and Cr=5,094.9 mg(ha.year-1). The composition of heavy material in liquid cattle manure (9% dry matter) is: Cd=0.18 mg/kg dry matter, Cu=37.1 mg/kg dry matter, Zn=162.2 mg/kg dry matter, Pb=3.77 mg/kg dry matter, Ni=4.3 mg/kg dry matter, Cr=3.9 mg/kg dry matter and Hg=0.4 mg/kg dry matter (Nemecek et al., 2019). The total amount of cattle manure, applied in 10 years, is 116,625 kg (10,496.25 kg of dry matter), in which the amounts of heavy metal are Cd=1,889.325 mg(ha.year-1), Cu=389,416.875 mg(ha.year-1), Zn=1,702,434.45 mg(ha.year-1), Pb=39,570.76 mg(ha.year-1), Ni=45,133.875 mg(ha.year-1), Cr=40,935.375 mg(ha.year-1) and Hg=4,198.5 mg(ha.year-1). From the calculated values, the total amount of each heavy metal is described in Table 2. Table 2. Calculated Values for M agro i Heavy Metal Quantities (mg (ha year-1)) Urea, Simple Superphosphate, Potassium Chloride, Manure, Total (M agro i) Cd 139.15 39,493.815 153 1,889.325 41,675.29 Cu 16,495.4 90,845.025 12,744.9 389,416.875 509,502.2 Zn 120,989.75 639,893.815 117,305.1 1,702,434.45 2,580,623.12 Pb 3,023.35 434,502.475 14,020.1 39,570.76 491,116.685 Ni 5,502.75 78,997.63 5,355 45,133.875 134,989.255 Cr 5,502.75 264,258.555 5,094.9 40,935.375 315,791.58 Hg - - - 4,198.5 4,198.5 Source: Author's elaboration (2024). M deposition i = Total heavy metal input from atmospheric deposition (mg (ha.year)-1). The following values were tabulated: Cd = 700 mg/ha/year, Cu = 2,400 mg/ha/year, Zn = 90,400 mg/ha/year, Pb = 18,700 mg/ha/year, Ni = 5,475 mg/ha/year, Cr = 3,650 mg/ha/year, and Hg = 50 mg/ha/year (Nemecek et al., 2019). The 10-year orchard period was considered. Therefore, the allocation factor Ai value for each heavy metal is: Cd = 0.856, Cu = 0.955, Zn = 0.741, Pb = 0.724, Ni = 0.711, Cr = 0.896, and Hg = 0.894. ∑ inputs i = total quantity of all inputs to the soil (organic and chemical fertilizers, pesticides, and deposition) kg(ha.year)-1, with the same value of M agro i calculated. Thus, we have: Cd = 41,675.29 kg/ha; Cu = 509,502.2 kg/ha; Zn = 2,580,623.12 kg/ha; Pb = 491,116.685 kg/ha; Ni = 134,989.255 kg/ha; Cr = 315,791.58 kg/ha; and Hg = 4,198.5 kg/ha. ∑ outputs i = total quantity of all outputs to the soil (leaching and erosion). For leaching, the values were calculated (in topic 44), being: Cd=0.000428 kg/ha; Cr=0.19 kg/ha; Cu=0.03438 kg/ha; Pb=0.004344 kg/ha; Hg=0.00001162 kg/ha; Ni=0 kg/ha and Zn=0.24453 kg/ha. For erosion, the values were calculated (in topic 47), being: Cd=0.00002422 kg/ha; Cr=0.2714637 kg/ha; Cu=0.2142713 kg/ha; Pb=0.1890496 kg/ha; Hg=0.0000861 kg/ha; Ni=0.2101429 kg/ha and Zn=0.3887187. Thus, the total output value for each heavy metal is Cd=0.00045222 kg/ha; Cr=0.4614637 kg/ha; Cu=0.2486513 kg/ha; Pb=0.1933936 kg/ha; Hg=0.00009772 kg/ha; Ni=0.2101429 kg/ha and Zn=0.6332487 kg/ha.

Elementary flow

Elementary flows / Emission to soil / agricultural

Chromium

1.69 kg

General comment

Elementary flow

Elementary flows / soil / agricultural

Copper

2.91 kg

General comment

Elementary flow

Elementary flows / soil / agricultural

Lead

2.13 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Mercury

0.0225 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Nickel

0.575 kg

General comment

Elementary flow

Elementary flows / Emission to soil / agricultural

Zinc

11.5 kg

General comment

Elementary flow

Elementary flows / Emission to air / unspecified

Ammonia, BR

0.00262 kg

General comment

Calculation based on Nemecek et al. (2019), which describes the European Monitoring and Evaluation Programme (EMEP) model of the European Environment Agency (EEA) as suitable for Ecoinvent processes. Therefore, the calculation formula used was: NH_3= 17/14*∑_(m-1)^M(EFa_m*p+EFb_m*(1-p))*N_(min,m) Where: NH3 = ammonia emissions after mineral fertilizer application [kg NH3]. m = fertilizer type (M = number of fertilizer types). In this case, only urea was used, as established by the method used, eliminating the need for summation. EFam = emission factor in soils with pH <=7 [kg NH3-N/kg N]. Tabled value of 0.131, recovered considering the region (tropical climate) and fertilizer type (urea). EFbm = emission factor in soils with pH>7 [kg NH3-N/kg N]. Disregarded for the study context, as there is no soil with an alkaline pH. p = fraction of soils with pH <= 7 [%/100]. 1 (100%) was considered for the study context, which has acidic soils. N min, m = mineral fertilizer application [kg N]. Same amount as the input (2,750 kg).

Elementary flow

Elementary flows / Emission to air / unspecified

Nitrogen oxides, BR

6.76E-4 kg

General comment

Calculation based on Emmenegger et al. (2018), using EEA models. For mineral and organic fertilizer (manure), a NOx-N emission factor of 0.018667 kg NOx-N/kg N applied and a N to NO2 conversion factor of 46/14 were considered (necessary because the format accepted in the Ecoinvent database is NO2). Final value obtained considering inputs of 2,750 kg of urea (46% N/kg) and 125 m3 of cattle manure (4.6 kg N tot/m3 of manure), being 1,265 and 575 kg of N, respectively.

Elementary flow

Elementary flows / air / unspecified

Dinitrogen monoxide

7.02E-4 kg

General comment

Nitrous oxide emissions to air were calculated based on Nemecek et al. (2019) N2O = 44/28*(0.01*(N tot+N cr+N som+14/17*〖NH〗_3+14/46*〖NO〗_x+0.0075*14/62*〖NO〗_3) Where: N2O = N2O emission [kg N2O ha-1]. Ntot = total nitrogen in mineral and organic fertilizers [kg N ha-1], with 46% N from the 2,750 kg of urea used (1,265 kg N/ha-1) and 2.3 kg N from the 125 m3 of manure (287.5 kg N/ha-1). Totaling 1,552.5 kg N/ha-1. Ncr = nitrogen contained in crop residues [kg N ha-1]. Barbosa et al. (2017) calculated the nitrogen content for acerola trees as 1.66% per kg of fruit, corresponding to 2,772.2 kg N ha-1. Nsom = nitrogen from soil organic matter mineralization [kg N ha-1]. A value of 1,350 kg N/ha was considered, according to experimental data from Müller Carneiro et al. (2019). NH3 = nitrogen losses in the form of ammonia [kg NH3 ha-1]. According to the previous calculation (topic 35), this was 437.74 kg NH3 ha-1. NOx = nitrogen losses in the form of nitrogen oxides [kg NO2 ha-1]. According to the previous calculation (topic 36), this was 112.831 kg NO2 ha-1. NO3 = nitrogen losses in the form of nitrate [kg NO3 ha-1]. In this regard, a change was made to the formula, disregarding the NO3 to N conversion factor. (14/62), since the value of 179,315.37 kg N/ha was previously calculated (topic 41).

Elementary flow

Elementary flows / air / unspecified

Carbon dioxide, fossil

0.0353 kg

General comment

Carbon dioxide emissions from the use of urea and lime, which were calculated based on Nemecek et al. (2019). The recovered emission factors were 1.57 kg CO2/kg Urea-N16 applied, 0.44 kg CO2/kg limestone and 0.48 kg CO2/kg dolomite, whose total emission was 5,887.16 kg of CO2 (being: 4,317.5 kg CO2 from urea; 33.66 kg of CO2 from limestone and 1,536 kg of CO2 from dolomite).

Elementary flow

Elementary flows / Emission to air / unspecified

Water

0.00118 m3

General comment

Some of the water used for irrigation in orchards is emitted into the air due to evotranspiration. According to Vionnet et al. (2012), the evotranspiration rate for food production is 0.122% of the total water used for irrigation.

Elementary flow

Elementary flows / Emission to water / ground water

Nitrate

1.07 kg

General comment

Calculation based on Nemecek et al. (2019), which describes the SQCB-NO3 model by Faist Emmenegger et al. (2009) as the most appropriate for non-European countries. The calculation formula used was: N = 21.37 + P/(c*L)*[0.0037*S+0.0000601*Norg-0.00362*U] Where: N = NO3 - leached N [kg N/(ha.year)]. P = precipitation + irrigation [mm/year]. The average annual precipitation is 567 mm per year in the São Francisco Valley (Teixeira and Filho, 2021), and irrigation in the acerola orchard over 10 years is 162,000 m3 (16,200 m3/year) (Calgaro and Braga, 2012). The irrigation value is divided by 1 hectare (10,000 m2) to obtain the water depth in meters (1.62 meters or 1,620 mm/year). c = clay content [%]. Based on Cunha et al. (2008), it was assumed that acerola trees are grown in Quartzips (6%), Ultisols (20%), and Latosols (15%) soils, so the average value of their clay content (14%) was used. L = rooting depth [m]. According to Calgaro and Braga (2012), the effective depth of the acerola tree root system is 0.6 m. S = nitrogen supply through fertilizer [kg N/ha]. Value calculated (topic 36) based on the amount of nitrogen from urea application, 1,265 kg/N ha-1, and from cattle manure, 575 kg N/ha-1. Norg = nitrogen in organic matter [kg N/ha]. A value of 1,350 kg N/ha was considered, according to experimental data from Müller Carneiro et al. (2019). U = nitrogen uptake by the crop [kg N/ha]. The final value was estimated at 0.77 kg N/ha. For this purpose, data from Ferreira et al. (2019) were used, who calculated nitrogen uptake at 114.5 mg N/plant-1, considering the 670 plants in the orchard.

Elementary flow

Elementary flows / Emission to water / ground water

Water, BR

7.89E-5 m3

General comment

Some of the water used for orchard irrigation is emitted to groundwater due to percolation. The formula described in Nemecek et al. (2019) was used to calculate the emitted amount: 〖Emission〗_(subwater)=0.2*((〖ET〗_irr/〖EF〗_irr ) - 〖ET〗_irr ) Where: Ewater = water emitted to groundwater [m3/t of acerola]. ETirr = Irrigation evapotranspiration [m3/t of acerola], being 197.64 m3 (calculated in topic 40) divided by 167 t of acerola produced. The total value is 1.1835 m3/t. EFirr = Irrigation efficiency factor [dimensionless], which is a tabulated value for the type of irrigation used. According to Calgaro and Braga (2012), the micro-sprinkler irrigation method is the most common for the acerola tree, which has an efficiency factor of 0.75, as recovered in Nemecek et al. (2019).

Elementary flow

Elementary flows / Emission to water / ground water

Cadmium, ion

2.56E-9 kg

General comment

The data and calculation formula for heavy metal emissions to groundwater, described in Nemecek et al. (2019), were used: M_(leach i) = m_(leach i)* A_i Where: M leach i = total emission of heavy metal i from leaching (mg.(ha.year)-1). The final value was converted to kg and considered the 10-year duration of the orchard. m leach = Average amount of heavy metal emission to water (mg.(ha.year)-1), tabulated as Cd=50 mg/ha/year, Cu=3,600, Zn=33,000, Pb=600, Ni=n.a., Cr=21,200, and Hg=1.3. Ai = Allocation factor for the share of agricultural inputs in the total inputs for heavy metals. As calculated previously (topic 34), the value of the allocation factor Ai for each heavy metal is: Cd=0.856, Cu=0.955, Zn=0.741, Pb=0.724, Ni=0.711, Cr=0.896 and Hg=0.894.

Elementary flow

Elementary flows / Emission to water / ground water

Chromium, ion

1.14E-6 kg

General comment

Elementary flow

Elementary flows / Emission to water / ground water

Copper, ion

2.06E-7 kg

General comment

Elementary flow

Elementary flows / Emission to water / ground water

Lead

2.6E-8 kg

General comment

Elementary flow

Elementary flows / Emission to water / ground water

Mercury

6.96E-11 kg

General comment

Elementary flow

Elementary flows / Emission to water / ground water

Zinc, ion

1.46E-6 kg

General comment

Elementary flow

Elementary flows / water / surface water

Phosphorus

6.01E-4 kg

General comment

Calculated based on Faist Emmenegger et al. (2009), where the formula for calculation is as follows: P_er=S_er*P_cs*F_r*F_erw Where: Per = amount of P released by erosion into rivers [kg P/(ha.year)]. The 10-year orchard period was considered. Ser = amount of eroded soil [kg/(ha.year)]. Calculation performed using the universal soil loss equation, described by Faist Emmenegger et al. (2009), and indicator values. Where: S_er=R*k*LS*c1*c2*P Where: R = Erosivity Factor [MJ mm ha-1 h-1 yr-1], which is calculated using the formula: R={█(0.0483*P^(1.61) if P≤850 mm@587.8-1.219*P+0.004105*P^2 if P>850 mm)┤ P=precipitation+irrigation*0.1 P = annual precipitation [mm/yr-1], where 567 mm/yr is precipitation and 1,620 mm/yr is irrigation, as calculated in topic 41. Therefore, the final calculated value is 2,426 mm/yr-1. The Erosivity Factor R was calculated at 4371.14 MJ mm ha-1 h-1 yr-1. k = Erodibility factor [t h MJ-1 mm-1], 0.0026. LS = Slope factor [dimensionless], 100. Calculated by Müller Carneiro et al. (2019). c1 = Cultivation factor [dimensionless], 0.1. c2 = Tillage factor [dimensionless], 0.25. P = Practice factor [dimensionless], 1. Using the data obtained, the amount of eroded soil (Ser) was calculated at 28,410 kg/(ha.a). Pcs = P content in the topsoil [kg P/kg soil]. The average value of 0.00095 kg/kg was used. Fr = P enrichment factor (-). The average value of 1.86 was used, which takes into account the fact that eroded soil particles contain more P than the average soil. Ferw = fraction of eroded soil that reaches the river [dimensionless]. The average value of 0.2 was used.

Elementary flow

Elementary flows / Emission to water / surface water

Water, BR

3.16E-4 m3

General comment

Some of the water used for orchard irrigation is emitted to surface water due to percolation. The formula described in Nemecek et al. (2019) was used to calculate the emitted quantity: 〖Emission〗_(supply water)=0.8*((〖ET〗_irr/〖EF〗_irr ) - 〖ET〗_irr ) Where: Esupply water = water emitted to groundwater [m3/t of acerola]. ETirr = Irrigation evapotranspiration [m3/t of acerola], being 197.64 m3 (calculated in topic 40) divided by 167 t of acerola produced. The total value is 1.1835 m3/t. EFirr = Irrigation efficiency factor [dimensionless], which is a tabulated value for the type of irrigation used. According to Calgaro and Braga (2012), the micro-sprinkler irrigation method is the most common for the acerola tree, which has an efficiency factor of 0.75, as recovered in Nemecek et al. (2019).

Elementary flow

Elementary flows / water / surface water

Cadmium, ion

1.45E-10 kg

General comment

The data and calculation formula for heavy metal emissions to surface waters due to erosion, described in Nemecek et al. (2019), were used: M_(erosion i) = c_(tot i) * S_er * a * f_erosion * A_i Where: M erosion i = total emission of heavy metal i due to erosion [mg]; C tot i = Total heavy metal content in the soil [mg.kg-1], where the values for arable land are Cd = 0.24 mg/kg, Cu = 20.1, Zn = 49.6, Pb = 19.5, Ni = 23, Cr = 24.1, and Hg = 0.073. Ser = Quantity of eroded soil [kg.(ha.year)-1], with 28,410 kg/(ha.year) calculated previously (topic 45). A = Accumulation factor, equal to 1.86. F = Erosion fraction of eroded soil that reaches the river, equal to 0.2. Ai = Allocation factor for the share of agricultural inputs in the total inputs for heavy metals. As calculated previously (topic 34), the allocation factor Ai for each heavy metal is: Cd = 0.856, Cu = 0.955, Zn = 0.741, Pb = 0.724, Ni = 0.711, Cr = 0.896, and Hg = 0.894.

Elementary flow

Elementary flows / water / surface water

Chromium, ion

1.63E-6 kg

General comment

Elementary flow

Elementary flows / water / surface water

Copper, ion

1.28E-6 kg

General comment

Elementary flow

Elementary flows / water / surface water

Lead

1.13E-6 kg

General comment

Elementary flow

Elementary flows / water / surface water

Mercury

5.16E-10 kg

General comment

Elementary flow

Elementary flows / water / surface water

Nickel, ion

1.26E-6 kg

General comment

Elementary flow

Elementary flows / water / surface water

Zinc, ion

2.33E-6 kg

General comment

Waste flow

E:Water supply; sewerage, waste management and remediation activities / 38:Waste collection, treatment and disposal activities; materials recovery / 382:Waste treatment and disposal / 3821:Treatment and disposal of non-hazardous waste

waste wood, untreated

0.096 kg

General comment

The density of cherry wood (600 kg/m3) was considered in the calculation (Carvalho, 2007), since no studies indicating the density of acerola wood were found. To this end, the volume of wood of a tree, in m3, was calculated, considering the trunk and branches as a cylinder. It was estimated that the trunk radius is 6 cm and its height, as defined in Calgaro and Braga (2012), is 2 to 3 meters. The branch measurements were estimated at 2 cm (radius) and 1.2 meters (length). Thus, the estimated weight of the wood of an acerola tree was 23.94 kg.

Elementary flow

Elementary flows / Emission to soil / agricultural

Calcium

4.58E-4 kg

General comment