Variability of Greenhouse Gas Footprints of Field Tomatoes Grown for

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Variability of greenhouse gas footprints of field tomatoes grown for processing: inter-year and inter-country assessment Wan Yee Lam, Rosalie van Zelm, Ana Benítez-López, Michal Kulak, Sarah Sim, John M. Henry King, and Mark A.J. Huijbregts Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04361 • Publication Date (Web): 01 Dec 2017 Downloaded from http://pubs.acs.org on December 5, 2017

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Environmental Science & Technology

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Variability of greenhouse gas footprints of field tomatoes grown for processing:

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inter-year and inter-country assessment

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Wan Yee Lam1*, Rosalie van Zelm1, Ana Benítez-López1, Michal Kulak2, Sarah Sim2, J. M. Henry King2,

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Mark A. J. Huijbregts1

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1

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P.O. Box 9010, 6500 GL Nijmegen, The Netherlands

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brook, Bedfordshire MK44 1LQ, United Kingdom

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*

Department of Environmental Science, Institute for Water and Wetland Research, Radboud University,

Unilever Safety and Environmental Assurance Centre, Unilever R&D, Colworth Science Park, Sharn-

Corresponding author

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ABSTRACT: Our study provides an integrated analysis of the variability of greenhouse gas (GHG) footprints of

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field grown tomatoes for processing. The global farm-specific dataset of 890 observations across 14 countries over

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a three-year period (2013-2015) was obtained from farms grown under Unilever’s sustainable agricultural code. It

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represents on average 3% of the annual global production of processing tomatoes: insights can be used to help

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inform corporate sourcing strategies and certification schemes. The median GHG footprint ranged from 18 in Chile

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to 61 kg CO2-eq. per tonne of tomatoes in India; lower than results reported in other studies. We found that foot-

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prints are more consistent within countries than between them. Using linear mixed effect models, we quantified the

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relative influence of environmental conditions and farm management factors. Key variables were area of production

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and the method of fertilizer application. GHG footprints decreased with increasing area of production to a threshold

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of 17.4 ha. Farms using single fertilizer application methods in general had a larger GHG footprint than those using

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a combination of methods. We conclude that farm management factors should be prioritized for future data collec-

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tion and more stringent guidance on acceptable practices is required if greater comparability of outcomes is needed

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either within a scheme, such as the Unilever’s sustainable agriculture code or between schemes.

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INTRODUCTION

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Greenhouse gas (GHG) calculators and footprinting can be used by companies to inform management strategies

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within agricultural supply chains1. The impacts of the agricultural phase in the life cycle of bio-based products are

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highly variable and subject to many influencing factors, especially in open-field cultivation systems2-5. Variability

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in GHG footprints (kg CO2-eq per tonne) of crop production is directly related to variation in factors such as,

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fertilizer use, machine use, irrigation and yield2, 3, 5. These sources of variability are interrelated and influenced by

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environmentally related factors, such as climate, soil properties and elevation as well as other farm related factors

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such as area of production and fertilizer application methods6, 7.

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Previous life cycle assessment (LCA) studies investigating variability of agricultural production focused on factors

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directly used in GHG footprint calculations as the major contributors to variability in GHG footprints2, 3, 5, 8, 9. For

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field tomato production, Clavreul et al2 conducted intra- and inter-year variability analysis of GHG footprints of

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cultivation using data from 189 farms from 2012-2015 in the Extremadura region in Spain and Portugal. The GHG

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footprints, ranging from 29 to 89 kg CO2-eq per tonne of tomatoes, were found to be most sensitive to the variability

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in yield, followed by farm practices such as the extent of pump irrigation and choice and amount of fertilizer used.

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Pishgar-Komleh3 quantified variability of GHG footprints of field tomato production using data from 204 farms in

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Iran and obtained GHG footprints ranging from 100 to 400 kg CO2-eq per tonne of tomatoes. They also found the

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variability of GHG footprints to be mainly driven by the variability in yield, followed by fertilizer application.

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While Clavreul et al2 attributed the importance of inter-year variability in GHG footprints to variability in weather

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conditions, there was no formal quantification of the relationship between climatic and soil conditions and the GHG

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footprint of the tomatoes.

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Linear mixed models (LMM), also known as linear mixed-effects models, are able to incorporate a wide variety of

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correlation patterns in their random effects structure and this flexibility provides accurate estimates of the fixed

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effects in the presence of correlated errors due to data hierarchy and repeated measurements 10, 11. This approach is

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particularly suitable for datasets that have several observations nested within each country, and repeated measure-

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ments from farms in different years; and offers a systematic approach to analyze the importance of other environ-

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mentally and farm related factors, such as soil, climate, and fertilizer application method, in contributing to the

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variability in GHG footprints of crop production. Analyzing the variability of the GHG footprints at the farm level ACS Paragon Plus Environment

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and understanding the drivers of the variability is necessary to identify possible areas of GHG reduction and to

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enable more targeted GHG mitigation strategies within agricultural supply chains.

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The goal of this study was to: 1) assess the variability (between farms, countries and years) of the GHG footprint

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of commercially grown field tomatoes globally and 2) understand the relationship between GHG footprints of com-

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mercially grown field tomatoes and environmental factors, such as climate and soil characteristics, and farm related

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factors, such as production area and fertilization method. The dataset represents farms that were compliant with the

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Unilever’s Sustainable Agriculture Code (SAC) and covers 14 countries that represent approximately 80% of global

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production of field-grown tomatoes and includes the top five producing countries namely, the United States of

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America (USA), China, Italy, Spain and Turkey12. Firstly, we assessed the variability in GHG footprints and quan-

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tified, with a partial correlation analysis, the relative importance of factors that are used in standard GHG footprint

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calculations of tomato production, namely (i) tomato yield, (ii) emissions from fertilizer production and field ap-

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plication and (iii) emissions from energy use. Secondly, we used LMM to quantify the relative influence of envi-

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ronmental conditions and farm management factors on the variability of GHG footprints for global tomato produc-

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tion. The first analysis was conducted using field tomato production data from 890 farm-specific observations

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across 14 countries and measured over three years (i.e. 2013 -2015)13. The second analysis was based on a subset

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of 719 observations with unique geolocations and complete description of farm management factors.

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MATERIALS AND METHODS

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GHG footprint. Following the system boundaries illustrated in Figure 1, the GHG footprint of field-grown tomato

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production on a specific farm x in a specific year j (GHGtomato in kg CO2-eq tonne-1 tomato produced) including

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GHG emissions from energy use by machinery and irrigation (GHGenergy in kg CO2-eq ha-1), GHG emissions from

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fertilizer production and field nitrous oxide emissions from application of nitrogen fertilizers and crop residues

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(GHGfertilizer in kg CO2-eq ha-1) per unit of tomato produced (Yield in tonne ha-1) was defined as (Equation 1):

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𝐺𝐻𝐺𝑡𝑜𝑚𝑎𝑡𝑜,𝑥,𝑗 =

GHG𝑒𝑛𝑒𝑟𝑔𝑦,𝑥,𝑗 + 𝐺𝐻𝐺𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟,𝑥,𝑗 Yield𝑥,𝑗

(1)

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Data for GHG footprint calculations (Table S1) were collected from the Cool Farm Tool14, but footprints were

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calculated outside of the data collection software14. Amount of energy and fertilizer consumption were provided in

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the data collection sheet as aggregate values by their respective types (MJ of electricity, diesel and petrol for energy

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consumption and kg of different types of fertilizers for fertilizer consumption), without further breakdown of the

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consumption in each agricultural process illustrated in Figure 1. As specific land use history for the farms was not

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available in the extracted dataset, biogenic GHG emissions from land use change were considered following the

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approach originating from Mila I Canals15 and recommended by Nemecek et al16. Upon analysis of the historical

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land cover data (FAOSTAT17 over the past 20 years), no land use change arising from tomato production was found

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in the sample of countries considered. Thus, emissions from land use change were considered to be zero in this

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analysis16. GHG emissions from pesticide production were excluded as many farms did not provide sufficient in-

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formation on type and amount of pesticide use. This is likely to have limited impact in the calculation of GHG

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footprints, as previous studies have indicated pesticide production and application represents less than 5% of the

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GHG footprint of field grown tomatoes2, 8. GHG emissions from capital goods production were also omitted as

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their contribution to the GHG footprint of agricultural products is typically low18. Temporary carbon sequestration

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by tomato plants was also not taken into account due to regular harvest of the tomato crop compared to perennial

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crops, as well as the short-lived nature of the tomato-based products8. Carbon dioxide (CO2), methane (CH4) and

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nitrous oxide (N2O) emissions were summed using Global Warming Potentials (GWP) of 1, 30 and 265 CO2-

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equivalents, respectively, representative of a 100-year time horizon19. The GHG emission factors, expressed as kg

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CO2-eq per unit of material or energy consumed, were derived from secondary sources, mainly ecoinvent version ACS Paragon Plus Environment

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3.220 (Table S5). See Section S2 of Supplementary Information (SI) for more details about the overall GHG calcu-

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lation methodology.

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Figure 1. System boundaries for greenhouse gas footprinting from cradle to farm gate (solid lines). Emissions from pesticides production and capital goods production were excluded (dotted lines).

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We calculated annual GHG footprints of 890 farm-specific observations from 14 countries: Australia, Chile, China,

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Egypt, Greece, India, Israel, Italy, New Zealand, Poland, Portugal, Spain, Turkey, and the USA for the three-year

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period 2013-2015. The summed production volume of the farms relative to the annual global tomato production in

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each year was about 3%12. The farms in this study applied conventional farming methods (not organic) and were

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sampled randomly for self-assessment according to the scheme rules21 from farms operating in compliance with

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Unilever’s Sustainable Agriculture Code (SAC)13. In certain countries, farms were concentrated in specific regions,

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these included: California in the USA, Extremadura region in Spain, and Xinjiang region in China. Farm data were

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collected via spreadsheets from the Cool Farm Tool14 in an unaudited format and they were processed and cleaned

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prior to analysis (Section S1 of SI). A data quality score for each observation, ranging from 1 to 12, with lower

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scores representing more unique and complete information, was applied in accordance to the criteria as specified

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in Section S3 of SI. Only farms that have unique and complete information for area, yield, fertilizer and energy


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consumption. were given data quality scores less than or equal to 7, and were included in the cleaned dataset of 890

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observations (Section S3 of SI).

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Variability and correlation analysis. A dataset of 890 farm observations was used for the analysis of variability

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of GHG footprints, as well as for the relative contribution to variability of the factors used in the GHG footprint

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calculations; i.e. GHGenergy, GHGfertilizer and Yield. Variability in GHG footprints was quantified using the inter-

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quartile range and coefficient of variation at four spatial and temporal scales namely, 1) within each country in each

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year, 2) within each country in all years, 3) within all countries in each year, 4) within all countries in all years

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(overall dataset). Only countries with data for at least 10 farms per year were included in the variability analysis at

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level 1; and only countries with data for at least 10 farms over the three years were included in the variability

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analysis at level 2 (See S1 of SI for number of farm observations in the 14 countries by year). This helps to ensure

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that the analysis at the country level is based on a sufficient number of observations within a single country. At

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level 3 and 4, the full dataset across all 14 countries was used as appropriate. We also quantified the variability of

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GHG footprints 5) between each year in each country and 6) between each country covering all years. We then

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calculated the Spearman’s rank and partial correlation coefficient between the GHG footprint and Yield, GHGfertilizer

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and GHGenergy at the first four spatial and temporal levels (ppcor package16, R 3.3.1 software17). The rank correlation

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provided an indication of the relative influence of each factor on the variability of the GHG footprints while con-

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sidering the interaction between them.

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Linear mixed model. We used LMM to assess variations in GHG footprints as a function of a set of environmental

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conditions and farm management factors at the global level. In this analysis, we only included observations with

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unique geocoded locations and for which data on farm management factors were available (N = 719, See S3 of SI).

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For each observation we obtained information from the Cool Farm Tool14 on two farm management factors, namely

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the area of production and fertilizer application method (Table 1). Farmers employ a variety of single and multiple

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fertilizer application methods, and thus we classified farms accordingly into single fertilizer application methods,

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i.e. ‘incorporation’, ‘apply in solution’, ‘subsurface drip’, and ‘broadcast’, and multiple methods, i.e. ‘incorporate-

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subsurface drip’, ‘incorporate-broadcast’, ‘broadcast-apply in solution’, ‘incorporate-apply in solution’, and ‘com-


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bination of 3 unique methods’ (9 types of fertilizer application methods in total). In addition, farm-specific envi-

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ronmental characteristics for climate conditions and soil type were obtained from spatially-explicit maps using

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ArcGIS22 (See Table 1 for the spatial resolution) (See S5 for detailed procedure of farm geolocation and spatial

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data extraction). Climate parameters were obtained for the growing season of open-field tomatoes in each country,

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e.g. mid-April to mid-October for Spain (See Section S4 of SI for the country-specific growing seasons). Soil

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parameters were obtained for the topsoil fraction (top 30 cm of soil) in which the roots of field tomatoes are typically

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concentrated 23, 24 and where soil parameters influence growth of tomatoes 25, 26. We chose extreme climate param-

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eters to capture extreme events, such as droughts which could have a large impact on GHG footprints due to in-

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creased irrigation demands.

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Table 1. Fixed effect variables for model-building Fixed effect variables Climate conditions during growing season

Grid size

Source

50km x 50km

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5km x 5km

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Minimum daily temperature (oC) Maximum daily temperature (oC) Monthly rainfall driest month (mm month-1) Monthly rainfall wettest month (mm month-1) Rain day frequency driest month (days month-1) Maximum monthly potential evapotranspiration (mm day-1) Maximum monthly cloud cover (%)

Soil properties of topsoil fraction Organic carbon (percentage by weight) Clay content (percentage by weight) Soil bulk density (kg dm-3) Cationic exchange capacity of clay fraction in topsoil (cmolc kg-1) Soil pH (-log(H+))


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Mean elevation

Farm management factors

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1km x 1km

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Farm-specific

Primary dataset

Area of production (hectares) Fertilizer application method (9 types)

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In our linear mixed model (Equation 2), 𝑌𝑖 is the response variable (GHG footprint), 𝑋𝑖𝑗 and 𝛽𝑗 represent the fixed

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effect variables (environmental conditions and farm management factors in Table 1) and their coefficients,

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𝑍𝑖𝑘 and 𝑏𝑖𝑘 represent the random effect variables and their coefficients, and 𝜀𝑖 is the error term. i, j and k represent

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each farm data point, each fixed effect variable and each random effect variable, respectively. To account for vari-

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ability between the countries, years and farms that may be influenced by factors other than the fixed variables, we

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included Country, Year and FarmID as random variables. We chose a random intercept structure of (1|Country/Far-

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mID)+(1|Year) (denoted in lme430 syntax) as farms were nested within countries and both farms and countries were

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represented by three years of data.

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𝑌𝑖 = 𝑋𝑖𝑗 𝛽𝑗 + 𝑍𝑖𝑘 𝑏𝑖𝑘 + 𝜀𝑖

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Prior to model building, we assessed the normality of the response variables and the homogeneity of response

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variables across the explanatory variables31. Both the GHG footprint and area of production were log10-transformed

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to correct for skewness and area was included as a quadratic term. We assessed multicollinearity between explan-

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atory variables using variance inflation factors (VIF)32. All VIFs were lower than 10, so all variables were retained

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for model selection. Each explanatory variable was standardized before model fitting. Models were fitted using all

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possible variable combinations with the packages MuMIn33 and lme430, and ranked according to the corrected

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Akaike’s Information Criterion (AICc)34. We selected models with delta AICc less than 2 and conducted model

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averaging based on Akaike weights35, 36. The goodness of fit of the averaged model was assessed using the weighted

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marginal and conditional R2 37, which represent the respective explained variance by the fixed effect variables and

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the full model (fixed + random effect variables). The explained variance by the random effect variables was

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obtained by subtracting marginal R2 from the conditional R2. We attributed a fraction of the marginal R2 to each

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fixed effect variable in proportion to its sum of squares obtained from the analyis of variance (ANOVA) of the

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model. Likewise, the explained variance by each random variable was attributed in proportion to its variance ACS Paragon Plus Environment

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obtained from the summary of the model. The relationships of GHG footprints with fixed effect variables found to

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be important at the global level were further examined at the country level using Spearman’s rank correlation co-

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efficient and variability analysis for continuous and categorical fixed effect variables, respectively. As in the case

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for variability analysis, only countries with data for at least 10 farms were included for the analysis at the country

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level.

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RESULTS

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GHG footprints and variability analysis. The annual mean GHG footprints of tomatoes weighted by production

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volume within all 14 countries in the years 2013, 2014 and 2015 (level 3) are 63, 50 and 47 kg CO2-eq per tonne

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of tomatoes, respectively. This represents on average a 25% decrease in GHG footprints within the sampled dataset

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over the three years. The GHG footprint shows large variability between countries and farms, and a slightly lower

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variability between years (Figure 2 and Section S6 of SI). The median GHG footprint within each country in all

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years (level 2) ranges from 18 to 61 kg CO2-eq. per tonne of tomatoes, with Chile having the smallest median and

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India the largest median GHG footprint. The weighted mean GHG footprint within each country in all years (level

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2) ranges from 19 to 59 kg CO2-eq. per tonne of tomatoes, with Chile showing the smallest and China the largest

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weighted mean GHG footprint. The coefficient of variation of GHG footprints within each country in all years

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(level 2), ranges from 33% in Portugal to 159% in USA; this is larger than the coefficient of variation of GHG

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footprints between each year in each country (level 5), that ranges from 8% in Greece to 50% in USA. See Section

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S7 of SI for variability diagrams of yield, fertilizer and energy consumption.

2013

GHG footprints (kg CO2 eq per tonne tomato)

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2015

100 80 60 40 20 0

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2014

Overall Australia Chile (n=890) Countries (n=24) (n=100)

China (n=212)

Greece (n=158)

India Italy Portugal Spain USA (n=106) (n=41) (n=41) (n=122) (n=67)

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Figure 2. GHG footprints shown for each country per year (level 1) and overall by year 2013, 2014 and 2015 (level

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3). n refers to the number of observations for each country. Only 9 countries (i.e. Australia, Chile, China, Greece,

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India, Italy, Portugal, Spain and USA) have data for at least 10 farm-year combinations in at least one year.

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Amongst them, only China, Greece, Spain and USA have data for at least 10 farms in each year from 2013 to 2015.

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The variability diagrams show the 10th percentile, first quartile, median, third quartile and 90th percentile. Farm-

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year combinations outside of this range are not presented. ACS Paragon Plus Environment

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Correlation analysis. Figure 3 shows that variability in GHGenergy, GHGfertilizer and Yield contribute fairly equally

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to the variability in GHG footprints within all 14 countries in each year (level 3) and within all 14 countries in all

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years (level 4). This is different when looking at the variability within each country in all years (level 2) (Figure

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3a). In this case, only 10 countries (i.e. Australia, Chile, China, Egypt, Greece, India, Italy, Portugal, Spain and

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USA) have data for at least 10 farms over the three years of the dataset and were included in the analysis. In Chile

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and the USA, variability in GHGenergy contributes most to the variability in the GHG footprint. Yield, on the other

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hand, is an important driver of variability in the GHG footprints in India, Australia and, to a lesser extent, Italy.

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Between the different years, most countries have only 2 years’ worth of data with at least 10 farms in each year for

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comparison. Only China, Greece, Spain and USA have data for at least 10 farms in each year from 2013 to 2015.

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The relative contribution of the different sources of variability remains relatively consistent over the years for farms

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within each country and for farms within all countries, i.e. they remain in the same region of the tri-plots. Exceptions

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occur for China in 2013 and USA in 2015. China moves from the higher contribution of yield and energy in 2013

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to the middle region in 2015. USA moves from the middle region in 2013 to the higher contribution of yield and

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energy in 2015. The median GHG footprints of field tomato production in China in 2013 and USA in 2015 are also

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notably higher than those in the same country in other years (Figure 2).

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a) All years

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b) 2013

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iv GHGenergy

GHGfertilizer

iv

GHGenergy

GHGfertilizer

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ii

i

ii

iii

i

iii

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Yield

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Yield

c) 2014

d) 2015

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iv

iv

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GHGenergy

GHGfertilizer

GHGenergy

GHGfertilizer

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ii

i

iii

ii

i

iii

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Yield

Yield

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Figure 3. Relative contribution of sources of variability to variance of GHG footprint by country for a) all years

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b) 2013, c) 2014 and d) 2015. Figure 3a covers the analysis within each country in all years (level 2) and within all

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countries in all years (level 4) while Figure 3b, 3c and 3d cover the analysis within each country in each year (level

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1) and within all countries in each year (level 3). The regions in the tri-plots correspond to the following situations:

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i, all three factors are relatively equally contributing; ii, Yield is the most contributing; iii, GHGfertilizer is the most

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contributing; iv, GHGenergy is the most contributing. The abbreviations are: Overall, All 14 countries in each year

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or all years; AU, Australia; CL, Chile; CN, China; EG, Egypt; ES, Spain; GR, Greece; IN, India; IT, Italy; PT,

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Portugal; USA, United States of America. Only 10 countries (i.e. Australia, Chile, China, Egypt, Greece, India,

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Italy, Portugal, Spain and USA) have data for at least 10 farm-year combinations over the three years of the

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dataset. Amongst them, only China, Greece, Spain and USA have data for at last 10 farms in each year from 2013

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to 2015.


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Linear mixed model. The marginal and conditional R2 of the best averaged model are 29.6% and 64.2% respec-

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tively and includes area of production (13.8% explained variance), method of fertilizer application (10%), rain day

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frequency driest month (3%) and minimum temperature (2.9%) as fixed effects variables. At the global level, the

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relationship between GHG footprints and area of production is non-linear, with GHG footprints decreasing with

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increasing area of production up to a threshold of 17.4 ha, and then increasing for farms with larger areas of pro-

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duction (Figure 4). Among the fertilizer application methods those farms using single fertilizer application methods

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in general have a larger GHG footprint than those using a combination of methods (Figure 5). Amongst the nine

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fertilizer application methods, ‘apply in solution’ is associated with the largest GHG footprints, while the combi-

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nation ‘incorporate-apply in solution’ results in the lowest GHG footprints. Fertilizer application methods other

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than these two extremes appear to be similar in their fitted values when considering the 90% confidence interval.

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See S9 of SI for country-specific variability of GHG footprints with area of production (Spearman’s correlation

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coefficient) and method of fertilizer application (median GHG footprints for each method of fertilizer application).

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Regarding the random effect variables, these explain 34.6% of the total variance of GHG footprints, with ‘country’

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being the most important (33.8% explained variance), followed by ‘farm’ (0.5%) and then ‘year’ (0.3%). 35.8% of

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the variability of GHG footprints remains unexplained by the model.


500

50

5 0.2

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2 Australia India Portugal 90% CI

20 200 Farm area (hectare) Chile China Italy New Zealand Spain Turkey Fitted

2000 Greece Poland USA

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Figure 4. Modeled relationship between GHG footprints and Area of production for the global dataset of 719

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observations in logarithmic scale on both axes, holding other factors constant at their median values. The bold

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line represents the fitted value and the grey dashed lines represent the 90% confidence interval. The data points

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represent the 719 farm-year combinations used for model-building. ACS Paragon Plus Environment

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Environmental Science & Technology 65 60 55 50 45 40 35 30 25 20 15

Single

Double

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Triple

Fertilizer application method

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Figure 5. Modeled relationship between GHG footprints and fertilizer application method at the global level,

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holding other factors constant at their median values. The squares represent the fitted values and the lines with

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caps represent the 90% confidence interval. Farms that used a single, double and triple number of fertilizer

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application methods are grouped as such; N refers to the number of observations for each fertilizer application

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method.

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DISCUSSION

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This study is, according to the authors’ knowledge, the first to provide an integrated analysis of the variability of

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GHG footprints of global production of a crop, namely commercial field grown tomatoes for processing. In addition

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to quantifying the relative importance of GHGenergy, GHGfertilizer and Yield on the variability of GHG footprints of

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field tomato production, we further explored the relationships of GHG footprints with additional farm and environ-

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mental factors not normally considered in the GHG footprint calculations, using linear mixed effect models. In the

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sections below we highlight the implications for GHG footprint reduction strategies within the field tomato supply

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chains.

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GHG footprints of field tomato production. The weighted mean GHG footprints of field tomato production in

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this study range from 19 to 59 kg CO2-eq. per tonne of tomatoes in Chile and China respectively. This is comparable

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to the values ranging from 29 to 89 kg CO2-eq per tonne of tomatoes reported previously for Spain and Portugal in

284

2012-20152 using the same data platform14. Compared to the range of GHG footprints of field tomatoes reported in

285

other literature, i.e. 100-400 kg CO2-eq per tonne in Iran in 2014-20153, 82 and 130 kg CO2-eq per tonne in Italy

286

in 200735 and 20118 respectively, the GHG footprints in our study and that of Clavreul et al2 are lower. The com-

287

paratively large footprints of Pishgar-Komleh3 could be explained by the fact that natural land was used as a refer-

288

ence state and carbon dioxide emissions from the conversion of natural land to agriculture were included3. The

289

differences may also be explained by the fact that the sample of farms in this and Clavreul’s study2 complied with

290

the Unilever Sustainable Agriculture Code (SAC)13. The SAC requires improvements in GHG management prac-

291

tices and the findings may be indicative of improved performance. Indeed, the 25% decrease in the annual weighted

292

mean GHG footprint over the period 2013 to 2015 is also suggestive of the potential effectiveness of the SAC in

293

reducing the GHG footprint of tomato production through education and enforcement of sustainable practices

294

within supply chains13. The efficacy of sustainability codes and certification schemes is often neglected due to their

295

principal focus on management practices rather than performance38. However, due to crop rotation and supply

296

chain sourcing practices it is not possible to ascribe the reduction in GHG footprint compared to non-certified

297

sources solely to compliance with the SAC.

298

Another reason for the differences in GHG footprints compared to earlier studies may be from the use of different

299

emission factors (e.g. emission factors from earlier versions of ecoinvent (v239, v2.28 and v3.13) instead of v3.240) ACS Paragon Plus Environment

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and changes in the IPCC recommended GWP values for GHG (e.g. GWP values of N2O of 298 CO2-equivalents

301

from IPCC 2007, 296 CO2-equivalents from CML2001 and 265 CO2-equivalents from IPCC 201319). Indeed, the

302

use of higher GWPs (296 CO2-equivalents for the GWP of N2O for a 100-year time horizon) by Clavreul et al2 from

303

within the Cool Farm Tool14 resulted in slightly higher GHG footprints than those from the same countries in our

304

study, i.e. Portugal and Spain (38 to 41 in our study vs 53 kg CO2-eq. per tonne of tomatoes in Clavreul et al.2).

305

However, the dataset for Spain and Portugal from Clavreul et al.2 also included an additional year (i.e. 2012) com-

306

pared to our study. In the study by Pishgar-Komleh et al.3, the use of higher GWP of N2O, coupled with the ex-

307

ceedingly high level of nitrogen input (up to 3000 kg N ha-1) could explain the higher GHG footprints in Iran when

308

compared to our study (range: 5 to 623 kg N ha-1, median: 210 kg N ha-1) and Clavreul et al.2 (range: 66 to 505 kg

309

N ha-1, median: 188 kg N ha-1). Lower yield (71 t ha-1 to 74 t ha-1) in the studies by Manfredi et al.8 and Theurl et

310

al.35, on the other hand, could explain the higher GHG footprints than those in our study (range of yield: 49 to 132

311

t ha-1, median=85 t ha-1) and Clavreul et al. (29-148 t ha-1, median= 86 t ha-1) despite the lower nitrogen inputs (130

312

to 143 kg N ha-1) in that study.

313

Variability of GHG footprints between farms, years and countries. The wide variability of GHG footprints

314

within our study shows the potential for further reduction in the GHG impact of tomato production. In the correla-

315

tion analysis, we found that the relative contribution to variability of GHG footprints by each factor, i.e. GHGenergy,

316

GHGfertilizer and Yield, is different between the different countries. On the other hand, it is consistent within the

317

same countries and years except for USA in 2015 and China in 2013, where the contribution by GHGenergy and

318

Yield, respectively are the most important. This suggests that footprints are more consistent within countries than

319

between them. One reason could be that tomato production tends to be concentrated in certain regions in countries

320

that make up a large proportion of the dataset, they include: Xinjiang in China, California in USA and Extremadura

321

region in Spain and Portugal. The farms within the same countries are hence likely to experience similar climate

322

and soil conditions as well as more similar farm management practices. The fact that farms in Australia, India and

323

to a smaller extent Italy can be found within the yield side of the tri-plot, (i.e. Region ii in Figure 3a) suggests that

324

not all farms are operating at the most efficient levels. Farms in India have the lowest median yields (See Section

325

S7 of SI) despite levels of fertilizer application and energy consumption that are comparable to, or higher than those

326

in other countries (See Section S7 of SI). Farms in Australia, on the other hand, have the largest median yield.

327

However, the large variability in yields (See Section S7 of SI) within the country implies that some farms had much ACS Paragon Plus Environment

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lower yields compared to their counterparts. It is therefore important to look into the reasons for the low yields so

329

as to improve the production as well as environmental performance of these farms.

330

The shape of the curve describing the relationship between farm area and GHG footprint suggests that production

331

area may need to reach a certain minimum size before achieving economies of scale41 through the use of machinery

332

and irrigation practices and through more effective ‘learning-by-doing’42. Most countries display negative relation-

333

ships between GHG footprints and the area of production (S9 of SI). India, with the lowest median area of produc-

334

tion (0.6 ha) amongst the different countries, is also the country with the lowest median yields and highest median

335

GHG footprints (S7 of SI). Only USA has large positive relationships between area of production and both GHG

336

footprint and GHGenergy, suggesting that exceedingly large farms (median area of production = 150 ha) may not

337

always be performing optimally. Nevertheless, compared to other countries, USA has one of the lowest median

338

GHG footprints (S7 of SI).

339

In the linear mixed model, farms that adopted ‘apply in solution’ as their fertilizer application method may have

340

the highest GHG footprints due to large wastage from fertilizer flows that did not get retained in the root system13,

341

i.e. higher fertilizer dose is needed to achieve the same uptake by roots and yield leading to larger GHG footprints.

342

The combination ‘incorporate-apply in solution’, however, results in the lowest GHG footprints. This may be due

343

to the synergistic effect such that the initial incorporation of solid fertilizers help build up a strong root system that

344

could better absorb the liquid fertilizers from the application of fertilizers in solution 43. In Australia, farms using

345

‘incorporate-subsurface drip’ as method of fertilizer application have higher yields, lower GHGenergy and lower

346

GHG footprints than farms that use ‘subsurface drip’ (S9 of SI). This explains the large variability of yields within

347

the country, and suggests that a shift from purely liquid based methods to combination of solid and liquid-based

348

methods may help to increase yields and lower GHG footprints.

349

The GHG contribution from energy was most important to the variability in the GHG footprints of production in

350

USA in 2015. The state of California, where the majority of USA farms were located, experienced its fourth con-

351

secutive dry year in 2015, with more than 60% of the land experiencing exceptional drought 44. We noticed a shift

352

in the dataset from ‘subsurface drip’ in 2013 to ‘broadcast’ and ‘apply in solution’ in 2015 as the most commonly

353

used methods of fertilizer application in USA. This could indicate that in the face of drought, farmers switched to

354

more water intensive irrigation methods45 to reduce water deficit of the crop. The result was larger GHG median ACS Paragon Plus Environment

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355

footprints since farms consumed more energy to operate irrigation pumps46. Overall, the farms in the USA were

356

successful in responding to changing weather conditions as the variability in yields in 2015 remained similar to

357

previous years (See S7 of SI).

358

Farms in China, however, were less successful in responding to drought conditions, which were the strongest in

359

2013 in Northern Xinjiang40, 41, where the majority of the tomato farms in China were located47, 48. High variability

360

in yield in 2013 (most important contributing factor for the variability of GHG footprints in China in 2013) occurred

361

despite higher energy consumption (S7 of SI), suggesting that such interventions may not always have produced

362

higher yields in times of drought. Indeed, we saw a shift in the most commonly used methods of fertilizer applica-

363

tion among the sample farms from ‘broadcast-apply in solution’ in 2013 to ‘incorporate-apply in solution’ and

364

‘incorporate-broadcast’ in 2014 and 2015 (See S9 of SI), with the earlier method associated with higher volume of

365

water use45. Further examination of factors influencing the differences between China in 2013 and USA in 2015

366

may provide guidance for future drought intervention practices.

367

Inherent differences between countries (‘country’ as a random effect variable) that are not captured by the fixed

368

effect variables explained 33.8% of variability in GHG footprints in the global dataset. This could suggest a diver-

369

gence of GHG footprints between countries due to their unique political and economic situations, e.g. country-

370

specific fertilizer policies 49-52, legislative limits 49, 51-55, subsidies or taxes. Moreover, farmers from the same country

371

may learn more easily from each other

372

persist unless changes are made at the country-level through policy improvements and technological transfers. Dif-

373

ferences between farms and years are less pronounced, and may suggest that most of these differences have been

374

captured by the fixed effect variables.

375

Implications for sustainable sourcing. Corporate and governmental policies for sustainable sourcing often pro-

376

mote certain sets of management practices without quantification of their actual impacts 57. In Unilever, the Sus-

377

tainable Agriculture Code (SAC) provides a mechanism for monitoring quantitative farm-level data over time13.

378

The findings of this study provide some evidence of a reduction in GHG emissions over time but repeated sampling

379

of the same farms over an extended number of years is required to fully understand the benefits of the scheme. The

380

large variability of GHG footprints within this study for sustainably sourced tomatoes partially reflects the range

381

of management practices that are acceptable in the Unilever SAC. If greater comparability of outcomes is required,

56

leading to a convergence of practice. Such differences are expected to


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either within a scheme such as the Unilever SAC, or between schemes then more stringent guidance on acceptable

383

practices would be required. However, highly prescriptive approaches to certification could hinder adoption by

384

farmers and reduce the push for continuous improvement across the sector.

385

Implications for development of data collection platforms and GHG calculators. As the energy and fuel con-

386

sumption were reported as single figures, the impact of specific management practices, e.g. irrigation, harvesting,

387

tilling, etc. could not be quantified. Moreover, information for factors that have significant influence on variability

388

in GHG footprints58, such as genetic resources (varieties), farmer’s knowledge and habits (e.g. on fertilizer appli-

389

cation, planting dates, pest and disease control), were not available. We also relied on village or city names for

390

geolocation of farms, leading to uncertainty in the extraction of spatial-temporal parameters. Data collection plat-

391

forms, such as the Cool Farm Tool14, could seek to facilitate the gathering of this information. This would improve

392

the identification of drivers of GHG variability and allow development of more specific GHG management strate-

393

gies. Indeed, in the latest online version of the CFT 14, the user is able to input information regarding the energy

394

consumed for each type of agricultural process, including machine usage and irrigation. However, there is a trade-

395

off between obtaining more data for detailed analysis and increasing the burden on farmers for further data collec-

396

tion and reporting57. Based on this study, we suggest prioritizing data collection related to types and quantities of

397

farm management practices rather than aggregate energy consumption. The data collected should include the types

398

and number of passes for soil preparation activities or the types of irrigation and the amount of water use.

399 400

Data quality issues related to the CFT were discussed by Keller (2016)57 and Clavreul et al (2017)2. However, there

401

was no methodological guideline regarding how to assess the quality of the data before this analysis. The

402

methodology developed in this study, specifically the assignment of a data quality score based on the general

403

criterion of uniqueness and completeness of the observation, could be considered by the developers of data

404

collection platforms and for future data analysis by others 1.

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409

ASSOCIATED CONTENT

410

Supporting Information. Data processing and cleaning methodology; Detailed methodology for derivation of

411

emission factors and calculation of GHG footprints; Data inclusion criteria for the different types of analysis;

412

Literature review of the typical growing period of open-field tomato proudction in different countries; Methodology

413

of the geocoding of farm locations and extraction of spatial-temporal parameters from spatial maps; Results of the

414

variability of GHG footprints; Results of the variability of yield, fertilizer application and energy use; Results of

415

the linear mixed model analysis; Results of the variability of GHG footprints with area of production and method

416

of fertilizer application supplied as Supporting Information are available free of charge via Internet at

417

http://pubs.acs.org.

418 419

AUTHOR INFORMATION

420

Corresponding Author

421

* Address: P.O. Box 9010 (mailbox no 89), NL-6500 GL Nijmegen, Phone: +31 (0)24 - 356 20 60; e-mail: lam-

422

[email protected]

423

Funding Sources

424

The study is part of the RELIability of product Environmental Footprints (RELIEF) project, which is funded by the Eu-

425

ropeans Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement

426

No. 641459.

427

Notes

428

The authors declare no competing financial interest.

429

ACKNOWLEDGMENTS

430

The authors would like to thank Isabela Butnar, Lau Tamberg, Valerio Barbarossa, Zoran Steinmann and Mirza Čengić

431

for their support in data extraction and technical advice. We would also like to thank three anonymous reviewers for

432

their valuable input.

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___________________________________________________________________________________

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TOC Art 74x36mm (120 x 120 DPI)


Variability of Greenhouse Gas Footprints of Field Tomatoes Grown for

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