This study evaluated the eco-efficiency of rainfed and irrigated maize production in Bosnia and Herzegovina. Environmental impact assessments were performed through energy, carbon footprint, and water scarcity footprint analysis. For economic analysis, gross and net returns and benefit–cost ratios were calculated. Eco-efficiency was measured by the ratio between the net return and environmental criteria. The findings indicate that the transition from rainfed to irrigated maize cultivation per unit of land results in a 53.7% higher yield and gross value of production, but also a 69.8% increase in energy input, a 22% rise in greenhouse gas emissions, and a 3.6-fold increase in the water scarcity footprint. While a positive link exists in irrigated maize between higher yield and lower carbon footprint per unit of product, rainfed systems outperform in energy efficiency, productivity, profitability, water scarcity footprint, and overall eco-efficiency. Both systems rely heavily on nonrenewable energy sources, with fertilization (affecting energy and carbon footprint), mechanization (affecting carbon footprint), and irrigation (exacerbating water scarcity) as the main contributors to the negative environmental impacts. The implementation of optimization strategies for these inputs is of paramount importance to reduce environmental impacts and promote sustainability in maize farming in Bosnia and Herzegovina.

  • Irrigated maize has 69.8% greater energy input than rainfed one.

  • Irrigated maize's energy performance indexes are lower than rainfed one.

  • Irrigated maize had 22% higher greenhouse gas emissions but 24% lower carbon footprint.

  • Irrigated maize has 53.7% higher revenue with 2.1–3.6 more water scarcity footprint.

  • Rainfed maize combines comparable economic return with low environmental impacts.

LCA

Life cycle assessment

LCC

Life cycle costing

LCIA

Life cycle impact assessment

GWP

Global warming

GHG

Greenhouse gas

EUE

Energy use efficiency

SE

Specific energy

EP

Energy productivity

BiH

Bosnia and Herzegovina

NR

Net return

GR

Gross return

BCR

Benefit–cost ratio

Cereals and grains are staple foods in the majority of human diets in both developed and developing countries (Laskowski et al. 2019). They provide a significant amount of energy, protein, B vitamins, and minerals to the world's population (McKevith 2004). Maize (Zea mays L.) is one of the most abundantly produced cereals in the world (Erenstein et al. 2022) and is fundamental to ensuring food security and sovereignty (Langner et al. 2019). It is a versatile multipurpose crop used for human consumption (direct food consumption pathway, processed or unprocessed), as livestock feed, and derives from animal-sourced foods (Erenstein et al. 2022). Every year, the United States, China, Brazil, Argentina, Ukraine, India, Indonesia, Mexico, the Russian Federation, and Canada produce more than 1 billion tons of corn.

Maize is also one of the major crops in the European Union (EU-28) and one of the most strategic agricultural products in many Western Balkan countries (Albania, Bosnia and Herzegovina (BiH), North Macedonia, Montenegro, and Serbia). Cereal production accounts for a significant portion of total agricultural production in Bosnia and Herzegovina (Zurovec et al. 2015). Maize and wheat are the most important cereals typically grown in a two-field crop rotation system. Maize is the most widely grown crop, especially in the north and east of the country (Grujcic et al. 2018). In the entity of the Republic of the Srpska (RS) in Bosnia and Herzegovina, maize represents 46.62% of the total arable land (>94,000 hectares) and 24.86% of the utilized agriculture areas (Republican Bureau of Statistics).

Over the last 15 years, BiH's territory has been influenced by both dry and wet years (UNDP 2021). Irrigation has been used as a supplement to rainfed agriculture to stabilize and increase yields. Bosnia's maize production is expected to reach 1.47 million metric tons in 2026, with an average annual growth rate of 2.2% since 2021 (Reportlinker 2021). As a result, the maize production is expected to intensify, leading to an increase in the inputs of agricultural resources (e.g., water, energy, seeds, labor, fertilizers, pesticides, technologies, and knowledge) to increase the aggregate yield per unit land area. Indeed, fertilizer consumption (kilograms per hectare of arable land) has increased linearly with crop production, from 17.1 kg ha−1 in 1995 to 90.1 kg ha−1 in 2020 (World Bank 2023). The country's need for irrigation of crops is expected to increase during dry summer periods, when water needs are greatest due to significant spatial and temporal variations in precipitation distribution.

The irrigated crop yields are always larger than rainfed yields across climate conditions. However, new scrutiny has raised concerns about the environmental costs of irrigation agricultural intensification (Garcia 2020). It is therefore important to assess the environmental impacts of increased crop productivity, resulting from higher levels of fertilization and the adoption of irrigation agriculture instead of rainfed. In particular, understanding the interdependences of different pillars, as well as addressing a life cycle perspective, is essential when evaluating food production systems (Fernández-Ríos et al. 2022). Methods such as life cycle energy analysis, life cycle assessment (LCA), life cycle costing (LCC), and various social and environmental evaluation approaches have emerged to identify strengths and weaknesses in energy consumption, environmental impact, and economic factors when viewed from a comprehensive life cycle perspective. Energy analysis, LCA, LCC, and eco-efficiency are commonly used to assess the sustainability of crop production, particularly maize production. These methods have been applied in multiple countries, such as Albania (Canaj & Mehmeti 2022), Greece (Bilalis et al. 2013), Turkey (Canakci et al. 2005), Italy (Fantin et al. 2017), Poland (Król-Badziak et al. 2021), Iran (Lorzadeh et al. 2011), China (Wang et al. 2007), Argentina (Arrieta et al. 2018), and the United States (Kim et al. 2014). Additional research by Zhang et al. (2018), Baum & Bieńkowski (2020), and Xiong et al. (2022) further contributed to our understanding of the environmental trade-off between productivity and environmental and economic sustainability in irrigated and rainfed maize production. These studies indicated that although higher resource usage led to an overall increase in environmental impact, targeted efforts to optimize crop yields and production efficiency could help mitigate carbon emissions.

The performance indices and advantages associated with each agricultural strategy are likely to be strongly influenced by the specific agronomic practices implemented in each country. Given the importance of the maize cultivation in BiH and growing concerns about water–energy–environmental trade-offs of irrigation, this research aims at assessing the eco-efficiency of maize production in BiH using rainfed and irrigation strategy. Our primary research question is which cultivation system aligns better with the principles of sustainability and eco-efficiency. To answer this, we employed an energy analysis, LCA, LCC, and evaluated eco-efficiency. The study's novelty lies in its holistic approach, considering the interplay of management strategies, the balance between economic and environmental performances, and a better understanding of the impact of intensified irrigation on the sustainability of crop production in southeastern Europe. In the realm of product sustainability and eco-efficiency research, there is notable interest in LCA and LCC. However, their application remains relatively scarce in Bosnia and Herzegovina (BiH) and the broader southeastern European context. Consequently, it is imperative to delve into the associated environmental and cost implications of cereal production from a life cycle perspective.

Study area

The data for this study were obtained from the H2020 SMARTWATER project's (https://www.smartwater-project.eu/) demonstration activities, which were set up in Aleksandrovac (44°58′ N; 17°18′ E), which is located 7 km from Laktaši, near Banja Luka (Figure 1).
Figure 1

Location of the study area (Aleksandrovac site).

Figure 1

Location of the study area (Aleksandrovac site).

Close modal

Laktaši is located in the western part of Republika Srpska and has a total area of 388.34 km2. The elevation is 122 m. The city's agricultural lands cover approximately 25,270 ha, with arable land accounting for 19,741 ha (78.12%), pastures accounting for 2,788 ha (11.03%), and orchards accounting for 1,762 ha (6.97%). Automorphic soils cover 74% of the land surface. District cambisol covers approximately 49.91% of the territory, making it the most common type of soil in this area. The climate in the Republika Srpska is favorable for maize farming. The climate of the region is mildly continental. The average annual temperature from 1961 to 2009 was around 10.8 °C. January has the lowest average temperature (0.6 °C), and July has the highest (21.5 °C). Precipitation ranges from 938 to 1,100 mm on average. Maize is usually planted in the spring when temperatures have warmed up and soil conditions are suitable. The growing season typically extends through the summer, with maize ready for harvest in late summer or early autumn. The total production of maize in 2021 was 429,755 tons, and the average yield was 4.6 t ha−1.

Assessment framework

The primary objective of this study was to conduct an assessment of the eco-efficiency performance and identify key areas of environmental impact in the production of maize under varying irrigation conditions, specifically rainfed and full irrigation. The study focused on the domestic maize hybrid known as BL 43, which holds a prominent position in Bosnia and Herzegovina's agricultural landscape. This hybrid is included in the country's official list of approved maize hybrids for cultivation and production due to its remarkable adaptability, particularly its capacity to withstand drought conditions. It is categorized within the FAO 400 group. The maize hybrid in question boasts robust and flexible stems, typically averaging a height of 270 cm. To assess its eco-efficiency comprehensively, we developed a model from a life cycle perspective. The phases included (i) scope definition for determining system boundaries, assumptions, limitations, and functional unit; (ii) data collection and inventory on resource inputs, energy consumption, emissions, waste generation, and economic costs; (iii) calculation of energy consumption, greenhouse gas (GHG) emissions, and water scarcity footprint (WSF) using LCA; (iv) LCC and economic framework to evaluate the economic performance of the hybrid alongside LCA; and (v) integration of the results of the LCA and LCC to evaluate the eco-efficiency. We utilized OpenLCA (GreenDelta 2022) modeling software version 1.11.0, as of the year 2022, developed by GreenDelta.

System boundaries and functional units

We adopted an attributional approach and cradle-to-farm gate to our analysis (Figure 2). The cradle-to-farm gate is composed of both on- and off-farm activities, i.e., the processes of manufacturing means of agricultural production and the cultivation of maize.
Figure 2

System boundaries of energy, GHG, and LCC of maize production.

Figure 2

System boundaries of energy, GHG, and LCC of maize production.

Close modal

As functional units, we used 1 kg and 1 ton of grain maize at the farm exit gate and 1 hectare of cultivated maize. The use of these units enabled us to capture and assess two essential aspects: the production intensity on a per-weight basis (1 kg and 1 ton of maize) and the technical efficiency of production in terms of land utilization (1 hectare of agricultural land). This approach enabled a comprehensive evaluation of the eco-efficiency of maize production across different dimensions.

Data collection and inventory

Maize production input data from SmartWater H2020 demonstration activities and Laktaši farmers were collected for eco-efficiency assessment of maize production. Average human labor, seeds, diesel fuel, chemical fertilizers, and plant protection were among the generic input data (Table 1). The water, energy, and water supply infrastructure were included as part of irrigated maize. The average maize grain yield was 6,016 and 9,892 kg ha−1 for rainfed and irrigated maize, respectively. The seed rate was 20 kg ha−1. Maize is sown at a 70 × 22.4 cm distance with 5 m of interspace. The basic fertilization was 300 kg ha−1 NPK (10:26:26) at tillage, and 200 kg ha−1 NPK (15:15:15) was applied at sowing. The rest of the fertilizer was supplied at the V6 phase (on June 4) and at the V14 phase (on July 13) with 110 kg ha−1 of ammonium nitrate (AN). Basar (S-metolahlor 960 g litre−1), Rezon Terbutilazin (500 g L−1), Talisman Nikosulfuron (40 g L−1), Plamen (dikamba – DMA 577.9 g L−1), and Alteox T prima (Alcohol ethoxylate 900 g L−1) were applied as plant protection products. Plots were drip-irrigated, and the irrigation scheduling was based on maize's actual evapotranspiration estimated by the two-step Food and Agriculture Organization (FAO) approach. The irrigation infrastructure (pump and on-farm system) was modeled using generic data retrieved from AUSLCI (ALCAS 2017) and the Ecoinvent database (Ecoinvent Database 3.1 2014).

Table 1

Data input for rainfed and fully irrigated maize cultivation in BiH

ParametersUnitRainfedIrrigated
Seeds kg t−1 3.32 2.02 
Diesel fuel L t−1 23.27 14.15 
Lubricating oil kg t−1 0.88 0.54 
Tractor work h t−1 8.89 5.41 
Tractor module kg t−1 4.45 2.70 
Synthetic rubber kg t−1 0.78 0.48 
Nitrogen fertilizer, as N kg N t−1 14.21 8.64 
Phosphorus fertilizer, as P2O5 kg P2O5 t−1 17.95 10.92 
Potassium fertilizer, as K2kg K2O t−1 17.95 10.92 
Herbicides kg t−1 0.636 0.387 
Pesticides kg t−1 0.045 0.027 
Irrigation water m3 t−1 – 483.20 
Diesel fuel irrigation MJ t−1 – 528.57 
Irrigation pump unit ha−1 – 
Extrusion, plastic pipes kg t−1 – 0.02605 
High-density polyethylene, HDPE kg t−1 – 0.01088 
Low-density polyethylene, LDPE kg t−1 – 0.01374 
Polypropylene, PP kg t−1 – 0.00037 
Polyvinylidenechloride kg t−1 – 0.00107 
Steel kg t−1 – 2.4 × 106 
Human labor h t−1 37.57 22.85 
ParametersUnitRainfedIrrigated
Seeds kg t−1 3.32 2.02 
Diesel fuel L t−1 23.27 14.15 
Lubricating oil kg t−1 0.88 0.54 
Tractor work h t−1 8.89 5.41 
Tractor module kg t−1 4.45 2.70 
Synthetic rubber kg t−1 0.78 0.48 
Nitrogen fertilizer, as N kg N t−1 14.21 8.64 
Phosphorus fertilizer, as P2O5 kg P2O5 t−1 17.95 10.92 
Potassium fertilizer, as K2kg K2O t−1 17.95 10.92 
Herbicides kg t−1 0.636 0.387 
Pesticides kg t−1 0.045 0.027 
Irrigation water m3 t−1 – 483.20 
Diesel fuel irrigation MJ t−1 – 528.57 
Irrigation pump unit ha−1 – 
Extrusion, plastic pipes kg t−1 – 0.02605 
High-density polyethylene, HDPE kg t−1 – 0.01088 
Low-density polyethylene, LDPE kg t−1 – 0.01374 
Polypropylene, PP kg t−1 – 0.00037 
Polyvinylidenechloride kg t−1 – 0.00107 
Steel kg t−1 – 2.4 × 106 
Human labor h t−1 37.57 22.85 

Energy and environmental footprint

Energy analysis method

The energy equivalent of inputs and outputs was determined by multiplying the amount of each input by the related energy coefficient (Table 2). The energy was classified into direct, indirect, renewable, and nonrenewable sources. The direct energy included human labor, diesel fuel, oil, and water for irrigation. Indirect energy included seeds, chemical fertilizers, plant protection products, and agricultural machinery. The energy from human labor, seeds, and water for irrigation was considered renewable energy. Diesel fuel energy, plant protection products, chemical fertilizers, and machinery were considered nonrenewable energies.

Table 2

The energy equivalent of inputs by type and source and output in maize production

ParameterEnergy equivalentsUnitType of energySource of energyReference
Human labor 1.96 MJ h−1 Direct Renewable Alam et al. (2023)  
Seeds corn 14.7 MJ kg−1 Indirect Renewable Zahedi et al. (2015)  
Pesticide, unspecified 193 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Herbicide 238 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Diesel fuel 56.31 MJ L−1 Direct Nonrenewable Alam et al. (2023)  
Nitrogen (N) 66.14 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Phosphorus (P) 12.44 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Potassium (K) 11.15 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Tractor machinery 62.7 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Water, irrigation 1.03 MJ m−3 Direct Renewable Alam et al. (2023)  
Maize, grain 14.7 MJ kg−1 – – Zahedi et al., (2015)  
ParameterEnergy equivalentsUnitType of energySource of energyReference
Human labor 1.96 MJ h−1 Direct Renewable Alam et al. (2023)  
Seeds corn 14.7 MJ kg−1 Indirect Renewable Zahedi et al. (2015)  
Pesticide, unspecified 193 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Herbicide 238 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Diesel fuel 56.31 MJ L−1 Direct Nonrenewable Alam et al. (2023)  
Nitrogen (N) 66.14 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Phosphorus (P) 12.44 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Potassium (K) 11.15 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Tractor machinery 62.7 MJ kg−1 Indirect Nonrenewable Alam et al. (2023)  
Water, irrigation 1.03 MJ m−3 Direct Renewable Alam et al. (2023)  
Maize, grain 14.7 MJ kg−1 – – Zahedi et al., (2015)  

To assess the energy performance of each cultivation strategy, a series of energy parameters were evaluated by the energy ratio between output and input. The energy use efficiency (EUE), energy productivity (EP), specific energy (SE), net energy (NEG), and energy profitability were calculated using the following formulas (Canaj & Mehmeti 2022; Alam et al. 2023):
formula
(1)
formula
(2)
formula
(3)
formula
(4)
formula
(5)

These indicators collectively provide a comprehensive picture of energy intensity and sustainability of each cultivation system. The EUE quantifies the extent to which the energy is converted into useful outputs, such as crops or food products. The EP assesses efficiency of energy use by the system in achieving production goals, while SE quantifies the extent to which energy is converted into useful crop output. The NEG indicates whether the production process is a NEG gain or loss.

Carbon footprint modeling

To assess GHG emissions from grain maize production, we used the carbon footprint (CF) methodology, according to the guidelines of the LCA methodology. The metric is global warming potential over a 100-year time horizon (GWP100). This metric integrates the radiative forcing of events (such as GHG emissions) over 100 years and compares it to the reference gas CO2. In the GWP calculation, we accounted for direct (foreground) and indirect (background) burdens. Background emissions from agricultural inputs in the carbon equivalent (GWPinput,j, kg CO2-eq ha−1) for crop j were estimated using the following equation:
formula
(6)
where ADi,j is the activity data about the input of the ith agricultural material for crop j (unit ha−1) and EFi,j is the emission factor of the ith agricultural material for crop j (kg CO2-eq unit−1).

Background included the impact of the production of seeds, fertilizers, pesticides, diesel, tractors, and irrigation equipment.

The direct emissions of GHG gases (Equation (7)) are expressed as carbon dioxide equivalents (CO2-eq).
formula
(7)
where mi,j is the mass of ith GHG gas input (kg GHG ha−1) and GWPi is the global warming potential of the ith GHG gas (kg CO2-eq kg GHG−1).

Table 3 presents the main parameters and emission coefficients to characterize the CF of maize production. In the calculation model, we accounted for the onsite GHG emissions from different practices, i.e., combustion of fossil fuels by the tractor, irrigation engines, and application and decomposition of fertilizers. The considered nitrogen-based environmental emissions from fertilizers were direct dinitrogen monoxide emissions (0.01 kg N2O–N for rainfed and 0.021 kg N2O–N for irrigated), indirect N2O from atmospheric deposition (0.01 kg N2O–N kg NH3–N−1), leaching/runoff (0.011 kg N2O–N kg NO3–N−1), nitrate–nitrogen leaching loss (0.24 kg NO3–N kg N−1), ammonia volatilization (0.24 kg NH3–N kg N−1 for rainfed and 0.3 kg NH3–N kg N−1), and nitrous oxide (0.21 kg NOx kg N2O−1). The method used to characterize GHG emissions was ReCiPe 2016 (Huijbregts et al. 2017). The background data were retrieved from database Ecoinvent 3.1, which includes average market data for most existing materials and energy supply processes and/or services.

Table 3

Data used for GHG emission computation of maize production in BiH

ParameterCoefficientUnitReference
N2O emissions from N inputs, rainfed 0.01 kg N2O–N kg N input−1 (CarbonCloud 2023
N2O emissions from N inputs, irrigated 0.021 kg N2O–N kg N input−1 
kg volatilized N per kg synthetic N 0.11 kg volatilized N kg N input−1 
N2O emissions from atmospheric deposition of N 0.01 kg N2O–N/(kg NH3–N + NOx–N) 
Fraction of N lost through leaching and runoff 0.24 kg N/kg N 
N2O emissions from N leaching and runoff 0.011 kg N2O–N/kg N 
Human labor 0.7 kg CO2-eq man hour −1 (Ordikhani et al. 2021
Maize seed, for sowing 2.18 kg CO2-eq kg−1 EcoInvent 3.1 
Pesticide, unspecified 11.49 kg CO2-eq kg−1 EcoInvent 3.1 
Herbicide 11.49 kg CO2-eq kg−1 EcoInvent 3.1 
Diesel fuel, including upstream emissions 0.0951 kg CO2-eq MJ−1 EcoInvent 3.1 
Lubricating oil 1.23 kg CO2-eq kg−1 EcoInvent 3.1 
Synthetic rubber 3.16 kg CO2-eq kg−1 EcoInvent 3.1 
Nitrogen fertilizer, as N 11.62 kg CO2-eq kg−1 EcoInvent 3.1 
Phosphorus fertilizer, as P2O5 1.88 kg CO2-eq kg−1 EcoInvent 3.1 
Potassium fertilizer, as K21.85 kg CO2-eq kg−1 EcoInvent 3.1 
Tractor machinery 6.1 kg CO2-eq kg−1 EcoInvent 3.1 
Pump, 40 Watt 8.7 kg CO2-eq unit−1 EcoInvent 3.1 
ParameterCoefficientUnitReference
N2O emissions from N inputs, rainfed 0.01 kg N2O–N kg N input−1 (CarbonCloud 2023
N2O emissions from N inputs, irrigated 0.021 kg N2O–N kg N input−1 
kg volatilized N per kg synthetic N 0.11 kg volatilized N kg N input−1 
N2O emissions from atmospheric deposition of N 0.01 kg N2O–N/(kg NH3–N + NOx–N) 
Fraction of N lost through leaching and runoff 0.24 kg N/kg N 
N2O emissions from N leaching and runoff 0.011 kg N2O–N/kg N 
Human labor 0.7 kg CO2-eq man hour −1 (Ordikhani et al. 2021
Maize seed, for sowing 2.18 kg CO2-eq kg−1 EcoInvent 3.1 
Pesticide, unspecified 11.49 kg CO2-eq kg−1 EcoInvent 3.1 
Herbicide 11.49 kg CO2-eq kg−1 EcoInvent 3.1 
Diesel fuel, including upstream emissions 0.0951 kg CO2-eq MJ−1 EcoInvent 3.1 
Lubricating oil 1.23 kg CO2-eq kg−1 EcoInvent 3.1 
Synthetic rubber 3.16 kg CO2-eq kg−1 EcoInvent 3.1 
Nitrogen fertilizer, as N 11.62 kg CO2-eq kg−1 EcoInvent 3.1 
Phosphorus fertilizer, as P2O5 1.88 kg CO2-eq kg−1 EcoInvent 3.1 
Potassium fertilizer, as K21.85 kg CO2-eq kg−1 EcoInvent 3.1 
Tractor machinery 6.1 kg CO2-eq kg−1 EcoInvent 3.1 
Pump, 40 Watt 8.7 kg CO2-eq unit−1 EcoInvent 3.1 

Water scarcity footprint modeling

The WSF profile was calculated using the LCA-based WF method AWARE (Boulay et al. 2017). The WSF profile accounted for foreground, or direct, water consumption and background, indirect water consumption. Background impacts from agricultural inputs (WSFinputj, m3 world eq ha−1) for each input j were estimated using the equation:
formula
(8)
where ADi,j is the activity data about the input of the ith agricultural material (unit ha−1) and CFi is the AWARE characterization factor of the ith agricultural material (m3 world eq unit−1).
Background impacts included the production of inputs (e.g., seeds, fertilizers, pesticides, diesel, tractors, and irrigation equipment). The WSF for each agricultural input was calculated with OpenLCA with data from EcoInvent 3.1 database (Table 4). The direct WSF impacts were estimated as a product of the volume of irrigation water consumption (VW) and the local water scarcity factor (CFAWARE).
formula
(9)
where we calculated the WSF of irrigation using regional-level CFs for the AWARE method (Boulay & Lenoir 2020). The sub-national CF for Republika Srpska is 1.21 m3 world eq./m3 consumed. The background scarcity data were retrieved from database Ecoinvent 3.1, which includes average market data for most existing materials and energy supply processes and/or services.
Table 4

Data used for water scarcity footprint computation

ParameterWSF_AWAREUnitReference
Maize seed, for sowing 0.441 m3 world eq kg−1 EcoInvent 3.1 
Pesticide, unspecified 3.6 m3 world eq kg−1 EcoInvent 3.1 
Herbicide 3.6 m3 world eq kg−1 EcoInvent 3.1 
Diesel fuel 0.00892 m3 world eq MJ−1 EcoInvent 3.1 
Lubricating oil 0.46 m3 world eq kg−1 EcoInvent 3.1 
Synthetic rubber 1.81 m3 world eq kg−1 EcoInvent 3.1 
Nitrogen fertilizer, as N 5.52 m3 world eq kg−1 EcoInvent 3.1 
Phosphorus fertilizer, as P2O5 2.76 m3 world eq kg−1 EcoInvent 3.1 
Potassium fertilizer, as K22.18 m3 world eq kg−1 EcoInvent 3.1 
Tractor machinery 2.03 m3 world eq kg−1 EcoInvent 3.1 
Pump, 40 Watt 4.41 m3 world eq kg−1 EcoInvent 3.1 
Water, Republika Srpska (CFagri, region1.21 m3 world eq m−3 consumed (Boulay & Lenoir 2020
ParameterWSF_AWAREUnitReference
Maize seed, for sowing 0.441 m3 world eq kg−1 EcoInvent 3.1 
Pesticide, unspecified 3.6 m3 world eq kg−1 EcoInvent 3.1 
Herbicide 3.6 m3 world eq kg−1 EcoInvent 3.1 
Diesel fuel 0.00892 m3 world eq MJ−1 EcoInvent 3.1 
Lubricating oil 0.46 m3 world eq kg−1 EcoInvent 3.1 
Synthetic rubber 1.81 m3 world eq kg−1 EcoInvent 3.1 
Nitrogen fertilizer, as N 5.52 m3 world eq kg−1 EcoInvent 3.1 
Phosphorus fertilizer, as P2O5 2.76 m3 world eq kg−1 EcoInvent 3.1 
Potassium fertilizer, as K22.18 m3 world eq kg−1 EcoInvent 3.1 
Tractor machinery 2.03 m3 world eq kg−1 EcoInvent 3.1 
Pump, 40 Watt 4.41 m3 world eq kg−1 EcoInvent 3.1 
Water, Republika Srpska (CFagri, region1.21 m3 world eq m−3 consumed (Boulay & Lenoir 2020

Economic and eco-efficiency performance

The economic performance of maize cultivation was assessed by calculating several key indicators, including the gross return (GR), net return (NR), and the benefit-to-cost (B:C) ratio. The gross value of production (GVP) was derived by multiplying the crop yield by the farm gate or producer price. The variable cost of production (VCP) represented the average expenses incurred in the entire process of maize cultivation: the cost of hiring human labor, diesel, seed, irrigation water, fertilizers, and pesticides. The costs of renting land and farm equipment were considered fixed costs. All of these costs were determined based on the average local price and were related to functional units of one hectare and one ton.
formula
(10)
formula
(11)
formula
(12)
formula
(13)
where GVP is the gross value of production (€ ha−1); FP is farmer producer price (€ kg−1); CY is the crop yield (kg ha−1); NR is the net return (€ ha−1); VCP is the variable cost of production (€ ha−1); TCP is sum of variable cost + fixed of production (€ ha−1); and BCR is the benefit–cost ratio.

The unit costs considered were 3.85 € kg−1 for seeds, 6 € h−1 for human labor, 0.5 € m−3 for irrigation water, 1.4 € kg−1 for diesel, 1.8 € kg−1 for oil, and 218 € kg−1 for pesticides. The price of nitrogen, phosphorus, and potassium fertilizer was 0.43 € kg−1, 0.74 € kg−1, and 0.74 € kg−1, respectively. The farm producer price considered was 0.85 € kg−1.

Eco-efficiency is a combined assessment of a product system's environmental performance and value. Eco-efficiency indicators are ratios of economic indicators and environmental indicators (use of nature). Eco-efficiency was measured by the following formula:
formula
(14)

Eco-efficiency was assessed across energy consumption, global warming potential, and water scarcity. The higher the eco-efficiency, the higher the net profit relative to the environmental burden/impact.

Energy balance and performance indicators

The calculated energy balance and performance indices in rainfed and irrigated maize production are presented in Table 5. In rainfed maize production, the total input and output energies are 19,819 and 88,436 MJ ha−1, respectively. For fully irrigated maize, these values were 34,837 and 145,419 MJ ha−1, respectively. The gap in energy input between these two systems amounted to 15,018 MJ per hectare, representing a considerable 69.8% difference. These findings underscore the significant differences in the energy requirements of these agricultural practices and shed light on their respective energy efficiency levels.

Table 5

Energy balance of rainfed and irrigated maize in BiH

IndicatorUnitRainfedIrrigated
Energy input MJ ha−1 19,819 34,837 
Energy output MJ ha−1 88,436 145,419 
Net energy gain MJ ha−1 68,617 110,581 
IndicatorUnitRainfedIrrigated
Energy input MJ ha−1 19,819 34,837 
Energy output MJ ha−1 88,436 145,419 
Net energy gain MJ ha−1 68,617 110,581 

The reported energy inputs of maize cultivation are 9,803.78 MJ ha−1 in Nigeria (Kosemani & Bamgboye 2021), 10,999.61 MJ ha−1 in Nepal (Poudel et al. 2019), 12,448.6 MJ ha−1 in Albania (Canaj & Mehmeti 2022), 23,338 MJ ha−1 in Greece (Bilalis et al. 2013), 24,679.94 M ha−1 in Turkey (Karaağaç et al. 2011), and 39,295.50 MJ ha−1 in Iran (Lorzadeh et al. 2011). The reported global mean energy input is 33,072.1 MJ ha−1 (Elsoragaby et al. 2019). Our energy inputs ranged from 21,506 to 36,524 MJ ha−1, which is comparable to Greece, Turkey, and Iran but higher than the values reported in Albania, Nigeria, and Nepal.

According to Figure 3, the main sources of the total energy used in the production of rainfed maize were mechanization (50%), fertilizers (41%), and plant protection (5%). The total amount of irrigation energy used for irrigated maize was calculated to be 15,018 MJ ha−1. The total irrigation energy was calculated as the sum of three elements: 4,874 MJ ha−1 for irrigation water, 7,642 MJ ha−1 for irrigation energy, and 2,502 MJ ha−1 for water supply infrastructure. Collectively, these components accounted for 43% of the total energy input in irrigated maize. This demonstrates the substantial role of energy associated with irrigation in the overall energy requirements of irrigated maize farming.
Figure 3

The share of different processes in energy input of maize production.

Figure 3

The share of different processes in energy input of maize production.

Close modal
Figure 4 classifies the total energy consumption in maize production into direct, indirect, renewable, and nonrenewable. In rainfed maize production, the share of direct, indirect, renewable, and nonrenewable energies was 40.4, 59.6, 3.4, and 96.6%, respectively. In irrigated maize production, direct, indirect, renewable, and nonrenewable energy shares were 58, 42, 15.4, and 84.6%, respectively. Rainfed maize production is significantly reliant on nonrenewable energy sources, implying a significant consumption of fossil fuels and potentially greater environmental implications. Irrigated maize production, on the other hand, has a higher proportion of renewable energy, mainly from water characterized as a renewable energy source. Both systems use a large amount of indirect energy, emphasizing the need of accounting for the energy embedded in inputs, as well as their production and transportation, when calculating the overall energy footprint of maize production.
Figure 4

The proportion of direct, indirect, renewable, and nonrenewable energies in rainfed and irrigated maize in Bosnia and Herzegovina.

Figure 4

The proportion of direct, indirect, renewable, and nonrenewable energies in rainfed and irrigated maize in Bosnia and Herzegovina.

Close modal

In other studies (Karaağaç et al. 2011; Elsoragaby et al. 2019; Kosemani & Bamgboye 2021), it was found that the indirect and nonrenewable energy contribution is higher than the direct and renewable energy contribution. In Turkey (Karaağaç et al. 2011), the share of direct, indirect, renewable, and nonrenewable energies was 20.28, 79.72, 1.64, and 98.16%, respectively. In Nigeria (Kosemani & Bamgboye 2021), the contributions of direct and indirect energy were 36.44 and 63.56%, while renewable and nonrenewable energy were 4.42 and 95.58%, respectively. The primary factors explaining the variation in direct energy between our findings and previous research are water supply and diesel energy for irrigation. Both of these factors fall under the category of direct energy sources. However, it is important to note that both systems share a common characteristic in that they predominantly rely on nonrenewable energy sources.

Based on energy input, yield, and energy output data, the EUE, EP, SE, NEG, and energy profitability were calculated (Table 6).

Table 6

Energy performance indicators of rainfed and irrigated maize in BiH

IndicatorUnitRainfedIrrigated
Energy use efficiency – 4.46 4.17 
Energy productivity kg MJ−1 0.30 0.284 
Specific energy MJ kg−1 3.29 3.52 
Energy profitability – 3.46 3.17 
IndicatorUnitRainfedIrrigated
Energy use efficiency – 4.46 4.17 
Energy productivity kg MJ−1 0.30 0.284 
Specific energy MJ kg−1 3.29 3.52 
Energy profitability – 3.46 3.17 

Rainfed maize energy efficiency, productivity, intensity, and profitability were estimated to be 4.46, 0.3 kg MJ−1, 3.29 MJ kg−1, and 3.46, respectively. For irrigated maize, these values were estimated to be 4.17, 0.284 kg MJ−1, 3.52 MJ kg−1, and 3.17, respectively. According to the findings, irrigated maize has 10.5% more SE. This means that it consumed approximately 10.5% more energy resources per unit of maize produced compared to rainfed maize. In other words, it was less energy efficient in terms of energy input per unit of output. Irrigated maize exhibited a 9.5% lower energy efficiency and productivity. This indicates that, for the same amount of energy input, irrigated maize yielded slightly less maize output compared to rainfed maize. It implies that rainfed maize is more efficient in converting energy into maize production.

The reported global mean EUE is 4.96 (Elsoragaby et al. 2019), or 10–17% lower than the results of this study. The EUE of maize studies varies from 1.48 to 8.63. It is reported to be 2.46 in Nigeria (Kosemani & Bamgboye 2021), 4.14 in Nepal (Poudel et al. 2019), 7.63 in Albania (Canaj & Mehmeti 2022), 8.63 in Greece (Bilalis et al. 2013), 6.54 in Turkey (Karaağaç et al. 2011), and 1.48 in Iran (Lorzadeh et al. 2011). The maize EP is reported to be 5.96 kg MJ−1 in Nigeria (Kosemani & Bamgboye 2021), 0.28 kg MJ−1 in Nepal (Poudel et al. 2019), 0.52 kg MJ−1 in Albania (Canaj & Mehmeti 2022), 0.59 kg MJ−1 in Greece (Bilalis et al. 2013), 0.44 kg MJ−1 in Turkey (Karaağaç et al. 2011), and 0.1 kg MJ−1 in Iran (Lorzadeh et al. 2011). The reported global mean EP is 0.5 kg MJ−1 (Elsoragaby et al. 2019), or 39–43% higher than in this study. The reported global mean SE for 1 kg of maize was reported to be 4.68 (Elsoragaby et al. 2019), or 25–30% higher than in this study. The percentage difference indicates that the energy consumption in the maize cultivation in Bosnia is lower, which can be seen as a positive outcome in terms of resource efficiency and potentially reduced environmental impact.

GHG emissions and carbon footprint

The results of the CF are summarized in Table 7. The average values for global warming were 2,833.75 and 3,554.1 kg CO2-eq ha−1 for rainfed and irrigated maize, respectively. This equates to 471.07 and 359.3 kg CO2-eq per ton of product for rainfed maize and irrigated maize, respectively. Irrigated maize has a 22% higher environmental CO2-eq emission intensity than rainfed maize. On the other hand, rainfed maize has a 24% higher impact per ton of product. Zhang et al.'s (2018) results demonstrated a consistent pattern, wherein GHG emissions were notably higher by 40% in the irrigated agricultural system compared to the rainfed system. However, despite the higher GHG emissions, the CF of the irrigated system was found to be 37% lower than that of the rainfed system. This suggests that while the irrigated system produced more GHG emissions per unit of land, it had a more favorable overall CF due to crop productivity.

Table 7

Cradle-to-gate global warming potential expressed for rainfed and irrigated maize in BiH

IndicatorUnitRainfedIrrigated
Global warming potential kg CO2-eq ha−1 2,833.75 3,554.1 
Global warming potential kg CO2-eq t−1 479.7 359.3 
IndicatorUnitRainfedIrrigated
Global warming potential kg CO2-eq ha−1 2,833.75 3,554.1 
Global warming potential kg CO2-eq t−1 479.7 359.3 

Per 1 ha, the GWP value for maize (mix system) ranges from 2,440 to 4,200 kg CO2-eq. ha−1. The intensity is in agreement with previous studies. For 1-ton maize production, large variations in GWP value exist between studies, as different values of input data and methods are used for the assessment. For 1-ton maize production, the reported GWP in the literature is −27 to 436 kg CO2-eq in the United States (Kim et al. 2014), 121 kg CO2-eq in Spain (Abrahão et al. 2017), 178–189.8 kg CO2-eq in Poland (Holka & Bieńkowski 2020), 181 kg CO2-eq in Albania (Canaj & Mehmeti 2022), 203 kg CO2-eq in Brazil (Giusti et al. 2022), 195 kg CO2-eq in Argentina (Arrieta et al. 2018), 243–353 kg CO2-eq in Canada (Jayasundara et al. 2014), 393.86–620 kg CO2-eq in North and Northeast China (Wang et al. 2007, 2015), 410 kg CO2-eq in Northern Italy (Fantin et al. 2017), 429 kg CO2-eq in Thailand, and 590–850 kg CO2-eq in Poland (Król-Badziak et al. 2021).

Figure 5 shows the GHG emissions sources for each strategy. According to Figure 5, the main sources of global warming potential in the production of rainfed maize were fertilizers (67%), mechanization (25%), and human labor (5%). The soil N2O emissions induced 18.7% of the rainfed GWP impact and 15% of the irrigated GWP impact. For irrigated maize, fertilizers were also the main contributor to the CF, accounting for 53% of the total GWP. The total amount of irrigation was calculated to be 720.4 kg CO2-eq ha−1, constituting approximately 15.6% of the overall global warming potential attributed to irrigated maize cultivation. The role of irrigation in total CF was estimated at 18% in Northern Italy (Fantin et al. 2017) and 23% in Albania (Canaj & Mehmeti 2022). The majority of the existing literature examining the environmental impacts of grain maize production using LCA has consistently indicated that nitrogen fertilization and mechanized processes have a substantial influence on the size of the CF. Nitrogen fertilization induced 38% of CF impacts in Albania (Canaj & Mehmeti 2022), 55–86% in the United States (Kim et al. 2014), higher than 65% in Northern Italy (Fantin et al. 2017), higher than 75% in northern China (Zhang et al. 2018), 72% in Canada (Jayasundara et al. 2014), and 79.4–84.6% in Poland (Holka & Bieńkowski 2020). Our findings well agree with the literature emphasizing that fertilizer production and transportation, nitrogen fertilizer application, and diesel fuel are key contributors to GHG emissions for both rainfed and irrigated systems.
Figure 5

The share of process and inputs to global warming potential of rainfed and irrigated maize production.

Figure 5

The share of process and inputs to global warming potential of rainfed and irrigated maize production.

Close modal

WSF assessment

Following the AWARE method, the water scarcity profile was 1,566 and 5,613 m3 world eq ha−1 for rainfed and irrigated maize, respectively. This is equal to 0.251 and 0.555 m3 world eq kg−1. Analysis of the WSF indicates that despite achieving higher productivity when evaluating water footprint per unit of product, the WSF associated with irrigated maize is exceedingly high. This indicates that the increased environmental impact with water use in the case of irrigated maize cannot be fully offset by the gains in productivity. For rainfed maize, the highest WSF occurs (Figure 6) with fertilizers at 1,007 m3 world eq ha−1 (0.167 m3 world eq t−1) followed by mechanization at 553.5 m3 world eq ha−1 (0.089 m3 world eq t−1) and plant protection at 14.76 m3 world eq ha−1 (0.0024 m3 world eq t−1). In contrast, for irrigated maize, the majority of the total WSF, accounting for 72%, is associated with irrigation. Specifically, agricultural water consumption for irrigation dominates the overall water scarcity profile, constituting 64% of the total WSF. The WSF of maize was reported at 1,090–8,280 world m3eq t−1 in Thailand (Giusti et al. 2022). Large variability in potential water scarcity impacts exits. Within the same crop, the WSF can vary by more than three orders of magnitude across countries (Boulay et al. 2019).
Figure 6

The share of process and inputs to water scarcity footprint of rainfed and irrigated maize production.

Figure 6

The share of process and inputs to water scarcity footprint of rainfed and irrigated maize production.

Close modal

Eco-efficiency analysis

The LCC inventory and economic analysis (as outlined in Table 8) unveiled a clear trend. This trend indicated that GVP and overall costs tended to rise as the level of resource-intensive inputs increased. In other words, more resources and inputs invested in the agricultural process result in the higher production value and costs. The net returns were 1,528 € ha−1 for rainfed and 1,591 € ha−1 for irrigated maize. The findings indicate that even though there was a notable increase of 27.5% in terms of maize yield in the irrigated system compared to the rainfed system, the net financial return from irrigated maize cultivation is only slightly higher, specifically by just 5%, when compared to rainfed maize cultivation. In other words, the substantial yield increase achieved through irrigation did not translate into a proportionally larger financial benefit to cover the incremental GHG emissions caused by higher water and energy resource input. As a result, rainfed cultivation exhibited a 13.5% higher BCR. Rainfed maize cultivation exhibited a 17% higher level of eco-efficiency when assessed based on GHG emissions and 40.7% for energy use. In the context of water scarcity, rainfed maize cultivation increased with a 70% higher eco-efficiency.

Table 8

Economic indexes and eco-efficiency score of rainfed and irrigated maize in BiH

IndicatorUnitRainfedIrrigated
GVP € ha−1 5,114 8,409 
Variable cost € ha−1 2,869 5,454 
Total cost of production € ha−1 3,586 6,817 
GR € ha−1 2,245 2,955 
NR € ha−1 1,528 1,591 
BCR – 1.43 1.23 
Eco-efficiency score € kg CO2-eq−1 0.54 0.45 
Eco-efficiency score € m−3 world eq 0.98 0.28 
Eco-efficiency score € MJ−1 0.077 0.046 
IndicatorUnitRainfedIrrigated
GVP € ha−1 5,114 8,409 
Variable cost € ha−1 2,869 5,454 
Total cost of production € ha−1 3,586 6,817 
GR € ha−1 2,245 2,955 
NR € ha−1 1,528 1,591 
BCR – 1.43 1.23 
Eco-efficiency score € kg CO2-eq−1 0.54 0.45 
Eco-efficiency score € m−3 world eq 0.98 0.28 
Eco-efficiency score € MJ−1 0.077 0.046 

In the context of Bosnia and Herzegovina, maize (Zea mays L.) holds a position of strategic importance within the agricultural landscape. It serves as a key crop that plays a vital role in the country's agricultural sector and overall economy. Sustainable intensification of cereal production is a critically important topic worldwide, including BiH. Therefore, there exists a pressing necessity to meticulously evaluate eco-efficiency and sustainability of diverse maize cropping systems to identify sustainable and eco-efficient cropping practices. In this research, we conducted an integrated environmental and economic assessment of irrigated and rainfed maize systems in Bosnia and Herzegovina. We conducted an LCA-based energy analysis, CF, and WSF assessment and used LCC to estimate life cycle costs. The findings indicate that the transition from rainfed maize cultivation to irrigation is linked to a substantial increase in energy input, amounting to a 69.8% increase (from 19,819 to 34,837 MJ ha−1), and a corresponding 22% increase in GHG emissions intensity, with levels rising from 2,833.75 to 3,554.1 kg CO2-eq per hectare. The WSF of irrigated maize was 3.6 times higher for each hectare cultivated. However, it is important to note that the irrigated system generates 1.53 times larger crop yields compared to rainfed cultivation, resulting in a higher gross value of production and NR. In agricultural systems, a notable trade-off exists between high-yielding practices that result in greater crop production per hectare and a lower environmental impact per ton of produced crop due to the economies of scale associated with higher yields. Our research findings reveal a consistent pattern: irrigated agricultural systems consistently exhibit lower CF per ton of grain produced when compared to their rainfed counterparts. Specifically, the CF of irrigated maize per unit of output is notably reduced by 24%. This is evident in the data, with values of 359.3 kg CO2-eq per ton for irrigated maize in contrast to 471.07 kg CO2-eq t−1 for rainfed maize. Conversely, our investigation also highlights that several other crucial factors demonstrate a distinct trend. Rainfed systems tend to outperform irrigated systems in terms of energy indices (efficiency, productivity, and profitability), product WSF, and overall eco-efficiency. This suggests that the increased environmental burden, including energy consumption and water scarcity, outweighs the benefits of reduced carbon emissions in terms of eco-efficiency. Both rainfed and irrigated maize production rely heavily on nonrenewable energy sources, with fertilization, irrigation, and mechanization playing substantial roles in energy consumption, GHG emissions, and water scarcity. Implementing optimization strategies for these inputs is critical to mitigate environmental impacts and enhance sustainability in maize farming in Bosnia and Herzegovina.

This pioneering study in Bosnia and Herzegovina (BiH) employs a life cycle thinking approach to analyze crop production systems, providing valuable insights into agricultural sustainability. It reveals that diverse agricultural systems have unique strengths and weaknesses, highlighting the complexity of sustainability in agriculture. The results have evidenced that the simultaneous consideration of multiple models and indicators, combining crop modeling, LCA, and LCC, can help to identify improved farm management practices. Future research should explore additional environmental footprint indicators and the interplay of environmental, economic, and social factors. It is crucial to study the impact of water-saving and nutrient-efficient strategies for more informed decision-making and sustainable intensification in BiH and beyond.

This research was supported by SmartWater project (Promoting SMART agricultural WATER management in Bosnia and Herzegovina) funded from the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No 952396). We thank colleagues from University of Banja Luka for supporting us with data collection.

Andi Mehmeti worked on conceptualization, formal analysis, methodology, resources, software, visualization, writing – original draft, and writing – review & editing. Mladen Todorovic focused on conceptualization, resources, supervision, validation, and writing – review & editing. Ivana Mitrovic and Mihajlo Marković rendered support in data curation, formal analysis, investigation, methodology, software, validation, and writing – review & editing.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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