A farm-gate life cycle assessment (LCA) of energy use and greenhouse gas emissions in rice production under acidic soil conditions in Malaysia

Abstract

Acidic soils constrain rice production in Malaysia by reducing yields and increasing input intensity, which affects energy usage and greenhouse gas (GHG) emissions. This study applied a manual life cycle assessment (LCA) framework to evaluate energy demand and GHG emissions under standard (pH > 5) and acidic (pH < 5) soil conditions in the Integrated Agricultural Development Area (IADA), Seberang Perak, Malaysia. Liming under acidic conditions increased total energy consumption by 2 % (23,500 MJ/ha) and GHG emissions by 139 % (2,326 kg CO₂-eq/ha). The regression analysis revealed high associations between soil pH, yield (R² = 0.81), energy efficiency (R² = 0.81), and GHG intensity (R² = 0.72), indicating soil conditions highly influenced sustainability performance.

1. Introduction

Rice (Oryza sativa) is the staple food for more than half of the world population, with 90 % of total production coming from Asia (Fukagawa et al., 2019). The global rice production is expected to increase by 34 % by 2050 (Alexandratos et al., 2012; Tesfaye et al., 2021), raising sustainability concerns with the high demand as the world population grows. In Malaysia, domestic rice production remains below the self-sufficiency level (SSL) (67–70 %) despite being a strategic commodity for national food security (Dorairaj et al., 2023). This shortage has resulted in reliance on rice imports from neighboring countries (Thailand and Vietnam), leaving the country vulnerable to global supply disruptions and unstable market trends.

Acidic soils have emerged as a critical global challenge that threatens agricultural productivity, including rice production, through the degradation of nutrient availability and agroecosystems (Regasa et al., 2025). Soil acidity varies in intensity and is widely distributed globally, covering significant areas of arable land. These soils are found in the southern belt of North and South America, Russia, the northern belt of South Asia, South Africa, Australia, and parts of New Zealand (Edna et al., 2022). Within this global context, Malaysia faces difficulty due to the limited rice-growing land base. As the smallest total rice cultivation area in Southeast Asia, approximately around 689,268 ha (Firdaus et al., 2020), acidic soil conditions pose significant challenges to national rice productivity and sustainability.

In tropical regions such as Malaysia, the acidic soils are primarily driven by the natural weathering process caused by high temperatures and year-round rainfall (Anda et al., 2008). In coastal plains, soil acidity further declines to below pH 4 due to the oxidation of pyrite-rich sediments, resulting in the formation of acid sulphate soils (Jamaludin et al., 2026). Approximately 20,000 ha of acid sulphate soil are currently used for rice cultivation in Peninsular Malaysia (Halim et al., 2018). Despite the differences in formation processes, both soils are highly acidic (pH < 5). This unfavorable condition increases the solubility of toxic elements such as aluminum (Al) and iron (Fe) (Ginocchio et al., 2009), while promoting leaching losses of base cations such as calcium (Ca²⁺), magnesium (Mg²⁺), potassium (K⁺), and sodium (Na⁺). Consequently, farmers often face challenges such as nutrient deficiencies, poor crop growth, and reduced yields (Agegnehu et al., 2019; Poschenrieder et al., 2008), necessitating higher agricultural inputs for sustainable production.

Numerous paddy fields in Malaysia, particularly in coastal regions, are developed on acidic or acid-sulfate soils due to the limited suitable arable land. As a result, rice production on these soils relies on adaptive management practices instead of land substitution. The adaptive management includes liming, an effective agronomic practice to reduce acidity and enhance crop productivity in marginal acidic soils (Shoghi Kalkhoran et al., 2021; Passos et al., 2019). Ground magnesium limestone (GML) is commonly applied to supply Ca and Mg, while alleviating Al and Fe toxicity. Furthermore, this measure stimulates soil microbial activity to increase nutrient mobilization (Panhwar et al., 2015). Although the benefits of liming for soil fertility are well established, the energy and environmental implications under field-scale rice production have not been systematically evaluated. Moreover, this activity remains an energy-intensive operation that increases energy demand, contributing to higher carbon dioxide (CO₂) emissions (Page et al., 2009).

Earlier studies focused mainly on agronomic performance and soil chemical improvement in Malaysia (Halim et al., 2018; Edwards et al., 1991; Mahmud et al, 2022; Shamshuddin et al, 2011), while few analyzed the energy efficiency and GHG emissions of rice cultivation on acidic soils. The life cycle assessment (LCA) provides a comprehensive framework to quantify energy use and GHG emissions across production systems. Nonetheless, existing LCA applications mostly rely on software-based modelling, which may be less accessible to field practitioners. This limitation highlights the need for a simple, transparent, and farmer-friendly LCA framework to assess the sustainability implications of soil acidity and liming practices in rice cultivation.

In this study, the influence of acidic soil conditions on energy efficiency and GHG emissions in Malaysian rice cultivation was evaluated using the manual LCA approach. A comparative study was performed between standard (pH > 5) and acidic conditions (pH < 5). The specific objectives of this study were as follows.

(1) To quantify and compare energy inputs, outputs, and efficiency under the two soil conditions.

(2) To estimate farm-gate GHG emissions and emission intensity.

(3) To identify key hotspots for management options to improve agronomic and environmental outcomes.

2. Materials and methods

2.1. Research location

This study was conducted at the integrated agricultural development area (IADA) Seberang Perak, located in the state of Perak in the northwest of Peninsular Malaysia (see Fig. 1 (a)). This IADA is one of the main granaries in Malaysia under the national agrofood policy (NAP), covering approximately 14,400 ha of rice fields (see Figs. 1 (b) and (c)) and Table 1 (Google Earth, 2026; IADA, 2021).

(a) Perak state, Malaysia

(b) IADA Seberang Perak, Perak state, Malaysia

(c) Distributions of paddy rice plots at IADA Seberang Perak

Fig. 1 Study site

Table 1 General information of the IADA Seberang Perak, MALAYSIA

No.	Item	Detail
1	Area	14,140 ha
2	No. of farmers	5,546
3	Soil type	1) Marine alluvium
		2) Riverine
4	Soil pH	3.8–4.8
5	Soil cation exchange capacity (CEC)	13–25.89 cmol (+)/kg
6	Lime type	GML
7	Liming operation	Once every 2.5 or 3 years
8	Cropping intensity	Twice per year

2.2. LCA framework

The assessment was conducted in accordance with the LCA framework outlined in ISO (international organization for standardization) 14040/14044 (ISO 14040, 2006; ISO 14044, 2006), with a simplified methodological approach to facilitate farm-level implementation. This evaluation comprised four main phases: (i) goal and scope definition; (ii) life cycle inventory (LCI) and data collection; (iii) life cycle impact assessment (LCIA); and (iv) result interpretation. The research flow is illustrated schematically in Fig. 2.

2.2.1. Goal and scope definition

This study aimed to estimate the impact of acidic soil conditions on energy efficiency and GHG emissions in rice cultivation systems. The LCA system boundary covered five major stages in rice cultivation, including land preparation, vegetative, reproductive, ripening, and harvesting. Functional units used in this study were defined as (i) 1 kg of paddy rice and (ii) 1 ha of paddy rice.

Fig. 2 Research flow

2.2.2. Data collection and LCI analysis

The LCI was developed using a combination of primary and secondary data. First, the primary data on the 2023/2024 cultivation year were obtained from officers at the IADA Seberang Perak via structured interviews, field records, and technical documentation. The primary data included the quantity and frequency of farm inputs such as fuel, seed, fertilizer (nitrogen [N], phosphate [P], potassium [K]), agrochemicals (pesticides, herbicides, insecticides, fungicides, rodenticides, molluscicides), labor requirement, and soil amendment practices, particularly the application of GML. These data represented field-level operational practices and input application rates under local rice production conditions. The secondary data was obtained from official government reports, published literature, and Malaysian agricultural standards guidelines. These data were used to supplement missing parameters, validate primary data, and provide information such as default energy conversion and emission factors.

In this study, the term “acidic soil” refers to soil with a pH < 5, which necessitates soil amendment treatment. A comparative study was then performed between acidic soil with pH < 5 and standard, which is less acidic soil with pH > 5. Both soils are managed under the same regime, except that liming is only applied to acidic soils to analyze its impact on rice production.

2.2.3. LCIA

Two environmental indicators were evaluated in the LCIA: Energy demand (MJ/unit), representing the energy input-output along the cultivation processes, and GHG emissions (kg CO₂-eq/unit), representing emissions associated with farm inputs. These indicators were selected to reflect the sustainability implications of acidic soil and liming practices.

2.3. Manual calculation approach

Energy input-output analysis was conducted to quantify the total energy input and output of rice production under standard and acidic soil conditions. All farm inputs were converted into energy units (MJ) using established energy conversion factors adopted from established literature. The embodied energy of agricultural inputs is quantified based on the cumulative energy required for their production, processing, and transportation. A summary of the energy conversion factors is provided in Table 2.

Table 2 Energy conversion factor (MJ/unit) for rice farming

Category	Input system	Unit	Energy conversion (MJ/unit)	References
Machinery	Tractor	kg	96.61	Muazu et al., 2015; Canakci et al., 2005
	Combine harvester	kg	87.63	Muazu et al., 2015; Canakci et al., 2005
	Others	kg	62.7	Muazu et al., 2015; Canakci et al., 2005
Fuel	Diesel	L	56.31	Hosseini et al., 2014; Devasenapathy et al., 2009
	Gasoline	L	48.23	Hosseini et al., 2014; Devasenapathy et al., 2009
Seed	Seed	kg	14.7	Hosseini et al., 2014; Pishgar-Komleh et al., 2011
Soil amendment	Limestone	kg	0.167	Guareschi et al., 2020, 2021
Fertilizers	Nitrogen	kg N	60.6	Kargwal et al., 2022
	Phosphate	kg P	11.1	Kargwal et al., 2022
	Potassium	kg K	6.7	Kargwal et al., 2022
Agrochemical (Active ingredient)	Pesticides	kg	120	Devasenthapaty et al., 2009; Mohammadi et al., 2008
Manpower	Labor	h	1.96	Jafrodi et al., 2022; Banaeian et al., 2011
Yield	Grain	kg	17	Jafrodi et al., 2022; Šarauskis et al., 2018; Pishgar-Komleh et al., 2011

The individual energy input calculations are provided in the supplementary materials. Total energy input was calculated as the sum of energy equivalents of all inputs used during the cultivation period, including seed, fertilizers, pesticides, GML, fuel, machinery, and labor, as expressed in Eq. (1).

  

$$\mathrm{TEI}=\sum _{i=1}^n(Q_i\times {\mathrm{CF}}_i)$$(1)

Where TEI is total energy input, n is the total number of input categories, Q_i is the quantity of input i per ha, and CF_i is the corresponding energy conversion factor (MJ/unit). Energy output was calculated based on yield using the energy equivalent of a rice grain, as expressed in Eq. (2).

  

$$\mathrm{TEO}=y\times {\mathrm{CF}}_{\mathrm{g}}$$(2)

Where TEO is the total energy output; y is the rice yield (kg/ha); and CF_g is the energy conversion of rice grain (MJ/kg).

2.4. Energy indices

Total energy input, output and yield were used to calculate energy indices, such as energy use efficiency (EUE), energy productivity (EP), specific energy (SE), and net energy (NE) for each soil condition (see Eqs. (3)–(6)) (Jafrodi et al., 2022). The results reflected the impact of energy input and output per hectare of rice farming on acidic soil conditions.

  

$$\mathrm{EUE}=\frac{E_{\mathrm{in}}}{E_{\mathrm{out}}}$$(3)

  

$$\mathrm{EP}=\frac{y}{E_{\mathrm{in}}}$$(4)

  

$$\mathrm{SE}=\frac{E_{\mathrm{in}}}{y}$$(5)

  

$$\mathrm{NE}=E_{\mathrm{out}}-E_{\mathrm{in}}$$(6)

Where E_in is the energy input (MJ/ha), E_out is the energy output (MJ/ha), and y is the rice yield (kg/ha).

The energy was then classified as direct, indirect, renewable, and non-renewable energy to identify dominant energy sources. The results also highlight the differences in energy dependency between standard and acidic soil management.

2.5. GHG emissions analysis

This study focused on GHG emissions from farm input such as machinery, fossil fuels, chemical fertilizer, and agrochemicals. The GHG emissions were estimated using the same method as energy inputs, by converting physical quantities of agricultural inputs into a common unit (kg CO₂-eq) using established emission conversion factors. The amount of GHG emissions was obtained by multiplying the input rates by the corresponding emission conversion (see Table 3).

Table 3 Farm input-derived emissions (kg CO₂-eq/unit) for rice production

Category	Input system	Unit	Emission conversion (kg CO2-eq/unit)	References
Machinery	All machinery	MJ	0.071	Dyer et al., 2006
Fuel	Diesel	L	2.76	Ekinchi et al., 2020; Eren et al., 2019; Clark et al., 2016
	Gasoline	L	2.32	Nikkhah et al., 2015; EPA, 2015
Seed	Seed	kg	1.5	Rahman et al., 2019
Soil amendment	Lime (GML)	kg	0.44	Rahman et al., 2019; De Klein, 2006
Fertilizers	Nitrogen	kg N	1.3	Lal, 2004
	Phosphate	kg P	0.2	Lal, 2004
	Potassium	kg K	0.15	Lal, 2004
Agrochemicals (active ingredients)	Insecticides, Fungicides, Herbicides, Rodenticides Molluscicides	kg	25	Audsley et al., 2009
Manpower	Labor	h	0.7	Hosseini et al., 2024; Singh et al., 1994

EIA: energy information administration, GML: ground magnesium limestone.

2.6. GHG intensity (GHGI)

The GHGI was determined through emissions emitted by yield (kg CO₂-eq/ kg), calculated using the formula by Eren et al., 2019 (see Eq. (7)).

  

$$\mathrm{GHGI}=\frac{{\mathrm{GHG}}_{\mathrm{ha}}}{y}$$(7)

Where GHG_ha refers to the amount of emissions (kg CO₂-eq/ha), and y is the yield (kg/ha).

2.7. Regression analysis

Simple linear regression was performed using the ordinary least squares (OLS) method by Montgomery et al. (2021). A total of 30 data sets collected from farmers were used (n = 30), comprising standard (n = 15) and acidic conditions (n =15). The analysis was conducted to estimate mean differences in yield, energy efficiency, and greenhouse gas intensity (GHGI) between the two soil condition groups using Eq. (8).

  

$$Y={\beta }_0+{\beta }_{1}x+\epsilon$$(8)

In this model, x represented the independent variable (soil pH), while Y was the dependent variable (yield, energy efficiency or GHGI). The parameters, β₀ and β₁, correspond to the intercept and regression coefficient for soil pH, respectively, and ε is the error component.

The regression was implemented using the statsmodels module in Python (Google Colab v3.10 [Google, 2025]). Soil condition was introduced as a binary dummy variable, where acidic soil (pH < 5) was coded as 0 and standard soil (pH > 5) as 1. Accordingly, the intercept (β₀) represented the expected value under acidic soil conditions, and β₁ captured the change associated with standard soil conditions. The statistical measurements, R², coefficient, and p-values, were used to assess the significant effect of soil pH on sustainability indicators.

3. Results and discussion

3.1. Energy consumption

Energy input distribution (MJ/ha) and contribution (%) of farm inputs under standard and acidic soil conditions are presented in Table 4. Granaries in Malaysia adhere to the national agrofood policy (NAP) and follow the Malaysian government’s standard operating procedures (SOPs), which are adapted to regional conditions for optimal and consistent operation. In this study, fertilizers, pesticides, and seed rates were equal in both soil conditions. Thus, differences in energy use between the systems can be attributed to soil-specific management (liming operation) instead of variation in agronomic inputs.

Table 4 Energy input distribution (MJ/ha) and contribution (%) of farm inputs under standard and acidic soil conditions

Items	Unit	Energy input standard (MJ/ha)	Contribution (%)	Energy input acidic (MJ/ha)	Contribution (%)
Nitrogen	kg	7,490	33	7,490	32
Gasoline	L	4,399	20	4,399	19
Diesel	L	3,660	16	4,223	18
Seed	kg	2,352	10	2,352	10
Pesticides	kg	1,930	9	1,930	8
Phosphate	kg	819	4	819	3
Machinery	kg	898	4	972	4
Potassium	kg	614	3	614	3
GML	kg	0	0	501	2
Labor	h	192	1	207	1
Total		22,354	100	23,507	100

GML: ground magnesium limestone.

The total energy input was slightly higher under acidic soil conditions (23,507 MJ/ha) compared to standard conditions (22,353 MJ/ha), representing an increase of 2 %. This increase was driven by liming operations required to correct soil acidity, including the applications of GML (501 MJ/ha), additional machinery (972 MJ/ha), higher diesel consumption (4,223 MJ/ha), and increased labor input (207 MJ/ha). Although modest in absolute terms, these additional inputs imposed a significant energy burden on acidic soil conditions.

Regardless of soil conditions, nitrogen fertilizer represents the largest energy consumption (32–33 % of total energy input) due to the high energy intensity of nitrogen fertilizer production (61 MJ/kg), which is largely driven by the Haber–Bosch production process (Ghavam et al., 2021; Modak, 2002; Nguyen et al., 1995). This outcome aligns with previous rice energy analyses (Elsoragaby et al., 2019; Nabavi-Pelesaraei et al., 2019; Vahedi, 2021), confirming nitrogen fertilizer as a critical hotspot in rice cultivation systems and a major contributor to cumulative energy demand irrespective of soil conditions. Under acidic soils, reliance on synthetic fertilizers may exacerbate soil degradation and nutrient inefficiencies, highlighting the importance of reducing fertilizer-related energy burdens. Integrating organic fertilizers into conventional nutrient management offers a promising strategy to lower energy consumption while simultaneously improving soil health, particularly in acidic rice cultivation systems.

Fuel (gasoline and diesel) ranked second and third in energy consumption under both conditions (16–20 %), which is consistent with previous studies (Nayak et al., 2023; Pishgar-Komleh et al., 2011). Gasoline consumption was primarily associated with small-scale portable equipment, such as knapsack sprayers for fertilizer and pesticide application. Meanwhile, diesel consumption was linked to large machinery operations, including land preparation, liming, and harvesting, which was higher under acidic soil conditions due to the additional liming and soil preparation activities. Given that tillage operations account for more than 50 % of total energy consumption in rice systems (Kakraliya et al., 2022), optimizing machinery represents a key opportunity to reduce the energy burden. In acidic soils, the adoption of minimum tillage practices combined with optimized lime application rates enhanced crop productivity while lowering energy requirements (Wakwoya et al., 2022).

Despite the slightly different total energy input between standard and acidic soil conditions, the underlying energy structure varied. Acidic soils management introduced additional energy-intensive operations associated with soil amendment, fuel use, machinery operation, fuel consumption, and labor. In contrast, energy input under standard conditions was largely dominated by fertilizers and routine field operations, indicating more stable soil chemical properties that support efficient nutrient uptake and crop growth without additional soil correction.

3.2. Energy indices

The energy input was slightly higher in acidic rice management (51 %; 23,507 MJ/ha), compared to standard (49 %; 22,354 MJ/ha) (see Fig. 3). This result reflected the energy burden on acidic soils due to additional soil amendment activities. Nevertheless, the values for both soil conditions were lower than those reported in previous studies (Hosseini et al., 2024; Kakraliya et al., 2022; Yuan et al., 2017). The energy input reduction is primarily due to input based on management level, ensuring adherence to the SOPs. Besides, the absence of irrigation reduced additional energy for water, electricity, machinery, and labor (Bazilian et al., 2011; Singh et al., 2019). Mostly in Malaysia, water is directly sourced from surface water sources through a non-mechanized irrigation system. Nonetheless, the existing irrigation and drainage system requires constant monitoring to avoid soil acidity that can damage rice crops and reduce yields (DOA, 2024).

Fig. 3 Comparison of energy indices between standard and acidic conditions

Lower energy output was recorded under acidic soil conditions (38 %; 42,500 MJ/ha), compared to standard conditions (62 %; 68,000 MJ/ha), with a reduction of 24 %. This decline in energy output was directly associated with the lower yield in acidic soil (2,500 kg/ha), compared to standard soil (4,000 kg/ha). This finding indicated the negative impact of low soil pH on plant growth and productivity, primarily through nutrient deficiencies (Liang et al., 2023; Pan et al., 2020) and Al and Fe toxicity to crops (Ma et al., 2020). Overall, the lower energy output under acidic soils strongly influenced all subsequent energy indices (see Fig. 3).

The EUE ratio on acidic soil was 37 % (1.8), which was 26 % lower than standard soil conditions (63 %; 3.0). Differences in EUE are influenced by many factors, including soil conditions (Htwe et al., 2021), indicating that the energy demands in acidic soil conditions resulted in a lower return and production. Despite the lower EUE in acidic conditions, the value remained greater than 1, indicating the viability of cultivation on acidic soils. Simplified and reduced-input practices can lead to highly efficient energy use (Mandal et al., 2015). Thus, improving energy use under acidic conditions depends on optimizing high energy-intensity inputs, particularly fertilizers and fuels, while enhancing soil productivity.

Higher SE was recorded in acidic soils (63 %; 9.4 MJ/kg) compared to standard conditions, indicating that more energy was required to produce 1 kg of grain. This outcome reflected inefficient energy utilization under acidic soils, where substantial energy inputs did not contribute directly to grain production. Previous studies have shown that only part of the total energy input is effectively converted into yield, while the remainder is lost through residues, leaching, or emissions to soil, water, and air (Shafie, 2016). Optimizing farm inputs through improved nutrient management, such as the use of slow-release nutrients and organic amendments, can enhance nutrient use efficiency and reduce excessive fertilizer application. When combined with efficient machinery use, these strategies can lower SE requirements and improve overall energy performance in acidic rice cultivation systems.

The EP and NE were 38 % (0.1 kg/MJ) and 29 % (18,992 MJ/ha), respectively, under acidic conditions, which was lower than standard conditions. These outcomes can be attributed to the high input and low yield (Yuan et al., 2017). Both indices describe the ability of the system to convert energy input into productive output. As the soil pH decreases, additional energy is required for liming and other management, leading to non-optimal EP and NE and low yield. Therefore, optimizing liming activities by applying lime at the right time, rate, and technique can enhance crop yield (Enesi et al., 2023) and improve EP and NE values.

3.3. Energy distribution

The energy distribution analysis provides details on the composition and source of total energy use, categorized into (i) direct energy (DE) and indirect energy (IE), and (ii) renewable energy (RE) and non-renewable energy (NRE) (see Fig. 4).

(a) Direct, indirect energy

(b) Energy distributions for standard and acidic conditions

Fig. 4 Energy distributions for standard and acidic conditions

The DE (diesel, gasoline, GML, labor) was slightly higher under acidic conditions (9,330 MJ/ha), compared to standard (8,251 MJ/ha). The 6 % increase was contributed by liming activities that require additional machinery operation, fuel consumption, and labor input. Similarly, there was a marginal increase in IE (machinery, GML, seed, fertilizer, pesticides) by 2 % in acidic conditions (14,678 MJ/ha) compared to standard conditions (14,103 MJ/ha), supporting the need for additional inputs of GML, machinery, and labor to enhance the poor soil conditions.

The RE (seed, labor) was relatively equal in acidic (2,559 MJ/ha) and standard (2,544 MJ/ha) conditions, indicating a similar level of seed and labor usage for liming operations. This activity was performed once during land preparation and did not highly affect the total renewable energy input. Nevertheless, NRE sources such as machinery, fuel, GML, fertilizer, and pesticides were higher in acidic and standard conditions (20,948 MJ/ha and 19,810 MJ/ha), indicating a high reliance on synthetic and fossil-based inputs in both soil conditions for rice production on fossil-based (Chaudhary et al., 2017; Kakraliya et al., 2022; Lalik et al., 2015). Acidic soil management further exacerbated this dependence through corrective soil interventions.

Higher dependency on IE and NRE suggested an urgent transition towards optimized input and renewable-based practices. One of the main aspects of the energy transition and the advantage of renewable energies is the increased independence caused by reduced fossil fuel imports (Meschede et al., 2024). Besides, the application of precision machinery, efficient fuel management, and gradual substitution of chemical fertilizers with bio-based or organic amendments could substantially reduce non-renewable energy, aligning with SDG 7 (affordable and clean energy), SDG 2 (responsible consumption and production), and SDG 13 (climate action).

3.4 GHG emissions

The GHG emissions distribution (kg CO₂-eq/ha) and contribution (%) of farm inputs under standard and acidic soil conditions are presented in Table 5. Under standard soil conditions, total GHG emissions were 975 kg CO₂-eq/ha and relatively distributed among several inputs: seeds (25 %), gasoline (22 %), diesel (18 %), and nitrogen fertilizer (16 %). This finding indicated that routine rice cultivation practices arise primarily from seed production, fuel combustion, and fertilizer usage. Meanwhile, acidic soil management resulted in higher emissions (2,326 kg CO₂-eq/ha), more than twice the value under standard conditions. This increase was driven by the GML application (1,320 kg CO₂-eq/ha), accounting for 57 % of total emissions under acidic conditions and responsible for the elevated carbon footprint in rice cultivation. The strong influence of GML on emissions is consistent with previous findings, indicating that CO₂ from lime consisted of 62–70 % of total CO_2, and the application contributes approximately 0.12–0.18 kg of CO₂ per kg of lime applied (Kunhikrishnan et al., 2016).

Table 5 Distribution (kg CO₂-eq/ha) and contribution (%) of GHG emissions in farm inputs under standard and acidic soil conditions

Items	Unit	GHG emissions standard (kg CO2-eq/ha)	Contribution (%)	GHG emissions acidic (kg CO2-eq/ha)	Contribution (%)
GML	kg	0	0	1320	57
Seed	kg	240	25	240	10
Gasoline	L	212	22	212	9
Diesel	L	179	18	207	9
Nitrogen	kg	161	16	161	7
Labor	h	69	7	72	3
Herbicides	L	49	5	49	2
Fungicides	L	22	2	22	1
Potassium	kg	18	2	18	1
Phosphate	kg	15	2	15	1
Molluscicides	L	8	1	8	0
Insecticides	kg	1	0	1	0
Machinery	kg	< 1	0	< 1	0
Rodenticides	L	< 1	0	< 1	0
Total		975	100	2,326	100

GHG: Greenhouse gases, GML: Ground magnesium limestone.

High emissions from seeds were evident under both soil conditions, accounting for 10–25 % of total emissions. This activity represents typical farming practices, where high seed rates are used to ensure high plant growth and production. In addition, seed production is high-energy and emission-intensive due to fertilizer use, pesticide application, and diesel-powered machinery (Giuliana et al., 2024). These findings demonstrated the importance of optimizing soil conditions and seed rates, and seed production for agronomic efficiency and reducing upstream emissions.

The GHG emissions from fuel (gasoline and diesel) were high in both soil conditions, ranging from 9 % to 22 %. Gasoline emissions were associated with the fertilizer and pesticide applications using small-scale equipment, whereas diesel emissions were linked to larger machinery operations such as land preparation, harvesting, and liming (acidic conditions). Diesel emissions were slightly higher in acidic soils, reflecting the additional field operations required for soil amendment. As fossil fuel combustion is a major source of GHG emissions and contributes to the depletion of non-renewable resources, improving energy-efficient mechanization and adopting sustainable fuel management practices are essential strategies for reducing emissions in rice production systems.

Emissions were normalized per unit of yield through GHGI, and the results revealed that acidic conditions produced higher values (0.94 kg CO₂-eq/kg of rice grains) compared to standard soil (0.25 kg CO₂-eq/kg) (see Fig. 5). In ideal conditions, crop yields are higher, and GHGI is lower (Iboko et al., 2023; Miao et al., 2023). Acidic soil limits nutrient availability and root development, reducing yield and increasing emission intensity when calculated per kilogram of rice produced. These findings demonstrated the strong influence of soil properties on emission efficiency in rice production systems. Despite the benefits of lime application in improving soil chemical conditions, the associated emissions and low production increased emission intensity.

Fig. 5 Comparison of GHGI of standard and acidic conditions

According to previous studies, conservation cropping systems can reduce GHG emissions and energy inputs without compromising yield (Dey et al., 2024). In acidic rice cultivation systems, reducing reliance on high-emission inputs such as GML through the integration of organic amendments, improved residue management, and enhanced soil biological activity offers a potential pathway to simultaneously lowering GHGI and improving productivity. Such integrated approaches present a win–win opportunity by enhancing emission efficiency while supporting sustainable rice production under challenging soil conditions.

3.5. Regression analysis

The regression analysis showed that soil condition highly influenced all sustainability indicators (see Table 6). Relative to the acidic soil (pH < 5), the standard soils (pH ≥ 5) were associated with higher system performance across all indicators.

Table 6 Regression analysis of soil condition effects on rice yield, energy efficiency, and GHG emission intensity

Outcome Variable	Unit	Intercept (β0) (Acidic soil)	Soil condition (β1) (Standard vs acidic)	R2	p-value
Yield	kg/ha	2,430.00	731.07	0.81	< 0.001
Energy efficiency	MJ/kg	1.36	0.37	0.81	< 0.001
GHG intensity	kg CO2-eq/kg	0.77	−0.55	0.72	< 0.001

Rice yield increased by 731 kg/ha under standard soil conditions, indicating that soil acidity remains a major constraint on biomass formation and crop productivity. This improvement in yield enhanced energy use efficiency, which increased by 0.37, reflecting more effective conversion of energy inputs into agricultural output under favorable soil conditions. In contrast, GHG emission intensity declined by 0.55 kg CO₂-eq/kg when moving from acidic to standard soils, demonstrating that emission efficiency was strongly influenced by yield level and inputs.

The strong R² values (0.71–0.81) demonstrated that soil condition highly effected the variation in system performance. These results consistence with the LCA findings that acidic soil management required more energy, particularly lime. Nonetheless, crop yield remains lower, resulting in reduced energy efficiency and increasing emissions. The regression analysis supported the analytical results of the manual LCA by demonstrating clear differences in sustainability performance between acidic and standard soil conditions.

4. Limitations of the study

The current study has several limitations. First, this study focused on the management level instead of individual farmer practices. The findings reflected the standardized input application based on national guidelines and research location, which may differ and be lower than other studies across different regions in Malaysia. Second, the study scope was limited to the farm gate. Thus, further investigations using LCA are necessary in other phases of rice production. Third, this manual LCA framework emphasized the embedded CO₂ emissions from farm inputs, excluding methane (CH₄) and nitrous oxide (N₂O) emissions. This scoping decision reflected the objective of developing a simplified and operationally accessible assessment tool that can be implemented directly by farmers, extension officers, and field practitioners without reliance on specialized LCA software or complex gas flux modelling. Therefore, future research should integrate CH₄ and N₂O quantification using intergovernmental panel on climate change (IPCC) tier 1 emission factors or field-based measurements for more comprehensive environmental assessments.

5. Conclusions

This study utilized a transparent manual LCA to compare energy use and GHG emissions in rice production on standard (pH > 5) and acidic conditions (pH < 5) in IADA Seberang Perak, Malaysia. Despite recording only 2 % increase in total energy use due to liming operation in acidic conditions, the GHG emissions rose by 40 % (2,326 kg CO₂-eq/ha compared to 975 kg CO₂-eq/ha). While GML contributed to a small fraction of total energy input, the production and application were associated with high CO₂ emissions, leading to a disproportionate increase in total GHG emissions under acidic soil conditions. The lower rice yield (2,500 kg/ha compared to 4,000 kg/ha) also reduced energy indices performance on acidic soil conditions. Nevertheless, the energy efficiency values indicated that cultivating on acidic soils is feasible and can be enhanced. Meanwhile, the regression results (R² = 0.72–0.81) confirmed that soil pH significantly impacted the sustainability of rice production. These findings underline the need for integrated soil management beyond GML to support low-carbon rice policy and productivity goals in Malaysia.

Acknowledgments

The author acknowledges the financial support from the Monbukagakusho MEXT Scholarship and Kyoto University School of Platform (KUSP), Japan. The author would also like to extend their appreciation to Universiti Putra Malaysia (UPM), IADA Seberang Perak, the Ministry of Agriculture and Food Security Malaysia, and the Department of Agriculture (DOA), Malaysia, for providing research facilities, data access, and technical support for this study.

Nomenclature

Symbol/Term	Description	Unit
GML	Ground Magnesium Limestone	kg
TEI	Total energy input	MJ/ha
TEO	Total energy output	MJ/ha
EUE	Energy use efficiency	ratio
EP	Energy productivity	kg/MJ
SE	Specific energy	MJ/kg
NE	Net energy	MJ/ha
DE	Direct energy	MJ/ha
IE	Indirect energy	MJ/ha
RE	Renewable energy	MJ/ha
NRE	Non-renewable energy	MJ/ha
GHG	Greenhouse gas emissions	kg CO2-eq/ha
GHGI	Ghg intensity	kg CO2-eq/ha
y	Yield	kg/ha
Y	Dependent variable	–
β0	Regression intercept	–
β1	Regression coefficient (soil condition)	–

Appendix A. supplementary data

The supplementary data, Equations and Table in this article are published in J-STAGE Data.

Declaration of competing interest

The authors declare no competing financial interests or personal relationships that may have influenced the work reported in this paper.

Notes

(URLs on references were accessed on 2 June 2026.)

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