Energy Use Efficiency and Carbon Footprint of Inorganic Fertilizer and Liquid Animal Manure in Maize Production Under Semi-Arid Conditions

Abstract

Improving energy efficiency and reducing the carbon footprint of crop production are critical for sustainable agriculture, particularly in semi-arid regions where resource use efficiency is essential. This study evaluated the effects of different fertilization strategies on energy use efficiency and carbon footprint in maize production. A field experiment was conducted during the 2023 growing season in Konya Province, Türkiye, using a randomized block design with three treatments and three replications. The treatments included an unfertilized control (U1), inorganic fertilizer application (U2), and liquid animal manure application (U3). The results showed that the highest grain yield was obtained in the liquid manure treatment, which was 2.08 times higher than the unfertilized treatment and 1.18 times higher than the inorganic fertilizer treatment. The highest total energy input was recorded in the inorganic fertilizer treatment (26,235.12 MJ ha⁻¹), while the highest total energy output was observed in the liquid manure treatment (203,154 MJ ha⁻¹). The liquid manure treatment also showed higher net energy efficiency, output–input ratio, carbon efficiency, and carbon sustainability index, while producing the lowest carbon footprint per unit of product. These findings indicate that liquid animal manure can improve maize productivity while enhancing energy efficiency and reducing carbon emissions in semi-arid agroecosystems.

1. Introduction

One of the fundamental challenges of global agriculture is to ensure food security for a steadily growing population while simultaneously reducing environmental pressures. Energy use in the agricultural sector depends on several factors, including the size of the agricultural workforce, the amount of arable land, input intensity in production, and the level of mechanization. Although agriculture is a vital sector for ensuring sustainable food security, its relative importance has been declining due to the rapid growth observed in the industrial and service sectors.

The rational management of natural resources represents a fundamental condition for achieving sustainability in agricultural production systems. Among these resources, energy efficiency represents a key pillar of sustainability in agriculture. Currently, global energy demand continues to rise, driven by demographic growth, the progressive limitation of arable land, and increasing expectations regarding living standards. Meeting the growing food demand has intensified agricultural practices, resulting in expanded use of chemical fertilizers, pesticides, mechanized equipment, and other resource inputs. Nevertheless, high energy consumption in agriculture generates environmental externalities that may compromise long-term sustainability. Greenhouse gas (GHG) emissions, particularly carbon-related emissions, are key contributors to climate change, and the agricultural sector represents a substantial source of these emissions. According to the United States Environmental Protection Agency (EPA), the “Agriculture, Forestry, and Other Land Use” sector was responsible for approximately 22% of global GHG emissions in 2019, arising from crop production, livestock activities, deforestation, and land use change processes [1]. Within the European Union, agriculture accounted for 10.8% of total GHG emissions, as indicated in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) [2]. In this context, the adoption of climate mitigation measures, combined with improved energy efficiency in agricultural systems, is essential for reducing GHG emissions, limiting natural resource depletion, and promoting environmentally sustainable agricultural development [3,4].

De Figueiredo et al. (2010) [5] investigated the relationship between sugarcane production and greenhouse gas emissions in Brazil. They reported emissions of 2406 kg of carbon dioxide equivalent (CO₂-eq) per hectare of cultivated land. A significant portion of total emissions (44%) was attributed to the burning of crop residues, approximately 20% to fertilizer use, and around 18% to fossil fuel consumption. Similarly, Pishgar-Komleh et al. (2012) [6] investigated energy consumption and CO₂ emissions in potato production in Iran. They reported total energy consumption as 47 GJ ha⁻¹ and GHG emissions as 992.88 kg CO₂-eq ha⁻¹. They also stated that the largest energy input was chemical fertilizers, accounting for 49% of total energy use, followed by seed input with a 24% share. They calculated specific energy consumption and energy efficiency as 2.12 MJ kg⁻¹ and 0.47 kg MJ⁻¹, respectively.

A comparison of energy input data over the past 30 years indicates a recent downward trend in energy input per unit of agricultural output. These findings confirm the economic feasibility of implementing energy-saving practices [7,8,9,10]. Input–output energy analysis is widely applied to evaluate the relationship between energy consumption and production performance across different crops. Numerous researchers have conducted energy and economic analyses to determine energy efficiency in crop production. For instance, Sartori et al. (2005) [11] studied soybean, maize, and wheat in Italy; Rafiee et al. (2010) [12] analyzed apple production in Iran; and Bojacá and Schrevens (2010) [13] examined coriander, lettuce, radish, and spinach in Colombia. Rajabi Hamedani et al. (2011) [14] analyzed the pattern of energy use and examined the correlation between energy inputs and potato yield in Hamadan province, Iran. The findings revealed that nitrogen fertilizer represented the dominant component of total energy consumption, accounting for 39% of overall input energy. Diesel fuel and seed inputs followed, contributing 21% and 14.9%, respectively, to the total energy requirement of the production system.

Although there has been substantial research on agricultural sustainability and energy use in agricultural production systems, studies specifically focused on determining the carbon footprint of agricultural production remain quite limited. The Central Anatolia region experiences very low annual precipitation (300–350 mm), with hot and dry summers and cold winters. In recent years, farmers in the region have tended to use resources excessively and inefficiently in pursuit of higher productivity, thereby accelerating soil degradation, particularly due to the overuse of chemical fertilizers and water.

In this study, the effect of using liquid animal manure as an alternative to chemical fertilizer on maize yield under semi-arid agricultural conditions was investigated. Furthermore, the study assessed the key determinants responsible for variations in energy consumption and environmental impacts across different fertilization approaches, while also identifying the technical measures necessary to mitigate their ecological footprint. To assess energy efficiency, input and output values obtained under field conditions were quantified using measured data, and the energy equivalents of the input and output components were determined based on established conversion coefficients. By integrating energy balance analysis with carbon footprint assessment under real field conditions, this research contributes to a better understanding of how alternative nutrient management practices can improve the environmental performance and sustainability of maize-based cropping systems in water-limited agroecological regions.

2. Materials and Methods

The study was conducted at the Sarıcalar Application and Research Farm of the Faculty of Agriculture, Selçuk University, Konya Province, located in the Central Anatolia region of Türkiye. To determine the physical properties of the soil, soil samples taken from each parcel were analyzed in the laboratory of the Soil Department, Faculty of Agriculture, Selçuk University. The soil properties of the trial area are given in

Table 1. Soil properties of the experimental area.

Specifications	Values
Clay (%)	40.6
Silt (%)	33
Sand (%)	26.4
Organic matter (%)	1.55
Volumetric density (g cm−3)	1.22
Soil penetration resistance (MPa) (0–20 cm)	0.69
Soil shear strength (Ncm−2)	1.03
Soil surface roughness (%)	7.05

. The parcel dimensions of all applications were arranged as 5 m × 50 m = 250 m².

The plots for each application were prepared and BORA F1 variety (Zea mays indentata) Hybrid Horsetooth Maize was planted at 70 × 20 planting density with a calculation of 71.420 seeds per hectare (ha). The three different applications that were tested are as follows:

U1: No application was made (control plot).

U2: Only mineral fertilizer was used, considering the amount of N, P and K required by the maize plant as 200 kg N ha⁻¹, 100 kg P₂O₅ ha⁻¹ and 80 kg K₂O ha⁻¹, respectively; half of the fertilizer was applied with planting, and the other half was applied during the second hoeing process.

U3: The nitrogen content of the liquid fertilizer was determined according to soil characteristics and the required amount of nitrogen (200 kg N ha⁻¹). Nitrogen was applied in three stages, in equal amounts in each stage: at a depth of 10 cm, after plant emergence, during intercropping, and at the last stage when fertilizer could be applied to the plants (Figure 1). The properties of liquid manure are given in

Table 2. Properties of liquid barn manure used in this study.

Specifications	Values
Volumetric weight (t m−3)	1.024
Viscosity (mm2·s−1)	1.49
pH	6.98
NH4-N (mg kg−1)	207.56
NO3-N (mg kg−1)	29.50
Total N (%)	0.85
C (%)	30.20
P (%)	0.10
K (%)	0.20

.

The applications were carried out with three replications. For all treatments, planting, irrigation, spraying, inter-row cultivation (hoeing), and harvesting were performed under the same standard conditions. Harvesting was conducted at the stage of physiological maturity, identified by the formation of the black layer at the point where the kernels attach to the cob, based on observations from five plants in each plot.

In the experimental plots, soil tillage and seedbed preparation were performed using a plow, cultivator, disk harrow, and roller. Planting was carried out using a four-row pneumatic precision planter. During the hoeing period, inter-row cultivation and fertilizer application were performed using an inter-row cultivator equipped with a fertilization unit. The main technical characteristics of the machinery used in the experiments are presented in

Table 3. Characteristics of the machines used in the trials.

Equipment Used in Applications	Working Width (mm)	Working Depth (mm)	Working Speed (kmh−1)
Plow	900	250	5
Cultivator	2180	120	7
Disc harrow	2100	100	6
Roller	2200	–	7
Liquid fertilizer injection machine	2100	100	4.5
Pneumatic precision planting machine	2800	50	7
Intermediate hoe	3010	50	4.5

.

For each application, an Aqua metro fuel consumption measuring device, model Contoil (Aqua metro Oil & Marine GmbH, Rostock, Germany), that can measure with 1 mL precision was used to determine the fuel consumption of the tractor.

2.1. Energy Input–Output Calculations

Seed energy input, fertilizer energy input, fuel-oil energy input, and labor energy input were calculated by multiplying the quantity of each input used per unit area by its corresponding energy equivalent coefficient. The energy equivalent values and resource coefficients adopted from previous studies are presented in

Table 4. Input and output equivalents of the energy used in maize production.

Parameters		Unit	Energy Equivalent (MJ Unit−1)	References
A. Input
Labor		h	2.3	[15,16]
Machine		h	121.3	[15,17]
Tractor		h	158.3	[15,17]
Fuel-oil		L	41	[18]
Electric		kWh	3.6	[19]
Fertilizer	N	kg	60.6	[20,21]
	P	kg	11.1	[22]
	K	kg	6.7	[23]
Herbicide		kg	254.45	[22,23]
Irrigation		m3	2.93	[24]
Seed		kg	15.3	[25,26]
A. Output
Yield		kg	14.7	[25,26]

.

For the calculation of human labor energy, one operator (driver) was considered in the analysis. The calculated energy indicators and the formulas applied in the analysis are provided in

Table 5. Energy parameters [27,28,29,30].

Parameters	Unit	Definitions
Total energy input	MJ ha−1	EI
Total energy output	MJ ha−1	E.O.
Net energy efficiency	MJ ha−1	Total energy output−Total energy input
Output/input ratio	%	Total energy output/Total energy input
Net energy ratio	%	Net energy efficiency/Total energy input
Energy efficiency	kg MJ ha−1	Grain yield/Total energy input
Energy required per unit product	MJ kg−1	Total energy input/Grain yield

.

Machine energy input was calculated with the following equation [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]:

$$\mathrm{ME}={\displaystyle \frac{\mathrm{A}·\mathrm{E}}{\mathrm{T}·\mathrm{C}}}$$

(1)

where

ME: Machine energy input (MJ ha⁻¹);

A: Weight of the tool (kg);

E: Production energy of agricultural machine or tool (MJ kg⁻¹);

T: Annual working hours (h);

C: Effective area capacity (ha h⁻¹).

2.2. Calculation of Carbon Footprint

Carbon equivalents (CEs) were obtained by multiplying the inputs given in

Table 6. Equivalent carbon emission estimates of agricultural inputs used in our study.

	Unit ha−1	kg CO2-eq ha−1	References
Labor	h	0.23	[32,33]
Tractor + machine	h	0.89	[32,33]
Fuel (diesel)	L	0.94	[34]
Irrigation	m3	0.17	[34,35]
N	kg	1.3	[33,34]
P	kg	0.2	[33,34]
K	kg	0.15	[33,34]
Herbicide	kg	1.5	[34]
Electric	kWh	0.0725	[34]
Seed	kg	0.32	[36]
Output (biological yield)	kg	0.44	[34]

by the respective carbon emission coefficients. Total carbon input and output were calculated as the sum of carbon equivalents of all inputs and outputs of each application.

According to Choudhary et al. (2017) [20], carbon output in crop production systems is estimated based on the total above-ground biomass produced. The methodology in Lal (2004) [34] assumes that dry plant biomass contains approximately 44% carbon and therefore carbon output is calculated by multiplying total biological yield (grain + straw) by a carbon fraction coefficient of 0.44.

This approach represents the amount of carbon accumulated in plant biomass during the growing period and is used as an indicator of carbon fixation within the production system. It does not represent net ecosystem sequestration but rather biomass-based carbon accumulation at field level.

Carbon output (kg CO₂-eq ha⁻¹) = Total biomass (grain yield + stem yield) × 0.44

(2)

C use efficiency (CE), C sustainability index (CSI) and C footprint (CF) were calculated using Equations (3)–(5), according to the methodology presented in [20]:

CF (kg CO₂-eq kg⁻¹ grain) = Total carbon input (kg CO₂-eq ha⁻¹)/Grain yield (kg ha⁻¹)

(3)

C = Total carbon output/total carbon input

(4)

CSI = (Total carbon output − total carbon input)/total carbon input

(5)

Energy and carbon inputs were calculated using standard energy equivalent and emission coefficients, which represent the upstream energy use and emissions embodied in each input. Each input was accounted for only once within the defined system boundary, and no overlap or duplication between direct and indirect energy components was allowed.

Statistical analysis was performed using a randomized complete block design with three replications. Data were subjected to one-way ANOVA, and treatment means were compared using Tukey’s HSD test at p < 0.05.

3. Results and Discussion

As a result of the experiments, 6640 kg ha⁻¹, 11,700 kg ha⁻¹ and 13,820 kg ha⁻¹ grain yields were obtained from U1, U2 and U3 applications, while biological yield values were determined as 13,978.95 kg ha⁻¹, 24,631.58 kg ha⁻¹ and 29,094.74 kg ha⁻¹, respectively.

With the data obtained from the experiments, the energy efficiency of maize production with different fertilizer applications was calculated and the results are given in

Table 7. Energy balance of the applications.

A-Inputs	U1		U2		U3
	MJ ha−1	%	MJ ha−1	%	MJ ha−1	%
Labor	18.13	0.15	18.13	0.07	23.47	0.09
Tractor	637.64	5.11	637.64	2.43	882.63	3.44
Machine	259.04	2.08	259.04	0.99	309.27	1.21
Fuel-oil	2101.75	16.86	2101.75	8.01	2396.45	9.35
Electric					153.36	0.60
Liquid manure					12,410.80	48.43
Inorganic fertilizer			13.766	52.47
Herbicide	203.56	1.63	203.56	0.78	203.56	0.79
Irrigation	8790	70.49	8790	33.50	8790	34.30
Seed	459	3.68	459	1.75	459.00	1.79
Total input	12,469.12	100	26,235.12	100	25,628.54	100.00
B-Output
Yield	97,608		174,342		203,154

.

The highest total energy input was obtained from the U2 application as 26,235.12 MJ ha⁻¹, while the highest total energy output was obtained from the U3 application as 203,154 MJ.ha⁻¹. The lower total energy input observed in the U1 application was mainly due to the absence of fertilizer use. In the U2 and U3 applications, fertilizer use increased the total energy input, and the high energy contribution of fertilizers reduced the relative share of other energy inputs within the total energy input. While the highest energy input was obtained from irrigation energy in the U1 application, the highest energy input rate was chemical fertilizer energy input in the U2 application as a result of the use of chemical fertilizers. The highest energy input rate was determined as irrigation energy in the U3 application, followed by liquid manure energy. In all applications, human labor, agricultural pesticides, machinery and seed energy inputs had the lowest rates.

Although the total energy input was high in the U2 application where chemical fertilizer was used and in the U3 application where liquid manure was used, the highest total energy output was obtained from the U3 application, followed by the U2 application. In the U1 application, the highest energy input was irrigation energy with a share of 70.49%, followed by fuel-oil energy with a share of 16.86%. In the U2 application, the highest energy input was chemical fertilizer energy with a share of 52.47%, followed by irrigation energy with a share of 33.50%. Konak et al. (2004) [37] reported a share of chemical fertilizer in energy inputs in maize production as 48.27%. Dilay (2021) [38] reported that the energy of chemical fertilizers had the highest share in energy inputs in maize production with 60.51%. In the U3 application, the highest energy input was obtained from liquid manure energy with a share of 48.43%, followed by irrigation energy with 33.50%. Although the liquid manure output in the U3 application had the highest share in the total energy input, it was lower than the share of the chemical fertilizer used in the U2 application in the total energy input. In all applications, fuel-oil energy was the energy input with the third-highest share. The highest fuel-oil energy input was obtained from U1 application with 16.86%. This was followed by U3 application with 9.35% and U2 application with 8.01% share. Plappally and Lienhard (2012) [39] highlighted that groundwater extraction in agricultural systems, particularly through pressure-driven pumping technologies, is characterized by substantial energy intensity. They further emphasized that the energy demand associated with irrigation practices differs depending on crop type, cultivated surface area, and the pressure levels required for water delivery.

The energy efficiency of the applications is given in

Table 8. Energy efficiency of applications.

Parameters	U1	U2	U3
Total energy input (MJ ha−1)	12,469.12	26,235.12	25,628.54
Total energy output (MJ ha−1)	97,608.00	174,342	203,154.00
Net energy efficiency (MJ ha−1)	85,138.88	148,106.88	177,525.46
Output/input ratio (%)	7.83	6.65	7.93
Net energy ratio (%)	6.83	5.65	6.93
Energy efficiency (kg MJ−1)	0.53	0.45	0.54
Energy required per unit product (MJ kg−1)	1.88	2.24	1.85

. Among the applications, the highest value in terms of net energy efficiency was obtained from U3 application with 177,525.46 MJ ha⁻¹, followed by U2 application with 148,106.88 MJ ha⁻¹. Among the applications, the highest output/input ratio was obtained from U3 application as 7.93, followed by U1 application with 7.83 and U2 application with 6.65. It can be said that the reason why U1 application has higher output/input ratio than U2 application is that no fertilizer application is made in U1 application. Mohammadi et al. (2014) [40] reported that the average values of energy use efficiency for canola, soybean, barley, wheat, paddy and maize silage were 6.8, 4.8, 4.4, 4.0, 3.8 and 2.7, respectively. Konak et al. (2004) [37] reported the energy input–output ratio of maize production as 3.98.

When the applications were examined in terms of energy efficiency, the highest energy efficiency was obtained from the U3 application with 0.54 kg MJ⁻¹, followed by the U1 application with 0.53 kg MJ⁻¹. The U2 application was the least energy-efficient with 0.45 kg MJ⁻¹.

When the applications were examined in terms of the energy value required for one kg of product production, the best result was obtained from the U3 application as 1.85 kg MJ⁻¹. It was obtained in the U2 application as 1.88 kg MJ⁻¹. Dilay (2021) [38] reported the specific energy and energy efficiency in maize production as 1.20 kg MJ⁻¹ and 0.83 kg MJ⁻¹, respectively.

Carbon Footprint

The environmental impacts of U1, U2 and U3 applications were quantitatively evaluated (

Table 9. Carbon input and output amounts of U1 application.

U1
	Unit	Unit ha−1	kg CO2-eq	kg CO2-eq ha−1	%
Labor	Day (8 h)	8	0.23	1.84	0.31
Tractor + machine	h	20.78	0.89	18.49	3.12
Fuel-oil	L	54.75	0.94	51.47	8.68
Irrigation	m3	3000	0.17	510	86.06
Herbicide	kg	0.8	1.5	1.2	0.20
Seed	kg	30	0.32	9.6	1.62
Total input				592.60	100
Output (biological yield)	kg	13,978.95	0.44	6150.74

,

Table 10. Carbon input and output amounts of U2 application.

U2
		Unit	Unit.ha−1	kg CO2-eq	kg CO2-eq ha−1	%
Labor		Day (8 h)	8	0.23	1.84	0.21
Tractor + machine		h	20.78	0.89	18.49	2.09
Fuel-oil		L	54.75	0.94	51.47	5.82
Irrigation		m3	3000	0.17	510	57.65
Inorganic fertilizers	N	kg	200	1.3	260	29.39
	P	kg	100	0.2	20	2.26
	K	kg	80	0.15	12	1.36
Herbicide		kg	0.8	1.5	1.2	0.14
Seed		kg	30	0.32	9.6	1.09
Total input					884.60	100
Output (biological yield)		kg	24,631.58	0.44	10,837.89

and

Table 11. Carbon input and output amounts of U3 application.

U3
		Unit	Unit ha−1	kg CO2-eq	kg CO2-eq.ha−1	%
Labor		Day (8 h)	8	0.23	1.84	0.21
Tractor + machine		h	23.54	0.89	20.95	2.41
Electric		kwh	42.6	0.0725	3.09	0.35
Fuel-oil		L	61.45	0.94	57.76	6.64
Irrigation		m3	3000	0.17	510	58.61
Liquid manure	N	kg	195.5	1.3	254.15	29.21
	P	kg	23	0.2	4.60	0.53
	K	kg	46	0.15	6.90	0.79
Herbicide		kg	0.8	1.5	1.2	0.14
Seed		kg	30	0.32	9.60	1.10
Total input					870.09	100
Output (biological yield)		kg	29,094.74	0.44	12,801.68

).

It was determined that U1 (592.60 kg CO₂-eq ha⁻¹) application had less C input than U2 (884.60 kg CO₂-eq.ha⁻¹) and U3 (870.09 kg CO₂-eq ha⁻¹) applications. The reason why carbon input in U2 and U3 applications was higher than U1 application is due to the use of chemical fertilizer in U2 application and liquid farm manure in U3 application. While only mineral fertilizer inputs were included in the calculations in U2 application, in U3 application, liquid farm manure and separation processes used to obtain this fertilizer were included in the calculations and also fuel consumption increased due to the use of a liquid farm manure spreader machine, which increased the C input of U3 application. Although no fertilizer was applied in the U1 treatment, resulting in lower carbon input, a significant decrease in biological efficiency was observed. When the C inputs within the applications were examined, the highest C input rate was obtained from irrigation with 86.06% in the U1 application. Since no fertilizer was used in the U1 application, the share of irrigation in C input increased. This was followed by fuel input with 8.68%. The highest C input rate was determined in irrigation with 57.65% in the U2 application, followed by mineral fertilizer and fuel with 33.01% and 5.82%, respectively. In the U3 application, the highest C input was determined in irrigation with 58.61%, followed by liquid farm fertilizer and fuel-oil with 30.53% and 6.64%, respectively. It was determined that the lowest C inputs were in seed, labor, herbicide and machinery inputs in all applications, and the electrical energy used in the U3 application was one of the lowest C inputs. Follett (2001) [41] reported that the C emission from pumped irrigation was 150–200 kg CO₂-eq ha⁻¹ per year, depending on the energy source. When carbon outputs were examined, the highest carbon output was obtained from U3 application as 12,801.60 kg CO₂-eq ha⁻¹. It was determined that U2 and U3 applications followed this with 10,837.89 kg CO₂-eq.ha⁻¹ and 6150.74 kg CO₂-eq ha⁻¹ values, respectively. Although the carbon inputs of U3 and U2 applications were higher than U1 application, the use of different fertilizers in U3 and U2 applications provided higher biological yields and higher carbon outputs than U1 application. The carbon output of U3 application was found to be 1.18 and 2.08 times higher than U2 and U1 applications, respectively, and U2 application was found to be 1.76 times higher than U1 application. Zhang et al. (2017) [42], based on data from 2013, estimated the carbon footprint of cereal production to be 4052 kg CO₂ ha⁻¹ or 0.48 kg CO₂ kg⁻¹ for maize and 5455 kg CO₂ ha⁻¹ or 0.75 kg CO₂ kg⁻¹ for wheat. The study reported that the main factors determining carbon emissions were nitrogen fertilizer application (8–49%), straw burning (0–70%), energy consumption by agricultural machinery (6–40%), and energy use for irrigation (0–44%). Xi et al. (2024) [43] compared Japonica rice (JR) and indica rice (IR) cultivation systems and reported that although the carbon input of the indica rice (IR) cultivation system was less than the carbon input of the Japonica rice (JR) cultivation system, the carbon output of the JR cultivation system was on average 11.8% higher than the IR cultivation system and that chemical fertilizer applied at different rates increased the carbon input by 32.4%.

The CE, CF and CSI results of the applications are given in

Table 12. CE, CF and CSI results of the applications.

	U1	U2	U3
Carbon efficiency (CE)	10.38	12.25	14.71
Carbon footprint (CF) (kg CO2-eq kg−1)	0.09	0.08	0.06
Carbon sustainability index (CSI)	9.38	11.25	13.71

. Among the applications, the U3 application was found to be higher than the U1 and U2 applications in terms of carbon efficiency (CE) and carbon sustainability index (CSI). Lal. (2004) [34] reported that the sustainability of agricultural ecosystems requires the carbon output/input ratio expressed as carbon efficiency (CE) to be >1 and to have an increasing trend over time. The reason why the carbon output in the application where liquid manure was used was higher than the applications where no fertilizer was applied and inorganic fertilizer was applied was because biological efficiency was higher in these applications.

U3 application created the lowest carbon footprint (CF) with a value of 0.06 kg CO₂-eq kg⁻¹, while 0.08 kg CO₂-eq kg⁻¹ was obtained in U2 application and 0.09 kg CO₂-eq kg⁻¹ in U1 application. Mohammadi et al. (2014) [40] indicated that demographic expansion, together with ongoing global climate change, exerts increasing pressure on food production systems. As a consequence, the intensified exploitation of water and energy resources has led to an expansion of agriculture’s environmental footprint. Moungsree et al. (2022) [44] evaluated greenhouse gas emissions and life cycle costs associated with maize production in Thailand. The authors reported that the average total greenhouse gas emissions from maize production were 0.429 ± 0.027 kg CO₂-eq kg⁻¹ of grain, with the highest emissions occurring during the dry season.

The findings of this study indicate that the relationship between energy inputs and carbon emissions highlights the important role of fertilization strategies in determining both energy efficiency and environmental performance in maize production systems. In particular, the use of inorganic fertilizers increased total energy input due to the high energy demand associated with fertilizer production, which in turn contributed to higher carbon emissions. In contrast, the application of liquid animal manure improved energy efficiency through higher grain yield and lower energy intensity per unit of product. These results suggest that nutrient management strategies can simultaneously influence productivity, energy use efficiency, and carbon footprint in semi-arid agroecosystems. However, the results of this study are based on a single growing season and site-specific conditions. Therefore, long-term field experiments conducted under different climatic and soil conditions are needed to provide a more comprehensive evaluation of the sustainability of nutrient management systems based on liquid animal manure. Organic materials with a high C/N ratio can lead to temporary nitrogen immobilization under certain conditions. However, this depends not only on the C/N ratio but also on the form of nitrogen and soil conditions. In liquid animal manure systems, a significant portion of nitrogen is found in mineral forms that can be rapidly absorbed by plants, especially ammonium (NH₄⁺) (Kirchmann and Witter, 1992, [45]; Sommer and Hutchings, 2001, [46]). Furthermore, nitrogen dynamics in the soil are determined by the interaction of microbial activity, decomposition processes, and environmental conditions (Recous et al., 1995) [47]. The significantly higher yield obtained in this study with liquid manure application demonstrates that nitrogen is available in sufficient quantities for plants and that immobilization is not a limiting factor. Therefore, nitrogen immobilization in this system is a non-limiting, condition-dependent process.

Beyond energy efficiency and carbon footprint indicators, the results of this study can also be interpreted within a broader sustainability framework. The use of liquid animal manure contributes to nutrient recycling and can be considered consistent with circular economy principles in agriculture, as livestock-derived residues are reused as nutrient sources in crop production systems. Such practices may reduce reliance on synthetic fertilizers while improving resource use efficiency. In semi-arid regions, where water availability is often a limiting factor, the application of organic amendments may also indirectly enhance soil structure and water retention capacity, thereby supporting more efficient water use. At the same time, sustainable nutrient management remains essential, since excessive nitrogen (N) and phosphorus (P) inputs may lead to environmental problems such as nutrient leaching and eutrophication. In this context, appropriate application rates and integrated nutrient management strategies are necessary to minimize potential environmental risks. These considerations are also consistent with the principles of conservation agriculture promoted by the FAO, which emphasize improved soil management, efficient resource use, and reduced environmental impacts in agricultural production systems.

Grain yield differed significantly among treatments (

Table 13. Analysis of variance (ANOVA) results showing the effects of fertilization treatments on grain yield, biological yield, energy efficiency, and carbon footprint in maize production.

Treatment	Grain Yield (kg ha−1)	Biological Yield (kg ha−1)	Energy Efficiency (kg MJ−1)	Carbon Footprint (kg CO2-eq kg−1)
U1	6640 ± 35.0 c	13,978.95 ± 59.3 c	0.53 ± 0.007 b	0.09 ± 0.005 a
U2	11,700 ± 19.3 b	24,631.58 ± 50.8 b	0.45 ± 0.012 c	0.08 ± 0.002 b
U3	13,820 ± 35.0 a	29,094.74 ± 94.4 a	0.54 ± 0.003 a	0.06 ± 0.001 c

Values are presented as mean ± standard error (n = 3). Different letters within each column indicate significant differences at p < 0.05 (Tukey’s HSD test).

) (p < 0.05), with the highest value observed in the liquid manure treatment (U3), followed by inorganic fertilizer (U2) and the unfertilized control (U1). A similar trend was observed for biological yield. Energy efficiency was significantly affected by fertilization strategy, with U3 showing the highest values and U2 the lowest. In contrast, carbon footprint was significantly reduced in U3, whereas the highest values were recorded in U1. These findings indicate that liquid manure application significantly improves both productivity and environmental performance in maize production systems.

4. Conclusions

The grain yield obtained from the liquid manure treatment was 2.08 times higher than the unfertilized treatment and 1.18 times higher than the inorganic fertilizer treatment. This increase suggests that liquid manure improved nutrient availability and soil fertility, which enhanced maize productivity under semi-arid conditions. Kirchmann and Witter (1992) [45] reported that a large proportion of nitrogen in animal slurry exists in ammonium form and becomes immediately available for plant uptake after application. Furthermore, organic amendments such as liquid manure can improve soil fertility not only through nutrient supply but also through enhanced microbial activity, improved soil structure, and increased soil water retention, which may contribute to higher crop yields (Lal, 2004) [34].

Net energy efficiency in the liquid manure treatment was 2.09 times higher than the unfertilized treatment and 1.20 times higher than the inorganic fertilizer treatment. The higher energy efficiency indicates that liquid manure improved the conversion of energy inputs into crop output, mainly due to higher grain yield and more efficient nutrient utilization.

The output/input ratio in the liquid manure treatment was 1.01 times higher than the unfertilized treatment and 1.19 times higher than the inorganic fertilizer treatment. Similarly, energy efficiency was 1.01 times higher than the unfertilized treatment and 1.21 times higher than the inorganic fertilizer treatment. These results indicate that the liquid manure treatment produced more energy output relative to the energy inputs, reflecting improved resource use efficiency in maize production.

The energy required per kilogram of product was higher in the unfertilized and inorganic fertilizer treatments. These values were 1.01 and 1.21 times higher, respectively, compared with the liquid manure treatment. The lower energy requirement in the liquid manure treatment suggests that this practice reduces production energy intensity while maintaining higher productivity.

Carbon efficiency in the liquid manure treatment was 1.42 times higher than the unfertilized treatment and 1.20 times higher than the inorganic fertilizer treatment. The carbon sustainability index was 1.46 times higher than the unfertilized treatment and 1.22 times higher than the inorganic fertilizer treatment. These results indicate that liquid manure improved carbon use efficiency and contributed to a more sustainable production system.

The carbon footprint of the unfertilized and inorganic fertilizer treatments was 1.42 and 1.20 times higher, respectively, than that of the liquid manure treatment. The lower carbon footprint in the liquid manure treatment highlights its potential to reduce greenhouse gas emissions per unit of maize production.

The statistically significant differences among treatments confirm that fertilization strategy plays a key role in determining both productivity and carbon performance in maize production systems

Overall, the liquid manure treatment produced the highest yield, energy efficiency, carbon efficiency, and carbon sustainability index, while generating the lowest carbon footprint. These findings suggest that liquid animal manure represents a promising management strategy for improving maize productivity while reducing energy use and environmental impacts in semi-arid agroecosystems.