The Impact of Maize Legume Intercropping on Energy Indices and GHG Emissions as a Result of Climate Change

Abstract

Multicropping can solve energy use and GHG balance problems, but the emergence, development, and productivity of such mixed crops are at risk due to the uneven distribution of precipitation. For this reason, investigations were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Single maize crops were compared with Crimson/red clover, Persian clover, and alfalfa intercropped maize. The objective of this study was to evaluate the main energy indices and GHG balance of legume intercropped maize cultivated in humid and arid vegetative conditions. The results showed that, under arid conditions, the quantity of intercrop biomass was about four times lower than that under humid conditions. Humid conditions were less suitable for maize and resulted in about 3–5 t ha⁻¹ less dried biomass from intercrops and about 6 t ha⁻¹ less biomass in single crops than in arid conditions. Due to the higher yield of maize biomass in the arid season, better energy indicators of crops were obtained in arid than humid conditions. The difference between net energy was about 122–123 MJ ha⁻¹ in all treatments, except for the maize crop with intercropped alfalfa, where the difference was 62 MJ ha⁻¹. All tested technologies were environmentally friendly; the CO₂ equivalent varied between treatments from 804 to 884 kg ha⁻¹. The uneven distribution of precipitation during the vegetative season provides insight into the improvement of intercropping technologies. Sowing intercrops at the same time as maize could improve their germination but increase the problem of weed spread.

1. Introduction

A priority of the European Commission is to increase energy efficiency by 27% over the next three years. The European Council for an Energy Efficient Economy has also highlighted energy efficiency as the main objective for controlling climate change [1], as energy use conservation reduces GHG emissions [2].

Energy resources in agricultural production are expensive, and the efficient use of energy is highly important [3]. Energy efficiency reflects the intensity of agricultural inputs [4], and higher levels of agricultural inputs could increase the demand for energy in grain production by 25% on average [5]. The demand for energy can be reduced through optimizing agricultural operations.

The efficient use of energy in agrotechnologies could decrease their anthropogenic impacts on the environment [6]. However, agriculture still exerts the most significant influence on the environment among all human activities and is closely tied to changes in land use, energy consumption, and greenhouse gas (GHG) emissions. Agriculture creates about 22% of Lithuania’s CO₂ emissions and up to 32% of global CO₂ emissions [7,8,9].

The most important factors in controlling GHG emissions are the reduced use of organic fertilizers [10], tillage intensity, and fertilization rates [11]. However, conversely, in some no-till practices, a significant increase in GHG emissions by 7.1% was found compared to conventional tillage [12]. Multicropping also increases soil fertility, phytosanitary conditions, and protection against weeds, diseases, and pests. A successful increase in yield is also achieved with the help of multicropping [13,14], especially in the case of climate change, which frequently amplifies crop and yield losses [15].

It is important to choose appropriate plants due to their different functions and benefits. Clover, maize, faba bean, and alfalfa could serve as effective companions in multicrops, given that maize has a high potential for biomass production [16]. Alfalfa and clover provide distinctive advantages as a perennial crop, contributing to the development of organic matter for enhanced structure, stability, and water retention capacity. Incorporating clover and alfalfa into a farm’s rotation can enhance the yield of other crops and potentially decrease the requirement for chemical inputs [17]. Faba beans are highly effective plants for intercropping and ecological services [18]. However, multicrop cultivation has still not been widely studied [19].

There is a lack of a standardized approach for determining greenhouse gas (GHG) emissions in crop production systems, and there remains a necessity to enhance the sustainability of agricultural technologies [20]. Estimating the total quantity of carbon inputs in agrotechnologies poses challenges. It is beneficial to convert different inputs into equivalent carbon dioxide emissions for agricultural applications.

The main aim of this study was to evaluate the fuels, working time, and energy inputs for the main materials and biomass energy outputs in the production of single and binary maize crops with varying biodiversity under humid and arid vegetative conditions. We hypothesize that the use of leguminous intercrops in maize cultivation will balance fuel and energy use and stabilize GHG emissions.

2. Materials and Methods

2.1. Experimental Site

The data were obtained through stationary field experiments of maize intercropping in 2010 (wet season) and 2023 (arid season), which were performed at the Experimental Station of Vytautas Magnus University, Agriculture Academy. The experimental soil was sandy loam (sand 57.4%, clay 14.9%) Planosol (Endohypogleyic-Eutric, Ple-gln-w) [21]. The pH of the soil varied from 7.1 to 7.5, and the contents of P₂O₅, K₂O, MgO, and N_tot were 130–249 mg kg⁻¹, 74–121 mg kg⁻¹, 560–791 mg kg⁻¹, and 1.14–1.30 g kg⁻¹, respectively.

Lithuania is a country with a surplus precipitation balance. During vegetative seasons, precipitation rates of 300–400 mm are unevenly distributed, with several drought periods during the vegetative period (Table 1). Meteorological data for 2010 show that the weather during the maize growing season was warm and more humid than usual. The precipitation rate was around 557 mm per vegetative season. A total of 350–400 mm of precipitation per vegetative season is the average range. Warm and humid summers are beneficial for maize, but the rainfall is excessive. Water often stagnates in the rows, worsening soil aeration and nutritional conditions. In the 2023 vegetative season, average air temperatures were similar to or higher than the long-term average, but the vegetative period was arid with 250 mm of precipitation only.

Arid conditions negatively influenced the germination, development, and productivity of legume intercrops sown after maize sprouting.

2.2. Treatments and Agronomic Practice

In our study, the following treatments were evaluated:

1. Inter-row mellowing (CO);

2. Intercropping and mulching with Crimson/red clover (RCRC);

3. Intercropping and mulching with Persian clover (PC);

4. Intercropping and mulching with alfalfa (AA).

The field experiment was carried out in 4 replicates. Crops were grown with continued cultivation.

In the fall, before the experiment was set up, the soil was ploughed with a Kverneland semi-screw plough. In the spring, when the soil reached physical maturity, it was cultivated with a KLG-4 compound cultivator at a depth of 4 cm. On the same day, 5:15:29 NPK mineral fertilizers were distributed. The fertilizer rate was 300 kg ha⁻¹. After fertilizing for up to 3 days, the maize was sown with a Kverneland Accord Optima pneumo-mechanical seeding machine in 45 cm wide rows with a distance between seeds of 21 cm. After the maize germinated, the inter-rows were loosened, and inter-row Fabaceae crops were inter-sown with a hand seeder for greenhouses, for a total of 4–6 rows. The sowing rates of maize and Crimson/red clover, Persian clover, and alfalfa were 30 kg ha⁻¹, 18 kg ha⁻¹, and 20 kg ha⁻¹, respectively.

The inter-rows of maize were mellowed, and the intercrops and weeds were cut and mulched 2–3 times during the maize growing season until the maize reached a height of 50–70 cm.

The intercrops were cut using a “Stihl” FS-550 hand brush cutter once they reached a height of 20–25 cm. The green mass of intercrops and weeds was distributed in the inter-rows of maize. The inter-rows of control plots were mellowed manually.

Pesticides were not used in agrotechnologies. The biomass was manually harvested at the end of the maize vegetative period in October (after the grain had reached the beginning of hard maturity). In the experiment, the energy of manual work was transformed into machine work (Table 2).

We used the normative data of the agricultural machinery of the Lithuanian Institute of Economy and Rural Development in the calculations of the energy and environmental assessment of agrotechnologies. We used a field area of 2–10 ha for the calculations. The power of tractors varied from 45 to 102 kW, while that of the biomass harvester was 250 kW. The data of tractor-operated drills when up to 200 kg ha⁻¹ seeds were sown are presented in calculations according to [22,23].

We chose the closest available mounted rotary mower to cut the inter-rows. A 6-furrow biomass harvester was chosen in the case of plots with areas up to 10 ha, biomass yield up to 12 t ha⁻¹, and a swath width of 3 m. In our model, the CO plots—where no intercrops were grown—had a lower harvester load than the intercropped maize.

In Table 3, we present the main technical indicators of the technological processes, including machine power, working width, output rate, working time costs, and diesel fuel costs. Harvesting operations were found to have the highest fuel consumption. Under higher load conditions, fuel consumption reaches as much as 27.6 L ha⁻¹.

2.3. Methodology

Samples for maize and intercrop biomass productivity evaluation were taken from at least 5 spots per each experimental plot and for each species of crop. Biomass was dried at a temperature of 105 °C to dry form. The results of dried biomass are presented in this study.

It was possible to evaluate the net energy of different agrotechnologies through selecting energy equivalents of technological operations (Table 4).

It is convenient to evaluate agrotechnologies according to the relative emissions of greenhouse gases. The equivalent of CO₂ gas (CO_2eq) is used for this (Table 5).

The ANOVA function off the statistical software SELEKCIJA (vers. 5.00, author dr. Pavelas Tarakanovas, Lithuanian Institute of Agriculture, Akademija, Kedainiu, Lithuania) was used for the data analysis. An LSD test was also performed. Letters refer to significant differences between treatments at p ≤ 0.05.

3. Results and Discussion

3.1. Energy Inputs

Energy consumption is a necessary factor in agriculture [34], as agrotechnologies use many different types of powerful machines for seeding, soil tillage, harvesting, and crop care [35,36]. Other scientists found that the largest proportion of energy inputs was used in chemical fertilization, while the smallest was used in human labor [37].

In experiments, energy consumption for human labor increased by about 14% when growing intercrops compared to the control treatment (Table 6). Fuel consumption also increased by up to 14% in all intercrops. With the use of agricultural machinery, the same trends emerged as in the previously mentioned analyzed indicators.

The lowest total energy consumption was recorded for the agrotechnology without intercrops (CO), which had 20–22% lower energy consumption than intercropped maize technologies.

3.2. Crop Productivity and Energy Indices

Energy is an important driving force of development and is particularly important in the agricultural sector. This is because agriculture is not only a consumer of energy but also a producer [34], as maize and hemp biomass are beneficial resources for biofuel production [38,39].

In the present study, the highest yield of dried biomass was obtained in single maize crops without companions (Table 7). The difference in biomass yields between vegetation conditions showed that maize was relatively unaffected by low rainfall amounts, while excess rainfall was less favorable for legume intercrops. However, in arid years, intercrops were more negatively affected, and the biomass yield was approximately four times lower than in humid conditions. However, in humid conditions, the intercrop biomass addition did not compensate for the total crop biomass yield and, in dry years, due to the higher maize yield, the total crop yield was between 20 and 46% higher than under wet vegetation conditions. These differences markedly influenced energy output and other energy indices.

Diesel fuel consumption, energy input, energy output, energy efficiency ratio, and the net energy of the various mechanized technological operations for tillage, sowing, fertilizing, and harvesting are presented in Table 8. In agrotechnologies, energy output is an energetical expression of the harvest. Therefore, the largest energy output, the energy consumption ratio, and the net energy were calculated in control plots without intercrops. In the absence of moisture, intercrops germinate poorly and develop slowly, so various methods of sowing, fertilizing, and selecting plant species or varieties, as suggested by some authors [40,41], bring little benefit.

3.3. Environmental Impact

While a large proportion of anthropogenic emissions derive from industrial processes, agriculture is considered to be one of the most polluting sectors in the world. Agriculture is a major source of greenhouse gases (GHGs) [42,43]. Considering that climate change is caused by the increasing emissions of greenhouse gases due to anthropogenic effects [44], sustainable farming ensures lower emissions to the environment and the entire food chain [45,46,47].

The GHG emissions for the agrotechnological inputs were recalculated into a CO_2eq system using the conversion equivalents (Table 9).

The total theoretical GHG emissions according to the CO₂ equivalent were the highest in intercropped maize cultivation, when assessing fuel (from 13.9% to 31.0%), agricultural machinery (from 13.7% to 21.4%), and sowing work (from 0.9% to 9.6%), compared to the controls K1 and K2, as no agricultural machinery, fuel, or labor hours were used for intercrop sowing. In our experiment, GHG emissions varied from 803.8 to 883.6836 kg CO_2eq ha⁻¹ and were similar for all technologies, as most technological operations were the same or differed little. Juarez-Hernandez et al. [48] found that the total GHG emissions in tested maize agrotechnologies ranged from 152.9 kg CO_2eq ha⁻¹ to 3475.8 kg CO_2eq ha⁻¹.

4. Conclusions

Humid vegetative conditions were more favorable for the development of leguminous intercrops than arid conditions. Under wet conditions, the intercrop biomass was about four times higher than that under dry conditions. Drought caused 23–40% higher intercrop biomass losses in clover and alfalfa compared with single maize cultivation. Smaller harvests reduced the energy output by up to 38%, energy efficiency ratio by up to 2.5-fold, and net energy by up to 42%. To the contrary, wet conditions were less suitable for maize and resulted in about 3–5 t ha⁻¹ less dried biomass in intercrops and about 6 t ha⁻¹ less biomass in single crops than in dry conditions. Due to the higher yield of maize biomass in the arid season, better energy indicators of crops were obtained under arid than humid conditions. The difference between the vegetation conditions was about 122–123 MJ ha⁻¹ in all treatments, except for the maize crop with alfalfa, where the difference was 62 MJ ha⁻¹. According to the CO₂ equivalent, all tested technologies were similarly environmentally friendly and only differed by about 10%.

We expected that intercrops would compete with maize and reduce its productivity but would produce abundant biomass to compensate for the losses. Unfortunately, this hypothesis was not confirmed as the intercrops sprouted and developed poorly during the dry period.

The increased number of waterlogging and drought periods due to climate change suggests the need to improve intercrop sowing technologies. Sowing intercrops at the same time as maize could solve the problem of their germination, but there remains the problem of abundant weeds. Therefore, it is necessary to study the effect of the spread weeds on the intercropped maize agroecosystem, including its energy and GHG balance, in more detail.

Author Contributions

Conceptualization, K.R.; methodology, K.R. software, A.Š. and K.R.; validation, K.R.; formal analysis, A.Š., R.K., A.S. and K.R.; investigation, A.Š., K.R., A.S., A.J., A.A. and R.K.; resources, K.R., A.S. and A.Š.; data curation, K.R. and A.Š.; writing—original draft preparation, K.R., R.K. and A.S.; writing—review and editing, K.R., R.K. and A.S.; visualization, K.R. and A.Š.; supervision, K.R. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Most of the data generated or analyzed during this study are included in the present article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Tables

Table 1

Average air temperatures and precipitation rates. Kaunas Meteorological Station, April–October 2010 and 2023.

Month	Average Air Temperature °C		Precipitation Rate mm
	Monthly	Long Term	Monthly	Long Term
April	7.4/8.5	6.9	58.5/26.7	41.3
May	13.7/12.6	13.2	94.8/14.3	61.7
June	16.5/17.3	16.1	127.0/64.0	76.9
July	21.9/18.0	18.7	100.7/36.8	96.6
August	19.7/20.2	17.3	112.5/96.2	88.9
October	12.0/17.1	12.6	63.3/11.6	60.0

Note: The numerator represents 2010 data, and the denominator represents 2023 data.

Table 2

Agrotechnological operations.

Technological Operation(Machinery/Depth/Material Rate)/Treatment	CO	RC	PC	AA
Deep ploughing	o	o	o	o
Pre-sowing cultivation	o	o	o	o
Fertilization (N15 P45 K87 kg ha−1)	o	o	o	o
Maize sowing	o	o	o	o
Intercrop sowing	-	o	o	o
Inter-row loosening (2–3 cm depth)	ooo	o	o	o
Intercrop mulching	-	oo	oo	oo
Low harvester load biomass harvesting	o	-	-	-
High harvester load biomass harvesting	-	o	o	o

Notes: CO: inter-row mellowing (control 1); RC: intercropping and mulching with Crimson/red clover; PC: intercropping and mulching with Persian clover; AA: intercropping and mulching with alfalfa. A dash means that no operation was performed, and o indicates the number of operations performed.

Table 3

Technical indicators of technological operations.

Technological Operation	Machinery Power(kW)	Working Width(m)	Field Capacity(ha h−1)	Working Time(h ha−1)	Fuel Consumption(L ha−1)
Deep ploughing	102	1.75	0.80	1.25	24.1
Pre-sowing cultivation	102	7.00	4.56	0.22	6.4
Maize sowing	45	3.00	1.41	0.71	4.0
Intercrop sowing	67	3.00	1.31	0.76	9.8
Fertilization	67	14.00	16.55	0.06	0.6
Inter-row loosening	54	3.00	1.56	0.64	4.1
Intercrop mulching	54	3.00	2.05	0.49	5.3
Low harvester load biomass harvesting	250	3.00	1.82	0.55	19.2
High harvester load biomass harvesting	250	3.00	1.37	0.73	27.6

Table 4

Energy equivalents in agrotechnologies.

Index	Energy Equivalent	Reference
Inputs:
Human labor (MJ h−1)	1.96	[24]
Diesel fuel (MJ L−1)	56.3	[24]
Agricultural machinery (MJ h−1)	357.2	[25]
Seeds of maize (MJ kg−1)	16.6	[24]
Seeds of clover	11.0	[26]
Seeds of alfalfa	11.9	[27]
N (MJ kg−1)	60.6	[24]
P2O5 (MJ kg−1)	11.1	[24]
K2O (MJ kg−1)	6.7	[24]
Outputs:
Maize biomass (MJ kg−1 dry matter)	17.7	[28]
Clover biomass (MJ kg−1 dry matter)	11.6	[29]
Alfalfa biomass (MJ kg−1 dry matter)	11.0	[29]

Table 5

CO₂ equivalents in agrotechnologies.

Input	CO2 Equivalent	Reference
Diesel fuel (kg CO2eq L−1)	2.76	[30]
Agricultural machinery (kg CO2eq MJ−1)	0.071	[31]
Seeds of maize (kg CO2eq kg−1)	15.3	[32]
Seeds of legumes (kg CO2eq kg−1)	0.22	[33]
N (kg CO2eq kg−1)	1.30	[7]
P2O5 (kg CO2eq kg−1)	0.20	[7]
K2O (kg CO2eq kg−1)	0.15	[7]

Table 6

Energy inputs of technological operations and materials in crop biomass production systems, MJ ha⁻¹.

Input	CO	RC	PC	AA
Human labor	9.2	10.5	10.5	10.5
Diesel fuel	3749.6	4909.4	4909.4	4909.4
Agricultural machinery	1682.4	1911.2	1911.2	1911.0
Seed of maize (30 kg ha−1)	498.0	498.0	498.0	498.0
Seed of clover (30 and 18 kg ha−1)	-	330.0	198.0	-
Seed alfalfa (20 kg ha−1)	-	-	-	238.0
N	909.0	909.0	909.0	909.0
P2O5	499.5	499.5	499.5	499.5
K2O	582.9	582.9	582.9	582.9
Total energy input	7930.6	9650.5	9518.5	9558.3

Notes: CO: inter-row mellowing (control treatment); RC: intercropping and mulching with Crimson/red clover; PC: intercropping and mulching with Persian clover; AA: intercropping and mulching with alfalfa.

Table 7

Harvest of single and binary maize cultivation in different vegetative conditions, kg ha⁻¹ of dried biomass.

Treatment	Humid Vegetative Season, 2010			Arid Vegetative Season, 2023
	Maize	Intercrop	Total	Maize	Intercrop	Total
CO	14,920	-	14,920	21,800	-	21,800
RC	10,090	2722	12,812	16,600	569	17,169
PC	9790	2134	11,924	16,100	561	16,661
AA	9780	1206	10,986	12,900	275	13,175

Notes: CO: inter-row mellowing (control treatment); RC: intercropping and mulching with Crimson/red clover; PC: intercropping and mulching with Persian clover; AA: intercropping and mulching with alfalfa.

Table 8

Energy indices of single and binary maize cultivation in different vegetative conditions.

Treatment	Energy Input MJ ha−1		Energy Output MJ ha−1		Energy Efficiency Ratio		Net Energy MJ ha−1
	HS	AS	HS	AS	HS	AS	HS	AS
CO	6439		264,084	385,860	41.0	59.9	257,645	379,421
RC	9650		178,593	300,420	18.5	31.1	168,943	290,769
PC	9518		168,478	291,478	17.7	30.6	158,960	281,959
AA	9558		169,182	231,355	17.7	24.2	159,624	221,797

Notes: CO: inter-row mellowing (control treatment); RC: intercropping and mulching with Crimson/red clover; PC: intercropping and mulching with Persian clover; AA: intercropping and mulching with alfalfa. HS: humid vegetative season (2010); AS: arid vegetative season (2023).

Table 9

GHG emissions from a single and intercropped maize cultivation.

Index/Treatment	CO	RC	PC	AA
Diesel fuel (kg CO2eq ha−1)	183.8	240.7	240.7	240.7
Agricultural machinery (kg CO2eq ha−1)	119.4	135.7	135.7	135.7
Seed (kg CO2eq ha−1)	459.0	465.6	463.0	463.4
Fertilizer (kg CO2eq ha−1)	41.6	41.6	41.6	41.6
Total GHG emissions (kg CO2eq ha−1)	803.8	883.6	881.0	881.4

Notes: CO: inter-row mellowing (control treatment); RC: intercropping and mulching with Crimson/red clover; PC: intercropping and mulching with Persian clover; AA: intercropping and mulching with alfalfa.

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