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Changes of Energy Balance and Ghg Emissions in Legume Intercropped Maize Agroecosystem as a Result of Drought

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08 February 2024

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12 February 2024

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Abstract
Multi-cropping can solve the energy and GHG balance problems, but the emergence, development and productivity of such mixed crops are at risk due to increasing drought periods. For this reason, single maize crops with inter-row mellowing and weed-mulching practices were compared with faba bean, crimson and Persian clover, and alfalfa intercropped maize cultivations. Tests were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Results showed that by the 30% higher energy inputs were calculated in technologies with intercropped crimson, Persian clover, and alfalfa than in monocropped maize. Single maize crops produced by the 23-40% higher biomass yields compare to clover and alfalfa intercropped maize because, due to the drought periods, intercrops weakly sprouted and developed. Lower harvests reduced the energy output by up to 38%, energy efficiency ratio - by up to 2.5 times and net energy - by up to 42%. All tested technologies were environmentally friendly and similar according to the CO2 equivalent. Long-term drought periods during vegetative season due to climate change make us think about the improvement of intercropping technologies. Sowing intercrops at the same time with maize could solve the problem of its germination but highlight the problem of abundant weed spread. Therefore, this takes us to another sphere of agrotechnologies - the effective non-chemical weed control.
Keywords: 
Subject: Environmental and Earth Sciences  -   Environmental Science

1. Introduction

Agriculture 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% Lithuania’s and up to 32% of global CO2 emissions [1,2,3].
The most important factor in controlling GHG emissions are reduced use of organic fertilizers [4], tillage intensity and fertilization rates [5]. However conversely, in some no-till practices, there were found the significant increase in GHG emissions by 7.1% compared to conventional tillage [6]. Multi-cropping is also an effective example of the use of resources. This provides prerequisites for more efficient fuel and energy consumption, as well as a relatively lower CO2 equivalent. Multi-cropping also initiates raise of soil fertility, phytosanitary conditions, protection against weeds, diseases, and pests. Successful increase in yield is also achieved with the help of multi-cropping [7,8], especially in the case of climate change, which frequently amplifying crop and yield losses [9].
It is important to choose the appropriate plants because the functions and benefits are performed differently. Clover, maize, faba bean, and alfalfa could serve as effective companions in multi-crops, given that maize has a high potential for biomass production [10]. Alfalfa and clovers provide distinctive advantages as a perennial crop, contributing to the development of organic matter for enhanced structure, stability, and water retention capacity. Incorporating clovers and alfalfa into a farm’s rotation can enhance the yield of other crops and potentially decrease the requirement for chemical inputs. It is notably recognized for its positive alfalfa impact on maize, which utilizes the nitrogen fixed in alfalfa’s roots [11]. Faba beans are great plants for intercropping and ecological service [12]. However, multi-crops cultivation has still not been widely studied [13].
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 [14]. Estimating the total amount of carbon inputs in agrotechnologies poses challenges. It is beneficial to convert different inputs into equivalent carbon dioxide emissions for agricultural.
The main aim of this study was to evaluate the fuels, working time, inputs of energy for main materials and biomass energy outputs for production of differently biodiverse single and binary maize crops. We hypothesize that use of leguminous intercrops in maize cultivation will balance fuel and energy use, also stabilize the GHG emission.

2. Materials and Methods

2.1. Experimental Site

A stationary field experiment was started in 2022 at the Experimental station of Vytautas Magnus University, Agriculture Academy. Experimental soil was sandy loam (sand 57.4%, clay 14.9%) Planosol (Endohypogleyic-Eutric, Ple-gln-w [15]. The pH of the soil is 7.4—7.5, content of available phosphorus - 226—249, potassium - 102—121, magnesium - 560—791 mg kg-1 and total nitrogen — 1.14—1.30 g kg-1.
Lithuania is a country with surplus precipitation balance, but in nova days, 300–400 mm precipitation rates distributes not even with several drought periods during vegetative period (Table 1). In 2023 vegetative season, average air temperatures were similar as long-term average or higher but every second month of vegetative period was arid.
These conditions negatively influenced on the germination, development and productivity of legume intercrops, sowed after maize sprouting.

2.2. Treatments and Agronomic Practice

In 2023, maize (Zea mays L.) (hybrid P7034) was grown in the field experiment with intercrops of the Fabaceae family: faba bean (Vicia faba L.) (variety “Trumpet”), Crimson clover (Trifolium incarnatum L.) (variety – “Kardinal”), Persian clover (Trifolium resupinatum L.) (variety – “Rusty”), alfalfa (Medicago sativa L.) (variety – “Giulia”). Six treatments in total were performed:
  • Inter-row mellowing (control 1, K1);
  • Inter-row mulching with weeds – permaculture element (control 2, K2);
  • Intercropping and mulching with faba bean (LUP);
  • Intercropping and mulching with crimson clover (PUD);
  • Intercropping and mulching with Persian clover (PED);
  • Intercropping and mulching with blue-flowered alfalfa (MEL).
The field experiment was carried out in 4 replicates. The plots were arranged in a randomized manner. The brutto size of the plot was 18.4 m2, the netto one was 18 m2. In total, there were 24 plots in the experiment. The pre-crop was a black fallow.
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 compound cultivator KLG - 4.3—4 cm deep. On the same day, mineral fertilizers NPK 5:15:29 were distributed. Fertilizer rate was 300 kg ha-1. After fertilizing, 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, which sowed 4—6 rows. Maize and intercrops were sown according to the intended sowing rates (Table 2).
The inter-rows of maize were mellowed, intercrops and weeds were cut and mulched 2—3 times during the maize growing season until the maize reaches a height of 50—70 cm.
The intercrops were cut with a hand brush cutter “Stihl” FS-550. Intercrops were started to be cut when they reach a height of 20—25 cm. The green mass of intercrops and weeds was spread in the inter-rows of maize. The inter-rows of Control 1 were mellowed manually.
Pesticides were not used in agro-technics. The biomass was harvested at the end of the maize vegetative period in October (after the grain has reached the beginning of hard maturity) manually. In the experiment, the energy of manual work was transformed into machine work (Table 3).
In the calculations of the energy and environmental assessment of agrotechnologies, we used the normative data of the agricultural machinery of the Lithuanian Institute of Economy and Rural Development [16,17]. We used a field area of 2–10 ha for the calculations. The power of tractors varied from 45 to 102 kW, biomass harvester - 250 kW. The data of tractor-operated drills are presented in calculations when up to 200 kg ha−1 seeds were sown.
For cutting the inter-rows, we chose the closest available mounted rotary mower. In the case of a plot up to 10 ha in area, biomass yield up to 12 t ha-1, and a swath width of 3 m, a 6-furrow biomass harvester was chosen. We modelled that the K1 and K2 plots, where no intercrops were grown, would have a lower harvester load than in intercropped maize. A lower load was placed on the combine harvester also in maize with faba beans, because there are practically no faba beans in the inter-rows before harvest.
In Table 4, we presented the main technical indicators of the technological processes, including machine power, working width, output rate and working time costs, and diesel fuel costs. The highest fuel consumption is determined for harvesting operations. Under higher load conditions, fuel consumption will reach as much as 27.6 L ha-1.

2.3. Methodology

Samples for maize and intercrops biomass productivity evaluation were taken in at least in 5 spots per each experimental plot and for each species of crop. 48 samples were formed totally. Biomass was dried at a temperature of 105 °C to absolutely dry form. The results of dried biomasses are presented in this study.
By selecting the energy equivalents of technological operations (Table 5), it is possible to evaluate the energy efficiency of different agro-technologies. The seed rates used for the calculations, indicated in Table 2, were fertilized at the rate of N15P45K87 kg ha-1 in all experimental plots.
It is convenient to evaluate agro-technologies according to the relative emissions of greenhouse gases. The equivalent of CO2 gas (CO2eq) is used for this (Table 6).
A computer program ANOVA from the statistical software SELEKCIJA (vers. 5.00, author dr. Pavelas Tarakanovas, Lithuanian Institute of Agriculture, Akademija, Kedainiu distr., Lithuania) was used for the data analysis. LSD test was performed. Letters mean significant differences between treatments at p ≤ 0.05.

3. Results and Discussion

3.1. Energy inputs

Energy consumption is a necessary factor in agriculture [30], because agro-technologies use plenty different powerful machines for seeding, soil tillage, harvesting and crop care [31,32] and other scientists found that the largest part of energy inputs was used in fertilizing chemical fertilizers. The lowest energy input was human labor [33].
In our experiment, energy consumption for human labor increased from 14.1% to 22.1% when growing intercrops compared to the control treatments (Table 7). Fuel consumption also increased from 13.9% to 30.9% in all intercropped cultivations. With the use of agricultural machinery, the same trends emerged as in the previously mentioned analyzed indicators.
The lowest total energy consumption was calculated by the agrotechnologies applied in the control plots K1 and K2.

3.2. Crop productivity and energy indices

Energy is an important driving force of development, but it is particularly important in the agricultural sector, as agriculture is not only a consumer of energy, but also a producer [30], because maize and hemp biomass is the beneficial resource for biofuel production [34,35,36].
Diesel fuel consumption, energy input, energy output, energy efficiency ratio and net energy of the various mechanized technological operations for tillage, sowing, fertilizing, and harvesting are presented in Table 8.
In agrotechnologies, energy input is the energy value of the harvest. The most energy was accumulated with the harvest in the plots of the first three treatments (Table 8). When clover and alfalfa were intercropped, they competed with maize and their yields decreased. In the year of average humidity, the intercrops produce a significant biomass yield, but in the drought conditions in 2023, their yield reached only 300-500 kg ha-1 and contributed little to energy reserves. Therefore, the largest energy output, the energy consumption ratio and the net energy are calculated in control plots without intercrops. In the absence of moisture, intercrops germinate poorly and develop slowly, so various methods of sowing, fertilizing, selecting plant species or varieties, as suggested by some authors [38,39], bring little benefit.

3.3. Environmental impact

A large proportion of anthropogenic emissions come from industrial processes, but agriculture is considered one of the most polluting sectors in the world [40,41]. Agriculture is a major source of greenhouse gases (GHG) [Hoffman et al., 2018]. Considering that climate change is caused by the increasing emission of greenhouse gases due to anthropogenic effects [42], sustainable farming ensures lower emissions to the environment and the entire food chain [43,44].
The GHG emissions for the agro-technological inputs were recalculated into a CO2eq system using the conversion equivalents (Table 9).
Total GHG emissions were the highest in intercropped maize cultivations, 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, because no agricultural machinery, fuel and labor hours were used for intercrop sowing. In our experiment, GHG emissions varied from 803.8 to 897.8 kg CO2eq ha−1 and were similar for all technologies. The level of GHG emissions was low because Juarez-Hernandez et al., [2019] found that the total GHG emissions ranged from 152.9 kg CO2eq ha−1 to 3475.8 kg CO2eq ha−1 in maize cultivation.

4. Conclusions

The highest energy inputs were calculated in technologies with intercropped crimson, Persian clover, and alfalfa or about 30% higher than in monocropped maize. Maize crops produced higher biomass yields without competing with intercrops. Contrary to expectations, the drought caused clover and alfalfa intercrops biomass losses by the 23-40% compare with monocropped maize cultivations. Lowe harvests reduced the energy output by up to 38%, energy efficiency ratio - by up to 2.5 times and net energy - by up to 42%.
According to the CO2 equivalent, all tested technologies environmentally friendly and were similar - differed by about 10%.
We expected that, although intercrops would compete with maize and reduce its productivity, it would produce abundant biomass to compensate for the losses. Due to the drought, intercrops sprouted and developed slowly, and their biomass was low, so the energy input of technology with intercrops was lower than that of monocropped maize. This has thrown energy efficiency and net energy out of balance. Due to climate change, the increased number of drought periods make us think about the improvement of intercrops sowing technologies. Sowing intercrops at the same time with maize could solve the problem of its germination, but there would be a problem of abundant weeds. Therefore, it is necessary to study in more detail what will be the effect of the spread weeds on the intercropped maize agroecosystem and its energy and GHG balance.

Author Contributions

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

Funding

The investigations are funded by the Ministry of Agriculture of the Republic of Lithuania, grant “Application of the allelopathic effect in crop agrotechnologies for the implementation of environmental protection and climate change goals”, No. MTE-23-3.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Average air temperatures and precipitation rates. Kaunas Meteorological Station, April-October 2023.
Table 1. Average air temperatures and precipitation rates. Kaunas Meteorological Station, April-October 2023.
Months Average air temperatures °C Precipitation rates mm
monthly long-term monthly long-term
April 8.5 6.9 26.7 41.3
May 12.6 13.2 14.3 61.7
June 17.3 16.1 64.0 76.9
July 18.0 18.7 36.8 96.6
August 20.2 17.3 96.2 88.9
October 17.1 12.6 11.6 60.0
Table 2. Sowing rates (kg ha−1) of differently intercropped maize cultivations.
Table 2. Sowing rates (kg ha−1) of differently intercropped maize cultivations.
Crop/Treatments K1 K2 LUP PUD PED MEL
Maize 30 30 30 30 30 30
Faba bean - - 200 - - -
Crimson clover - - - 30 - -
Persian clover - - - - 18 -
Alfalfa - - - - - 20
Table 3. Agro-technological operations.
Table 3. Agro-technological operations.
Technological operation (machinery/depth/material rate)/Treatments K1 K2 LUP PUD PED MEL
Deep ploughing o o o o o o
Pre-sowing cultivation o o o o o o
Fertilization (N15 P45 K87 kg ha−1) o o o o o o
Maize sowing o o o o o o
Intercrops sowing - - o o o o
Inter-row loosening (2–3 cm depth) ooo o o o o o
Intercrops mulching - oo oo oo oo oo
Low harvester load biomass harvesting o o o - - -
High harvester load biomass harvesting - o - o - o
Notes: K1 - inter-row mellowing (control 1); K2 - inter-row mulching with weeds - permaculture element (control 2); LUP - intercropping and mulching with faba bean; PUD - intercropping and mulching with crimson clover; PED - intercropping and mulching with Persian clover; MEL - intercropping and mulching with alfalfa. A dash means that no operation was performed, and a tick indicates the number of operations performed.
Table 4. Technical indicators of technological operations.
Table 4. 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
Intercrops 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
Intercrops 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 5. Energy equivalents in agro-technologies.
Table 5. Energy equivalents in agro-technologies.
Indices Energy equivalent Reference
Inputs:
Human labour (MJ h−1) 1.96 [18]
Diesel fuel (MJ L−1) 56.3 [18]
Agricultural machinery (MJ h−1) 357.2 [19]
Seeds of maize (MJ kg−1) 16.6 [18]
Seeds of faba bean (MJ kg−1) 21.0 [20]
Seeds of clover 11.0 [21]
Seeds of alfalfa 11.9 [22]
N (MJ kg−1) 60.6 [18]
P2O5 (MJ kg−1) 11.1 [18]
K2O (MJ kg−1) 6.7 [18]
Outputs:
Maize biomass (MJ kg−1 dry matter) 17.7 [23]
Faba bean biomass (MJ kg−1 dry matter) 17.0 [24]
Clover biomass (MJ kg−1 dry matter) 11.6 [25]
Alfalfa biomass (MJ kg−1 dry matter) 11.0 [25]
Table 6. CO2 equivalents in agro-technologies.
Table 6. CO2 equivalents in agro-technologies.
Inputs CO2 equivalent Reference
Diesel fuel (kg CO2eq L−1) 2.76 [26]
Agricultural machinery (kg CO2eq MJ−1) 0.071 [27]
Seeds of maize (kg CO2eq kg−1) 15.3 [28]
Seeds of legumes (kg CO2eq kg−1) 0.22 [29]
N (kg CO2eq kg−1) 1.30 [1]
P2O5 (kg CO2eq kg−1) 0.20 [1]
K2O (kg CO2eq kg−1) 0.15 [1]
Table 7. Energy inputs of technological operations and materials in crop biomass production systems, MJ ha−1.
Table 7. Energy inputs of technological operations and materials in crop biomass production systems, MJ ha−1.
Inputs K1 K2 LUP PUD PED MEL
Human labor 9.2 8.6 10.5 10.5 10.5 10.5
Diesel fuel 3749.6 3896.0 4436.4 4909.4 4909.4 4909.4
Agricultural machinery 1682.4 1575.2 1911.0 1911.2 1911.2 1911.0
Seed of maize (30 kg ha-1) 498.0 498.0 498.0 498.0 498.0 498.0
Seed of faba bean (200 kg ha-1) - - 4200.0 - - -
Seed of clover (30 and 18 kg ha-1) - - - 330.0 198.0 -
Seed alfalfa (20 kg ha-1) - - - - - 238.0
N 909.0 909.0 909.0 909.0 909.0 909.0
P2O5 499.5 499.5 499.5 499.5 499.5 499.5
K2O 582.9 582.9 582.9 582.9 582.9 582.9
Total energy input 6438.7 6477.3 8347.8 9650.5 9518.5 9558.3
Notes: K1 - inter-row mellowing (control 1); K2 - inter-row mulching with weeds - permaculture element (control 2); LUP - intercropping and mulching with faba bean (LUP); PUD - intercropping and mulching with crimson clover (PUD); PED - intercropping and mulching with Persian clover (PED); MEL - intercropping and mulching with alfalfa.
Table 8. Productivity and energy indices of different single- and binary- maize cultivations.
Table 8. Productivity and energy indices of different single- and binary- maize cultivations.
Treat-ments Harvest
kg ha−1 of dried biomass		 Energy input
MJ ha−1 		Energy output
MJ ha−1 		Energy efficiency ratio Net energy
MJ ha−1
Maize Intercrop Total
K1 21800.0 - 21800.0 6438.7 385860.0 59.9 379421.3
K2 20500.0 - 20500.0 6477.6 362850.0 56.0 356372.7
LUP 17600.0 4268* 21868.0 8347.8 384008.0 46.0 375660.2
PUD 16600.0 569 17169.0 9650.5 300420.0 31.1 290769.5
PED 16100.0 561 16661.0 9518.5 291478.0 30.6 281959.5
MEL 12900.0 275 13175.0 9558.3 231355.0 24.2 221796.7
Notes: K1 - inter-row mellowing (control 1); K2 - inter-row mulching with weeds - permaculture element (control 2); LUP - intercropping and mulching with faba bean (LUP); PUD - intercropping and mulching with crimson clover (PUD); PED - intercropping and mulching with Persian clover (PED); MEL - intercropping and mulching with alfalfa. * - theoretical number based on Romaneckas et al. [37]. In our experiment, the faba beans were cut and mulched because due to poor germination and development, the inter-rows were mainly covered with weeds.
Table 9. GHG emissions from a single- and binary- maize cultivation.
Table 9. GHG emissions from a single- and binary- maize cultivation.
Indices/Treatments K1 K2 LUP PUD PED MEL
Diesel fuel (kg CO2eq ha−1) 183.8 191.0 217.5 240.7 240.7 240.7
Agricultural machinery (kg CO2eq ha−1) 119.4 111.8 135.7 135.7 135.7 135.7
Seed (kg CO2eq ha−1) 459.0 459.0 503.0 465.6 463.0 463.4
Fertilizer (kg CO2eq ha−1) 41.6 41.6 41.6 41.6 41.6 41.6
Total GHG emission (kg CO2eq ha−1) 803.8 803.4 897.8 883.6 881.0 881.4
Notes: K1 - inter-row mellowing (control 1); K2 - inter-row mulching with weeds - permaculture element (control 2); LUP - intercropping and mulching with faba bean (LUP); PUD - intercropping and mulching with crimson clover (PUD); PED - intercropping and mulching with Persian clover (PED); MEL - intercropping and mulching with alfalfa.
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