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Article

Effect of Energycane Integration on Ground-Dwelling Arthropod Biodiversity in a Sugarcane-Sweet Corn Cropping System

by
Amandeep Sahil Sharma
1,
Ricardo A. Lesmes-Vesga
1,
Simranjot Kaur
1,
Hardeep Singh
2 and
Hardev Singh Sandhu
1,*
1
Everglades Research and Education Center, University of Florida, 3200 E Canal St S, Belle Glade, FL 33430, USA
2
West Florida Research and Education Center, University of Florida, 5988 US-90, Milton, FL 32583, USA
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1685; https://doi.org/10.3390/agronomy15071685
Submission received: 5 June 2025 / Revised: 7 July 2025 / Accepted: 8 July 2025 / Published: 12 July 2025
(This article belongs to the Section Innovative Cropping Systems)
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Abstract

Integrating bioenergy crops into existing agricultural systems may influence soil biodiversity, yet evidence remains limited for second-generation bioenergy crops such as energycane. This study examined the impact of energycane integration on soil arthropod communities in the Everglades Agricultural Area, Florida, compared to traditional sugarcane and sweetcorn cropping systems. Over two crop cycles (plant cane and first ratoon), soil arthropod abundance and diversity were assessed using pitfall traps. Energycane and sugarcane, both perennial crops, showed no significant differences in order richness or Shannon diversity. Similarly, when energycane was compared with sugarcane and sweetcorn (during the first sampling), it had similar arthropod abundance. However, sweetcorn remained fallow in the second and third samplings, attracting arthropods like fire ants and earwigs, particularly due to pigweed. Diversity metrics based on Hill numbers revealed a decline in the effective abundance of ground-dwelling arthropods with increasing diversity order, influenced by differences in sampling duration. Importantly, no previous studies have been found that have reported on the effects of energycane integration into the existing cropping system on soil arthropod biodiversity. These findings highlight that energycane supports biodiversity levels comparable to sugarcane cropping systems with no negative impacts on soil arthropod abundance. This study underscores the need to consider soil biodiversity impacts when evaluating sustainable bioenergy crop transitions and the potential ecological trade-offs of perennial cropping systems.

1. Introduction

The increase in demand for bioenergy production to meet the requirements of an increasing population globally requires large footprints in terms of land for its production [1], and these land-use changes can affect the region’s biodiversity [2,3]. Cultivating feedstock for biofuels may impact biodiversity through several motivating factors, such as land-use shifts, overuse, contamination, alien species, and climatic change [4,5]. These effects can have a negative or positive impact on the ecosystem of that habitat [6]. Depending on factors including the farming system type, regions, period taken into consideration, and geographical scale, energy farming might lead to changes in biodiversity. Furthermore, the generation of biomass can have either immediate consequences, such as the conversion of native or inanimate ecosystems into bioenergy crops, or unforeseen consequences, such as the conversion of natural vegetation into land utilization kinds that are eliminated elsewhere by the production of energy crops [7,8]. Numerous national and international studies [4,7,8,9,10,11] have been conducted and published that assess or investigate the ecological viability of biofuel crop production. Diverse findings have been reported in publications about the impacts of biofuel crop production on biodiversity, indicating that there is presently no universally acknowledged approach to measure these impacts [4,7,11]. Specific reviews (e.g., [12,13,14] usually concentrate on the effects of biodiversity for a given crop or crops in a given region.
However, few reports have been made that biodiversity and ecosystems have been affected by bioenergy production. Still, for integrating new energy crops, the effect of energy crop production on biodiversity is a critical step [2]. A study on bioenergy crops showed that planting non-food, perennial biomass crops, such as grasses and trees used in short-rotation coppicing, can help enhance agricultural sustainability. These advantages include improved ecosystem services and biodiversity [15]. Similar findings were seen in other studies, where the perennial bioenergy crops such as switchgrass (Panicum virgatum L.) and silvergrass (Miscanthus spp.) positively impact the specific regions’ biodiversity [16]. Similar results were obtained when fields planted with switchgrass (second-generation biofuel crops) and prairie species supported significantly higher numbers of plant varieties, methane-oxidizing microbes, insects, and bird populations than those planted with maize (Zea mays). Although maize yielded more biomass, other ecological benefits, such as methane uptake, natural pest regulation, pollination, and bird conservation, were notably enhanced in perennial grasslands [17].
In research focusing on ant diversity and related ecosystem functions in bioenergy landscapes across the Midwestern U.S., perennial native biofuel crops hosted as many as 185% more ant species and delivered up to 55% greater pest control than maize systems [18]. A broader investigation on mixed cropping, which evaluated annual crops, single-species perennials, and mixtures of perennials, showed that biodiversity advantages over maize were especially prominent in mixed-species systems. Conversely, two energy sorghum-based setups displayed species richness levels comparable to or even below those found in maize [19].
Furthermore, better management approaches can lower the danger of biodiversity loss in certain areas and improve landscape design when energy crops are grown on marginal or low-productive lands [20,21]. Diversity improvements over maize in simpler perennial crop systems were typically modest and paled compared to the substantial gains seen in diverse perennial mixtures, beyond a few specific investigations, such as [22] on earthworm diversity in Brazilian sugarcane and the biodiversity impacts of high-sugar energy sorghum in the Midwestern U.S. [19]. Little attention has been given to the biodiversity outcomes in energycane (EC) production systems.
The research on integrating bioenergy crops for biomass production has at least no negative impact on the soil biodiversity of the region; it has a positive effect, as due to land-use changes, there is an increase in species diversity and relative abundance of these species in the region where bioenergy crops were integrated. In addition, the bioenergy crops provided richness in the taxa and supported various ecological activities in the area where they were incorporated [17].
In the Everglades Agricultural Area (EAA) in Florida, United States, most of the areas have been cultivated under sugarcane (SC) and sweetcorn (SW) cropping systems. Still, due to an increase in the soil subsidence of organic soil, the marginal areas are increasing where SC production is not viable. Therefore, a cropping system is required to sustain these marginal areas. Still, there remains a question about integrating a new cropping system into an area and its impact on the native biodiversity. EC (high fiber sugarcane) is an alternative solution for biofuel production integration into these marginal areas. Because of cultural practices similar to SC, its integration into these areas is easily feasible. In Florida, a few cultivars of EC were tested in 2014 to sustain under marginal or degraded areas, which were released in 2016 [23,24,25]. However, there are no reports of EC integration into the SC-SW cropping system or its effects on the abundance and diversity of soil arthropods. In addition, no reports were found of soil biodiversity affected by the EC cropping system in Florida. This study can be helpful in seeing the integration of bioenergy crops into the traditional cropping system and assessing their positive or negative impact on the ground-dwelling arthropod abundance. Therefore, this project aims to evaluate the effect of the integration of EC into SC and SW cropping systems on the soil arthropod abundance and biodiversity.

2. Materials and Methods

2.1. Experimental Site and Design

This research was conducted at the Everglades Research and Education Center (EREC), University of Florida, Institute of Food and Agricultural Sciences (IFAS), in Belle Glade, Florida, United States. The coordinates of the experimental plot were 26°39′18.23” N and 80°37′55.58” W. The soil at this location was classified as Histosol, the most common soil type in the EAA. This soil was high in organic matter; however, due to carbon oxidation over the past several decades, it had become shallow, with less than 1 m of depth to bedrock in most fields. The shallow soil was characterized by high pH and undulating slopes, resulting in widespread flooding in some areas with less soil depth.
Soil properties such as high pH, shallow depth, and slopes of <1% with poor drainage qualified these soils as marginal [26]. The climate at the site is subtropical, with a 10-year annual average precipitation of 1310 mm and an average temperature of 21.3 °C.
This experiment was conducted during 2022–2024, and the experimental design consisted of 48 research plots of approximately 350 m2 each. Three crops, EC, SC, and SW, were grown in a randomized complete block design (RCBD) under two nitrogen and two phosphorus fertilization treatments with four replications, arranged in a factorial design: 3 crops × 2 N rates (recommended and recommended + 50 kg N/ac) × 2 P rates (recommended and recommended + 50 kg P/ac) × 4 replications. An Integrated Landscape Management (ILM) approach was employed, wherein EC was integrated into the identified marginal areas of the existing SC and SW cropping systems. Cultivars were selected based on performance documented in the literature and by commercial growers in the EAA. The EC cultivar used was UFCP 84-1047 [23], the SC cultivar was CP 96-1252 [27], and the SW cultivar was Obsession. SW was grown once each year from March to July. For the remainder of the year, the SW plots were left fallow (SWF) and were managed using minimal herbicide applications and cultivation. For SC and EC, plots were maintained with pre- and post-emergence herbicides; for example, Atrazine @ 1 qt/ac + Armezon @ 0.75 oz/acre was applied 35 days after planting. Metrixore @ 1 lb/ac + Atrazine @ 2 qt/acre was applied 60 days after planting. Atrazine 1 qt/ac + Armezon 1/5 oz/ac was applied 95 days after planting. Atrazine 1 qt/ac + Explorer 3 oz/ac was applied 150 days after planting in the plant cane. No herbicide was applied in the first ratoon in SC and EC. Soil arthropod data were collected in three sampling occasions over a period of two years (plant cane and first ratoon).
The ILM layout was developed before planting using a combination of Geographic Information Systems (GISs) and biophysical modeling, based on soil characteristics, topography, yield potential, water quality, and hydrology. Crops were planted according to the maps generated, with alleys left for equipment access.

2.2. Soil Biodiversity Sampling Using Pitfall Traps

Soil arthropods were sampled using double-layered pitfall traps, following the design developed in 2016 [28]. The outer layer of each trap consisted of polyvinyl chloride (PVC) pipes with an internal diameter and height of 152 mm. A 6 mm diameter PolyTetraFluoroEthylene tubing was slit and lined around the upper rim of the PVC pipe. The pipe was installed in a hole within the interior crop row, with its upper 6 mm extending above the soil surface to prevent rainwater and debris from entering the collection jar.
The inner layer consisted of a transparent plastic funnel (152 mm top opening, 50 mm bottom opening) resting on the Teflon tubing and secured with Gorilla tape. A transparent collection jar (114 mm internal diameter, 102 mm height, 700 mL capacity) was placed below the funnel inside the PVC pipe. It was half-filled with ethylene glycol (antifreeze) to preserve specimens. A 254 mm plastic cover plate supported by wooden dowels was positioned above each trap to shield it from rainfall.
One pitfall trap was deployed in each plot of EC, SC, and sweetcorn as a rotational crop (SWRC), which includes SW and SWF. Arthropod sampling occurred at three timepoints: after planting (S1), mid-season (S2), and before harvesting (S3) in both the plant cane and first ratoon. For each deployment, jars were prepared with ethylene glycol, labeled, inserted into the traps, and retrieved after two to four weeks. The traps remained deployed for more time in case the collection jars were empty to collect some arthropods for analysis. This changes the average time in deployment of the traps. The collection jars sometimes did not collect the arthropods because of leakage, improper setup of pitfall traps, or rainwater leaking into the collection jars. After retrieval, the jars were tightly sealed and transported to the Sugarcane Agronomy Lab at the University of Florida for specimen identification. The summary of the total trap deployment in the plant cane and the first ratoon from different sampling times is mentioned in Table 1.

2.3. Soil Biodiversity Sample Processing

In the lab, the contents of the collection jars were sieved and rinsed with tap water to remove soil and debris. The cleaned samples were transferred into containers filled with 70% ethanol and poured through a Büchner funnel lined with filter paper for rapid drainage. The retained samples were transferred into 145 mm diameter petri dishes using tweezers. Arthropods were identified and counted to the taxonomic order level [29].

2.4. Statistical Analysis

The daily trap catch was used to calculate the number of arthropods collected in a day from all the crops. The daily trap catch was determined by dividing the total number of ground-dwelling arthropods by the time the trap was deployed. Hill numbers were used to measure biodiversity. [30,31]
For the Hill Numbers, the formula applied (Equation (1)) was
D q = ∑ i = 1 S p i q 1 1 − q
where D is the diversity measure of order q, pi is the proportion of individuals in the Order i, S is the number of orders, and q is the Diversity order (O for order richness, 1 for Shannon diversity).
For order richness, the formula applied (Equation (2)) was
D q = ∑ i = 1 S 1
Order richness is the number of different taxonomic orders in an assemblage that does not count the relative abundance of each orders and instead values each order equally [31,32]. With the diversity order q = 1,
Shannon diversity has a formula (Equation (3)) to calculate the relative abundance of each order, i.e., frequency [31].
D 1 = e x p − ∑ i = 1 S p i ln p i = e x p H
All the data for soil biodiversity were analyzed using R statistical software (V 4.1.2) [33]. The mixed-effect model to analyze the Analysis of Variance (lme package [34]) was used to investigate the effect of cropping system, sampling time, fertilizer treatments, and different years on daily trap catch, order richness, and Shannon diversity. The crops, sampling time, fertilizer treatments, and two years were considered fixed effects, whereas the replication was considered random. The collected data were assessed for compliance with model assumptions. Model validation was conducted using residual plots and quantile-quantile (Q-Q) plots to ensure the appropriateness of the statistical approach. A significance level of 0.05 was used in the experiment, and all analyses were considered significant if p-values were less than 0.05. Tukey’s Honestly Significant Difference (HSD) test (emmeans package [35]) was used to compare the means of each fixed factor and the interaction between each fixed factor at α = 0.05.

3. Results

3.1. Daily Traps Catch Across Crop Types and Sampling Times

A total of 288 traps were collected for all the crops in two years, with 96 traps each for EC, SC, and SW (Table 1). The mean trap duration in the plant cane was 30 days, while in the first ratoon it was 15 days. The plant cane had a larger duration of trap deployment due to crop establishment in the plant cane taking longer than the first ratoon. Overall, 14,605 soil arthropods were collected over two years of data collection and identified as soil arthropods based on the taxonomic order. Of 14,605 soil arthropods, 4503, 4202, and 5900 were collected from EC, SC, and SW, respectively (Table 1). The traps also collected other arthropods (not ground-dwelling) that had not been identified and discarded during the identification process, for example, house flies, butterflies, bees, and wasps. The daily trap catch interacted significantly with crops and year (p = 0.023). Therefore, the results have been analyzed separately for each year, i.e., plant cane (Year 1) and the first ratoon (Year 2) (ANOVA Table 2 and Table 3).
In the plant cane, the daily trap catch showed no significant difference between EC, SC, and SW during the S1 (Table 2), where EC had an average of 2.2, SC had an average of 2.0, and SW had a 2.79 average of soil arthropod catch per day. During S2 and S3, SW plots remained fallow (SWF), with an average of 3.43 arthropod abundance across the sampling time and treatments (Figure 1). The fertilizer treatments applied to the crops did not significantly affect the daily trap catch in all the crops. EC (2.1) and SC (2.2) had similar arthropod abundance but were considerably lower than the SW.
In the first ratoon, during S1, EC (1.7), SC (1.6), and SW (1.75) had similar arthropod abundance with no significant differences (Table 3) across all the treatments (Figure 1). Overall, EC (1.7) and SW-SWF (SW 1.75 and SWF 1.89 with an average of 1.8) had the highest arthropod abundance compared to SC (1.3) when averaged across the sampling time and treatments. Like the plant cane, in the first ratoon, no significant effect of treatments was observed on daily trap catch. In addition, there was no significant difference between the crops observed in the first ratoon for arthropod abundance.
When combined for the plant cane and first ratoon, EC had 1.31, while SC and SWRC (SW + SWF) had 1.38 arthropod abundance across all the samplings and treatments (Supplementary Figure S1).

3.2. Soil Arthropods Distribution in Taxonomic Orders

The abundance of soil arthropods in different taxonomic orders showed variability with the crops and sampling time, in both years, followed by a two-way interaction between these parameters. Therefore, data for the plant cane and first ratoon have been analyzed separately for both years (Supplementary Table S2). The species obtained in the pitfall traps were earwigs (Forficula auricularia), fire ants (Solenopsis spp.), ground beetles (Carabus nemoralis), rove beetles (Staphylinus olens), wolf spiders (Hogna spp.), other spiders, crickets (Gryllus assimilis), delphacids (Tagosodes orizicolus), blatella (Blattella germanica), and sap beetles (Carpophilus spp.). These species were categorized based on different taxonomic orders. In total, there were seven different taxonomic orders, such as Araneae, Blattodea, Coleoptera, Dermaptera, Hemiptera, Hymenoptera, and Orthoptera. The average numbers of each taxonomic order are given in the Supplementary Table S1. In the plant cane, in the SWRC (SW + SWF), taxonomic orders Dermaptera and Hymenoptera had significantly higher abundance than EC and SC. In the taxonomic orders Araneae, Blattodea, Coleoptera, Hemiptera, and Orthoptera, all the crops had similar abundance in the plant cane. The highest abundance was also observed in Dermaptera and Hymenoptera compared to other taxonomic orders in the plant cane, across all the crops. In contrast, no differences in all the taxonomic orders were observed between the crops in the first ratoon (Figure 2).
The soil arthropod distribution among different taxonomic orders also varied seasonally with the sampling time from each crop in both years. In the plant cane, SWF had a higher abundance of Dermaptera, Hymenoptera, and Orthoptera in S2 and S3, while the abundance of Coleoptera was higher in S2 only. During S1, the Blattodea (Blatella) population was significantly higher (p < 0.05) in SW than EC and SC in the plant cane (Figure 3). On the other hand, no significant trend was observed in different sampling times in the first ratoon between the crops. However, in the plant cane, Blattodea, Dermaptera, and Hymenoptera had the highest abundance, while in the first ratoon, Araneae and Hymenoptera had the highest abundance among all the crops (Figure 2).
Over both years, the SWRC (SW + SWF) had a significantly higher abundance of Blattodea, Coleoptera, and Dermaptera than SC and EC. The Blattodea was dominated by Blattella (Common insect of SW), Coleoptera by ground beetle and rove beetle, and Dermaptera by earwigs, Orthoptera by crickets. The arthropod abundance of Araneae, Hemiptera, Hymenoptera, and Orthoptera was similar in EC and SW, with a significantly lower abundance of these taxonomic orders in the SC crop. Combined across all the samplings, EC had an arthropod abundance similar to that of the SC cropping system, while the SWRC had the highest.

3.3. Order Richness

Order richness was used to calculate the number of categories identified for soil arthropods, which includes different taxonomic orders in this study. The order richness showed variability with crops and sampling time in both years (ANOVA Table 2 and Table 3).
In plant cane, in S1 and S2, there was no significant difference in the order richness of all three crops. However, during S3, the data collected from SWF showed variability in order richness compared to EC and SW. The interaction between sampling time and cropping system showed that SWF had a significantly (p < 0.05) higher order richness during S3. During S1 and S2, EC had a similar order richness to SW and SC in the plant cane (Figure 4). Overall, the highest order richness of 6.1 in the plant cane was observed in the SWRC (SW + SWF), followed by EC and SC with 5.31 and 5.18, respectively. Averaged across different sampling times, the SWRC (SW + SWF) had significantly (p < 0.05) higher order richness than EC and SC in the plant cane. However, EC had similar order richness as compared to SC in the plant cane.
In the first ratoon, contrasting results were obtained than the plant cane. During S1, EC had similar order richness compared to SW and SC, but SC had significantly lower order richness compared to SW. On the other hand, during S2, EC and SC had significantly lower order richness as compared to SW. No differences were observed in S3 between the crops. Unlike the plant cane, no trend was observed during the different sampling times in the first ratoon for order richness between the crops (Figure 4). Overall, in the first ratoon, SWRC (SW + SWF) had a significantly higher order richness of 5.38, followed by EC with 4.52, and SC with 4.35 when averaged across all the samplings. No significant differences were observed in EC and SC for order richness in the first ratoon. The EC had order richness similar to the SC and SW cropping systems.

3.4. Shannon Diversity

Shannon diversity is used to calculate the equal weightage of each individual order across all the taxonomic orders. The Shannon diversity varied significantly with years, crops, and sampling times (ANOVA Table 2 and Table 3). Shannon diversity analysis for S1 showed that EC had significantly higher Shannon diversity than SW, similar to SC in the plant cane. During S3, SWF showed variability in the Shannon diversity, resulting in significantly higher values than EC and SC. The interaction between the sampling time and cropping system showed that EC had significantly (p < 0.05) higher Shannon diversity than SW during S1 in the plant cane. In contrast, during S3, SWF had the highest Shannon diversity among EC and SC. EC had similar Shannon diversity to the SC cropping system in the plant cane (Figure 5). Averaged across all the sampling, the SWRC (SW+SWF) had the highest Shannon diversity of 1.55 in the plant cane, followed by EC with 1.4 and SC with 1.3.
In the first ratoon, during S1, no significant differences were observed between EC, SC, and SW for Shannon diversity. However, SWRC (SW+SWF) had a significantly (p < 0.05) higher Shannon diversity than EC and SC when averaged across the different samplings. In the first ratoon, SW had a Shannon diversity of 1.6, EC had 1.48, and SC had 1.35. Unlike the plant cane, no trend of Shannon diversity was observed in the first ratoon in all the samplings between the crops (Figure 5). EC had a similar Shannon diversity in the first ratoon compared with the SC-SW cropping system.

4. Discussion

This study compared bioenergy crops (second-generation) with conventional cropping systems or first-generation biofuel (SC in our case) regarding soil arthropod abundance and diversity at a high taxonomic level. First-generation biofuel crops are those rich in oil, starch, or sugar, while second-generation biofuel sources consist of lignocellulosic plants and rapidly growing tree species [36]. However, sugarcane, a first-generation biofuel crop, is being used as a source of sugar in our study. A global synthesis analysis revealed that areas cultivated with first-generation biofuel crops experienced reductions of 37% in species richness and 49% in species abundance compared to conventional vegetations [37]. In contrast, second-generation biofuel plantations showed smaller declines, with species richness and abundance decreasing by 19% and 25%, respectively. Comparable findings were also documented in a global meta-analysis [38].
In this study, we compared the soil arthropod diversity of EC with SC and SW cropping systems in Florida using data collected over two years (plant cane and first ratoon). The treatments were applied in order to see their effects on biomass production and, therefore, their effects on the ground-dwelling arthropods. Overall, no effect of treatments was observed on the different diversity orders. Opposite results were obtained in soybean [38], where the application of organic fertilizer in combination with other fertilizers significantly affected the soil biodiversity [39]. We found no significant order richness or biodiversity disadvantage for growing EC between the SC-SW conventional cropping systems. In contrast, when averaged across both years, the EC showed order richness similar to the SC cropping system. Similar results were obtained in a study between EC and SC for soil arthropod abundance in Florida [40].
However, the SWRC (SW and SWF) had significantly higher order richness and diversity than EC. SW remained fallow for most of the year with a decent population of weeds growing in the fallow lands. Due to difficulties maintaining the experimental setup, the cultivation and herbicide application were minimized in the fallow land. EC and SC have similar order richness and arthropod abundance, with no harmful impact of EC integration into the SC-SW cropping system.
In a study in northeast Germany, comparing three energy crops, it was found that biodiversity and order richness were highest in energy crop fields and lowest in silage maize. The enhanced biodiversity in energy crops was linked to the more complex vegetation structure [41]. Invertebrates play a fundamental role in agricultural biodiversity and are frequently associated with weedy vegetation [42]. A high plant species diversity supports more complex and varied arthropod communities [43]. Similar research observed that the highest number of invertebrate families occurred in field margins and open biomass crop fields, where plant diversity was greater [44]. The findings in a meta-analysis showed that off-field habitats like field margins and hedgerows are more effective in enhancing biodiversity than in-field practices such as organic farming [45]. Similarly, results have revealed that the fallow areas in the miscanthus field resulted in a higher weed population that promotes biodiversity [12].
Our study’s ILM design follows minimal tillage during the crop season. As perennial crops, EC and SC require fewer inputs and cultivations than SW, an annual crop [12]. The perennial crops (EC and SC) have a C4 photosynthetic pathway that allows greater efficiencies in using light, water, and nutrients compared to SW (C4 annual species) [46]. Lower input demands, such as reduced use of pesticides, fertilizers, water, and minimal soil disturbance from tillage or cultivation, can enhance habitat stability and support higher arthropod diversity and abundance [17,47,48]. However, recent research comparing biodiversity outcomes between perennial and annual crops has yielded mixed findings.
In this study, seven different taxonomic orders were evaluated in all the crops at different sampling times. The abundance of these different taxonomic orders at different sampling times showed their distribution based on the time of the year. For example, the Dermaptera order had higher abundance in S1 only, while the Araneae and Blattodea had higher abundance in S2 and S3. This could result from different weather conditions and other environmental parameters during the different sampling times in the experiment. On the other hand, the abundance of Coleoptera and Hymenoptera showed that these can be beneficial in the SC and EC. These orders are the predators for several pests that occur at different stages and serve as natural control agents against them [49,50]. Similar results were observed in our findings. The abundance of wolf spiders and other spiders (Araneae) in all the crops showed that the crops have several predators for pests as a natural control. The EC had a similar number of predators as compared to the SC-SW cropping system. Therefore, there is no negative impact of EC on natural predators of the EAA regions. Similar findings have been observed in SC, in Brazil, where several predators belonging to Araneae, Coleoptera, and Hymenoptera are beneficial as natural agents for several pests [49,51]. However, the dynamics of these predators have not been revealed much, and no other information was observed about the diversity of spiders in the sugarcane and sweetcorn cropping system in EAA. However, only one study found the abundance of spiders in the organic sugarcane production [49]. Therefore, this study suggests that there is an abundance of different taxonomic orders in EC that are comparable to the SC-SW cropping system, and EC had no negative impact on the ground-dwelling arthropods and had similar predators as SC and SW in the EAA.
A study comparing annual, single-perennial, and mixed-perennial cropping systems found that mixed-perennial systems offered significantly greater biodiversity benefits over maize than single-perennial systems for most plant and animal groups [19]. In contrast, energy sorghum systems showed order richness equal to or lower than that of maize. While single perennial crops provide benefits such as reduced inputs and improved carbon storage, nutrient retention, and soil moisture, the most significant conservation benefits for microorganisms were found in mixed perennial systems with higher plant diversity [52]. The abundance of ground beetles and richness in perennial versus annual crops were site-specific, with no apparent differences in species composition [53]. Similar results were reported for variable arthropod diversity over time in perennial energy crops in eastern Poland, without consistent evidence of a positive impact [54]. A study on Miscanthus found lower spider and ground beetle diversity in Miscanthus than in nearby conventional agricultural land [32]. However, a meta-analysis involving three perennial grasses and five taxonomic groups showed significantly greater biodiversity for plants and small mammals, with non-significant but positive trends for arthropods and birds, and neutral effects on earthworms [55].
The differences in conclusion from the above studies can be attributed to the perennial crops, annual crops, and fallow land availability during the experimental setup. Our study’s perennial crops (EC and SC) have similar arthropod abundance, order richness, and Shannon diversity, but significantly less than the annual crop (SW). The observed increase was linked to pigweed (Amaranthus viridis) in the SW(SWF) fallow plots. Species of Amaranthus are known to provide a habitat for various predatory insects, such as fire ants, earwigs, and ground beetles like Lebia analis. Moreover, the abundance of these predator species is closely tied to the density of weed cover [56].
The Hill number serves as a base for measuring the biodiversity [30,31], where Hill diversity of order 0 represents the order richness (number of orders in a taxa) and order one represents Shannon diversity (equal weightage of each individual). The effective number of species usually decreases with increased diversity as the highest order emphasizes dominant species. In this study, the number of orders decreased from 6 to 1.5 with the increasing order of diversity, i.e., from order richness to Shannon diversity. This happened because of the variability in the duration of trap deployment, as the mean time for trap deployment in the plant cane was 30 days, while the mean time in the first ratoon was 15 days. This resulted in more species in the plant cane compared with the first ratoon. However, the abundance of arthropods was similar when calculated using daily trap catch in both years. Similar results were observed in a study, where the effective number of orders decreases with an increasing order of diversity [57]. There were minimal studies where the integration of crops in the existing cropping system has been analyzed for arthropod abundance and diversity. Our study highlights the importance of studying the negative impact on the soil biodiversity of the region while trying to integrate a new cropping system into an existing or conventional cropping system.

5. Conclusions

This study investigated the impact of integrating an energy crop (EC) into the SC-SW cropping system on soil arthropod biodiversity. Ground arthropod sampling throughout the growing seasons revealed that arthropod abundance and order richness were significantly higher in the SWRC (SW + SWF), likely due to increased weed diversity during fallow periods; no significant differences in either metric between EC or SC systems were found. These findings indicate that incorporating EC into the SC–SW conventional cropping system does not negatively affect soil arthropod abundance or diversity. Additionally, diversity metrics showed a decline with increasing Hill number order, reflecting a shift from order richness to dominance-based indices. The decrease in order numbers from order richness to Shannon diversity was observed in this study. These results emphasize the importance of assessing biodiversity outcomes when modifying cropping systems. Future research should explore how such integrations can be optimized to support agricultural productivity and ecosystem services.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071685/s1, Figure S1: Daily Trap catch was analyzed across all the treatments, sampling time, and year in different cropping systems. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW and SWF samplings; Table S1: Abundance of each taxonomic order in each crop in the plant cane and first ratoon at different sampling times. The numbers represent the Mean of each arthropod collected at each sampling across each treatment; Table S2: Summary of ANOVA (fixed effects) for various response variables measured in the plant cane and the first ratoon in EC, SC, and SW for taxonomic orders. Crop refers to the different crops, Treatment refers to the fertilization treatments, and sampling time refers to different samplings in both years. Taxonomic orders refer to different ground-dwelling taxonomic orders observed in both years.

Author Contributions

A.S.S.: Data collection, conceptualization, investigation, validation, writing—original draft. R.A.L.-V.: Data collection, reviewing, and editing. S.K.: Writing, reviewing, and editing. H.S.: Writing, reviewing, and editing. H.S.S.: Validation, supervision, writing, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (BETO), under contract number DE-EE0009281.

Data Availability Statement

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We appreciate the efforts of the editors and anonymous reviewers whose thoughtful feedback and recommendations enhanced the clarity and rigor of this study.

Conflicts of Interest

The authors declared that there are no conflicts of interest regarding the publication of this article.

Abbreviations

The following abbreviations are used in this manuscript:
ECEnergycane
SCSugarcane
SWSweetcorn was grown
SWFSweetcorn Fallow plots
SWRCSweetcorn as Rotation Crop
EAAEverglades Agricultural Area
ILMIntegrated Landscape Management

References

  1. McDonald, R.I.; Fargione, J.; Kiesecker, J.; Miller, W.M.; Powell, J. Energy sprawl or energy efficiency: Climate policy impacts on natural habitat for the United States of America. PLoS ONE 2009, 4, e6802. [Google Scholar] [CrossRef] [PubMed]
  2. Edwards, F.A.; Edwards, D.P.; Larsen, T.H.; Hsu, W.W.; Benedick, S.; Chung, A.; Vun Khen, C.; Wilcove, D.S.; Hamer, K.C. Does logging and forest conversion to oil palm agriculture alter functional diversity in a biodiversity hotspot? Anim. Conserv. 2014, 17, 163–173. [Google Scholar] [CrossRef] [PubMed]
  3. Immerzeel, D.J.; Verweij, P.A.; Van Der Hilst, F.; Faaij, A.P. Biodiversity impacts of bioenergy crop production: A state-of-the-art review. GCB Bioenergy 2014, 6, 183–209. [Google Scholar] [CrossRef]
  4. Dornburg, V.; van Vuuren, D.; van de Ven, G.; Langeveld, H.; Meeusen, M.; Banse, M.; van Oorschot, M.; Ros, J.; van den Born, G.J.; Aiking, H. Bioenergy revisited: Key factors in global potentials of bioenergy. Energy Environ. Sci. 2010, 3, 258–267. [Google Scholar] [CrossRef]
  5. MEa, M.E.A. Ecosystems and Human Well-Being: Wetlands and Water Synthesis; World Resources Institute: Washington, DC, USA, 2005. [Google Scholar]
  6. Hudson, L.N.; Newbold, T.; Contu, S.; Hill, S.L.; Lysenko, I.; De Palma, A.; Phillips, H.R.; Alhusseini, T.I.; Bedford, F.E.; Bennett, D.J. The database of the PREDICTS (projecting responses of ecological diversity in changing terrestrial systems) project. Ecol. Evol. 2017, 7, 145–188. [Google Scholar] [CrossRef]
  7. Smeets, E.M.; Bouwman, L.F.; Stehfest, E.; Van Vuuren, D.P.; Posthuma, A. Contribution of N2O to the greenhouse gas balance of first-generation biofuels. Glob. Change Biol. 2009, 15, 1–23. [Google Scholar] [CrossRef]
  8. Wicke, B.; Van Der Hilst, F.; Daioglou, V.; Banse, M.; Beringer, T.; Gerssen-Gondelach, S.; Heijnen, S.; Karssenberg, D.; Laborde, D.; Lippe, M. Model collaboration for the improved assessment of biomass supply, demand, and impacts. GCB Bioenergy 2015, 7, 422–437. [Google Scholar] [CrossRef]
  9. Beringer, T.; Lucht, W.; Schaphoff, S. Bioenergy production potential of global biomass plantations under environmental and agricultural constraints. GCB Bioenergy 2011, 3, 299–312. [Google Scholar] [CrossRef]
  10. Chum, H.L.; Nigro, F.; McCormick, R.; Beckham, G.; Seabra, J.; Saddler, J.; Tao, L.; Warner, E.; Overend, R. Conversion technologies for biofuels and their use. Bioenergy Sustain. Bridg. Gaps 2015, 72, 374–467. [Google Scholar]
  11. Van Dam, J.; Faaij, A.; Hilbert, J.; Petruzzi, H.; Turkenburg, W. Large-scale bioenergy production from soybeans and switchgrass in Argentina: Part B. Environmental and socio-economic impacts on a regional level. Renew. Sustain. Energy Rev. 2009, 13, 1679–1709. [Google Scholar] [CrossRef]
  12. Dauber, J.; Jones, M.B.; Stout, J.C. The impact of biomass crop cultivation on temperate biodiversity. GCB Bioenergy 2010, 2, 289–309. [Google Scholar] [CrossRef]
  13. Fitzherbert, E.B.; Struebig, M.J.; Morel, A.; Danielsen, F.; Brühl, C.A.; Donald, P.F.; Phalan, B. How will oil palm expansion affect biodiversity? Trends Ecol. Evol. 2008, 23, 538–545. [Google Scholar] [CrossRef] [PubMed]
  14. Hartmann, H.; Daoust, G.; Bigué, B. Negative or positive effects of plantation and intensive forestry on biodiversity: A matter of scale and perspective. For. Chron. 2010, 86, 354–364. [Google Scholar] [CrossRef]
  15. Berndes, G.; Hansson, J.; Egeskog, A.; Johnsson, F. Strategies for 2nd generation biofuels in EU–Co-firing to stimulate feedstock supply development and process integration to improve energy efficiency and economic competitiveness. Biomass Bioenergy 2010, 34, 227–236. [Google Scholar] [CrossRef]
  16. Haughton, A.J.; Bohan, D.A.; Clark, S.J.; Mallott, M.D.; Mallott, V.; Sage, R.; Karp, A. Dedicated biomass crops can enhance biodiversity in the arable landscape. GCB Bioenergy 2016, 8, 1071–1081. [Google Scholar] [CrossRef]
  17. Werling, B.P.; Dickson, T.L.; Isaacs, R.; Gaines, H.; Gratton, C.; Gross, K.L.; Liere, H.; Malmstrom, C.M.; Meehan, T.D.; Ruan, L. Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proc. Natl. Acad. Sci. USA 2014, 111, 1652–1657. [Google Scholar] [CrossRef]
  18. Helms IV, J.A.; Ijelu, S.E.; Wills, B.D.; Landis, D.A.; Haddad, N.M. Ant biodiversity and ecosystem services in bioenergy landscapes. Agric. Ecosyst. Environ. 2020, 290, 106780. [Google Scholar] [CrossRef]
  19. Haan, N.L.; Benucci, G.N.; Fiser, C.M.; Bonito, G.; Landis, D.A. Contrasting effects of bioenergy crops on biodiversity. Sci. Adv. 2023, 9, eadh7960. [Google Scholar] [CrossRef] [PubMed]
  20. Manning, P.; Gossner, M.M.; Bossdorf, O.; Allan, E.; Zhang, Y.-Y.; Prati, D.; Blüthgen, N.; Boch, S.; Böhm, S.; Börschig, C. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 2015, 96, 1492–1501. [Google Scholar] [CrossRef]
  21. Sang, T.; Zhu, W. China’s bioenergy potential. GCB Bioenergy 2011, 3, 79–90. [Google Scholar] [CrossRef]
  22. Nunes, D.H.; Pasini, A.; Benito, N.P.; Brown, G.G. Earthworm diversity in four land use systems in the region of Jaguapitã, Paraná State, Brazil. Caribb. J. Sci. 2006, 42, 331. [Google Scholar]
  23. Gordon, V.S.; Comstock, J.C.; Sandhu, H.S.; Gilbert, R.A.; Sood, S.; Korndorfer, P.; El-Hout, N.; Arundale, R. Registration of ‘UFCP 84-1047’ Sugarcane for Use as a Biofuel Feedstock. J. Plant Regist. 2016, 10, 251–257. [Google Scholar] [CrossRef]
  24. Gordon, V.S.; Comstock, J.C.; Sandhu, H.S.; Gilbert, R.A.; Sood, S.G.; Korndorfer, P.; El-Hout, N.; Arundale, R. Registration of ‘UFCP 87-0053’ sugarcane for use as a biofuel feedstock. J. Plant Regist. 2016, 10, 258–264. [Google Scholar] [CrossRef]
  25. Sandhu, H.S.; Gilbert, R.A.; Comstock, J.C.; Gordon, V.S.; Korndörfer, P.; Arundale, R.A.; El-Hout, N. Registration of ‘UFCP 82-1655’ sugarcane. J. Plant Regist. 2016, 10, 22–27. [Google Scholar] [CrossRef]
  26. Duersch, B.G.; Bhadha, J.H.; Root, T.L.; Louda, J.W. The role of rice (Oryza sativa L.) in sequestering phosphorus compounds and trace elements: Speciation and dynamics. Sci. Total Environ. 2020, 725, 138366. [Google Scholar] [CrossRef]
  27. Sandhu, H.; Davidson, W. Sugarcane Cultivars Descriptive Fact Sheet: CP 96-1252, CP 01-1372 and CP 00-1101: SS-AGR-410/SC102, 12/2016. EDIS 2017, 2017, 1–4. [Google Scholar] [CrossRef]
  28. Brown, G.R.; Matthews, I.M. A review of extensive variation in the design of pitfall traps and a proposal for a standard pitfall trap design for monitoring ground-active arthropod biodiversity. Ecol. Evol. 2016, 6, 3953–3964. [Google Scholar] [CrossRef]
  29. Borror, D.; Triplehorn, C.; Johnson, N. An Introduction to the Study of Insects, 6th ed.; Saunders College: Philadelphia, PA, USA, 1989. [Google Scholar]
  30. Hill, M.O. Diversity and evenness: A unifying notation and its consequences. Ecology 1973, 54, 427–432. [Google Scholar] [CrossRef]
  31. Jost, L. Entropy and diversity. Oikos 2006, 113, 363–375. [Google Scholar] [CrossRef]
  32. Williams, M.A.; Feest, A. The effect of Miscanthus cultivation on the biodiversity of ground beetles (Coleoptera: Carabidae), spiders and harvestmen (Arachnida: Araneae and Opiliones). Agric. Sci. 2019, 10, 903–917. [Google Scholar] [CrossRef]
  33. The R Core Team. R: A Language and Environment for Statistical Computing, version 4.1.2; R Foundation for Statistical Computing: Vienna, Austria, 2024.
  34. Pinheiro, H.P.; Rodrigues-Motta, M.; Franco, G. Modelling performance of students with generalized linear mixed models. In Proceedings of the 29th International Workshop on Statistical Modelling, Göttingen, Germany, 14–18 July 2014; pp. 133–136. [Google Scholar]
  35. Lenth, R.V. Emmeans: Estimated Marginal Means, aka Least-Squares Means. 2021. Available online: https://rdrr.io/cran/emmeans/ (accessed on 7 July 2025).
  36. Mohr, A.; Raman, S. Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energy Policy 2013, 63, 114–122. [Google Scholar] [CrossRef] [PubMed]
  37. Tudge, S.J.; Purvis, A.; De Palma, A. The impacts of biofuel crops on local biodiversity: A global synthesis. Biodivers. Conserv. 2021, 30, 2863–2883. [Google Scholar] [CrossRef]
  38. Núñez-Regueiro, M.M.; Siddiqui, S.F.; Fletcher, R.J., Jr. Effects of bioenergy on biodiversity arising from land-use change and crop type. Conserv. Biol. 2021, 35, 77–87. [Google Scholar] [CrossRef] [PubMed]
  39. Hasibuan, R.; Cindowarni, O.; Lumbanraja, J.; Lumbanraja, F.R. Impact of soil fertilization on arthropod abundance and diversity on soybean agroecosystem. J. Biodiversitas 2022, 23, 1828–1835. [Google Scholar] [CrossRef]
  40. Sandhu, H.S.; Cherry, R.H. Effects of tillage practices on arthropod ground predators in Florida sugarcane. J. Entomol. Sci. 2014, 49, 415–419. [Google Scholar] [CrossRef]
  41. Platen, R.; Konrad, J.; Glemnitz, M. Novel energy crops: An opportunity to enhance the biodiversity of arthropod assemblages in biomass feedstock cultures? Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 2017, 13, 162–171. [Google Scholar] [CrossRef]
  42. Brooks, D.R.; Clark, S.J.; Perry, J.N.; Bohan, D.A.; Champion, G.T.; Firbank, L.G.; Haughton, A.J.; Hawes, C.; Heard, M.S.; Woiwod, I.P. Invertebrate biodiversity in maize following withdrawal of triazine herbicides. Proc. R. Soc. B Biol. Sci. 2005, 272, 1497–1502. [Google Scholar] [CrossRef]
  43. Ebeling, A.; Hines, J.; Hertzog, L.R.; Lange, M.; Meyer, S.T.; Simons, N.K.; Weisser, W.W. Plant diversity effects on arthropods and arthropod-dependent ecosystem functions in a biodiversity experiment. Basic. Appl. Ecol. 2018, 26, 50–63. [Google Scholar] [CrossRef]
  44. Semere, T.; Slater, F.M. Ground flora, small mammal and bird species diversity in miscanthus (Miscanthus × giganteus) and reed canary-grass (Phalaris arundinacea) fields. Biomass Bioenergy 2007, 31, 20–29. [Google Scholar] [CrossRef]
  45. Batáry, P.; Dicks, L.V.; Kleijn, D.; Sutherland, W.J. The role of agri-environment schemes in conservation and environmental management. Conserv. Biol. 2015, 29, 1006–1016. [Google Scholar] [CrossRef]
  46. Evans, J.R. Improving photosynthesis. Plant Physiol. 2013, 162, 1780–1793. [Google Scholar] [CrossRef] [PubMed]
  47. Burmeister, J.; Panassitm, B.; Heine, F.; Wolfrum, S.; Moriniere, J. Perennial alternative crops for biogas production increase arthropod abundance and diversity after harvest–results of suction sampling and metabarcoding. Eur. J. Entomol. 2023, 120, 59–69. [Google Scholar] [CrossRef]
  48. Landis, D.A.; Werling, B.P. Arthropods and biofuel production systems in North America. Insect Sci. 2010, 17, 220–236. [Google Scholar] [CrossRef]
  49. dos Santos, L.A.O.; Naranjo-Guevara, N.; Fernandes, O.A. Diversity and abundance of edaphic arthropods associated with conventional and organic sugarcane crops in Brazil. Fla. Entomol. 2017, 100, 134–144. [Google Scholar] [CrossRef]
  50. Silva, A.B.D.; Batista, J.D.L.; Brito, C.H.D. Capacidade predatória de Euborellia annulipes (Lucas, 1847) sobre Spodoptera frugiperda (Smith, 1797). Acta Sci. Agron. 2009, 31, 7–11. [Google Scholar] [CrossRef]
  51. Schatz, B.; Kjellberg, F.; Nyawa, S.; Hossaert-McKey, M. Fig wasps: A staple food for ants on Ficus. Biotropica 2008, 40, 190–195. [Google Scholar] [CrossRef]
  52. Li, S.; Barreiro, A.; Almeida, J.P.; Prade, T.; Mårtensson, L.-M.D. Perennial crops shape the soil microbial community and increase the soil carbon in the upper soil layer. Soil. Biol. Biochem. 2025, 200, 109621. [Google Scholar] [CrossRef]
  53. Burmeister, J. Promotion of ground beetles by integrating perennial energy crops into existing agricultural landscapes. Biomass Bioenergy 2021, 146, 105973. [Google Scholar] [CrossRef]
  54. Radzikowski, P.; Matyka, M.; Berbeć, A.K. Biodiversity of weeds and arthropods in five different perennial industrial crops in eastern Poland. Agriculture 2020, 10, 636. [Google Scholar] [CrossRef]
  55. Lask, J.; Magenau, E.; Ferrarini, A.; Kiesel, A.; Wagner, M.; Lewandowski, I. Perennial rhizomatous grasses: Can they really increase species richness and abundance in arable land?—A meta-analysis. GCB Bioenergy 2020, 12, 968–978. [Google Scholar] [CrossRef]
  56. Altieri, M.; Whitcomb, W. Manipulation of insect populations through seasonal disturbance of weed communities. Prot. Ecol. 1979, 1, 185–202. [Google Scholar]
  57. Chao, A.; Chiu, C.-H.; Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annu. Rev. Ecol. Evol. Syst. 2014, 45, 297–324. [Google Scholar] [CrossRef]
Agronomy 15 01685 g001
Figure 1. Daily trap catch was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
Figure 1. Daily trap catch was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
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Figure 2. Taxonomic orders were analyzed for abundance across all the treatments and sampling times in different cropping systems. Different letters on the bars represent significant differences (α = 0.05) between the crops across all sampling times and treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW and SWF samplings.
Figure 2. Taxonomic orders were analyzed for abundance across all the treatments and sampling times in different cropping systems. Different letters on the bars represent significant differences (α = 0.05) between the crops across all sampling times and treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW and SWF samplings.
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Agronomy 15 01685 g003
Figure 3. Taxonomic orders were analyzed for abundance across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
Figure 3. Taxonomic orders were analyzed for abundance across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
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Agronomy 15 01685 g004
Figure 4. Order richness was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
Figure 4. Order richness was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
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Figure 5. Shannon diversity was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
Figure 5. Shannon diversity was analyzed across all the treatments in different cropping systems. S1 refers to after planting/after germination sampling, S2 refers to mid-seasonal sampling, and S3 refers to before crop harvesting sampling. Different letters on the bars represent significant differences (α = 0.05) between the crops across all treatments. Error bars represent the standard error of the mean. Sweetcorn includes SW (S1) and SWF (S2, S3) samplings.
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Table 1. Summary of total trap deployment and catch of soil arthropods in the plant cane and the first ratoon. PC—Plant Cane (First Year), FR—First Ratoon (Second Year).
Table 1. Summary of total trap deployment and catch of soil arthropods in the plant cane and the first ratoon. PC—Plant Cane (First Year), FR—First Ratoon (Second Year).
Time of Traps DeploymentDuration of Deployment (Days)ECSCSW ECSCSW
PCFRPCFRPCFRPCFRTotalPCFRPCFRPCFRTotal
After Planting / After Regrowth (S1)3015161616161616961271518110750814605385402
Mid-Seasonal (S2)301516161616161696859512111147313826184955
Before harvesting (S3)30151616161616169667566852947413006024248
Total904548484848484828828051698274714554142175814,605
Table 2. Summary of ANOVA (fixed effects) for various response variables measured in the plant cane for EC, SC, and SW. Crop refers to the different crops, treatment refers to the fertilization treatments, and sampling time refers to different samplings in both years. * showed the interaction between two fixed effects.
Table 2. Summary of ANOVA (fixed effects) for various response variables measured in the plant cane for EC, SC, and SW. Crop refers to the different crops, treatment refers to the fertilization treatments, and sampling time refers to different samplings in both years. * showed the interaction between two fixed effects.
Response Variable Fixed Effect (p-Values)
Crop (C)Treatment (T)Sampling Time (S)C * TC * ST * SC * T * S
Daily Trap Catch<0.0010.837<0.0010.4360.2930.9610.998
Order Richness<0.0010.238<0.0010.63<0.00010.940.99
Shannon Diversity0.0460.935<0.0010.804<0.0010.9380.989
Table 3. Summary of ANOVA (fixed effects) for various response variables measured in the first ratoon for EC, SC, and SW. Crop refers to the different crops, Treatment refers to the fertilization treatments, and sampling time refers to different samplings in both years. * showed the interaction between two fixed effects.
Table 3. Summary of ANOVA (fixed effects) for various response variables measured in the first ratoon for EC, SC, and SW. Crop refers to the different crops, Treatment refers to the fertilization treatments, and sampling time refers to different samplings in both years. * showed the interaction between two fixed effects.
Response Variable Fixed Effect (p-Values)
Crop (C)Treatment (T)Sampling Time (S)C * TC * ST * SC * T * S
Daily Trap Catch0.0240.2440.7850.2670.5910.4260.804
Order Richness0.0010.090.0030.830.040.6010.966
Shannon Diversity<0.0010.197<0.0010.8260.0860.5830.689
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Sharma, A.S.; Lesmes-Vesga, R.A.; Kaur, S.; Singh, H.; Sandhu, H.S. Effect of Energycane Integration on Ground-Dwelling Arthropod Biodiversity in a Sugarcane-Sweet Corn Cropping System. Agronomy 2025, 15, 1685. https://doi.org/10.3390/agronomy15071685

AMA Style

Sharma AS, Lesmes-Vesga RA, Kaur S, Singh H, Sandhu HS. Effect of Energycane Integration on Ground-Dwelling Arthropod Biodiversity in a Sugarcane-Sweet Corn Cropping System. Agronomy. 2025; 15(7):1685. https://doi.org/10.3390/agronomy15071685

Chicago/Turabian Style

Sharma, Amandeep Sahil, Ricardo A. Lesmes-Vesga, Simranjot Kaur, Hardeep Singh, and Hardev Singh Sandhu. 2025. "Effect of Energycane Integration on Ground-Dwelling Arthropod Biodiversity in a Sugarcane-Sweet Corn Cropping System" Agronomy 15, no. 7: 1685. https://doi.org/10.3390/agronomy15071685

APA Style

Sharma, A. S., Lesmes-Vesga, R. A., Kaur, S., Singh, H., & Sandhu, H. S. (2025). Effect of Energycane Integration on Ground-Dwelling Arthropod Biodiversity in a Sugarcane-Sweet Corn Cropping System. Agronomy, 15(7), 1685. https://doi.org/10.3390/agronomy15071685

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