View of Soil Properties and Macrofauna Community in a Converted Intensive Rice Field into an Organic Polyculture in Malang Regency, Indonesia

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2024, Vol. 14, No. 1, 119 – 130 http://dx.doi.org/10.11594/jtls.14.01.13

How to cite:

Inayah D, Mustafa I, Arisoesilaningsih E (2024) Soil Properties and Macrofauna Community in a Converted Intensive Rice Research Article

Soil Properties and Macrofauna Community in a Converted Intensive Rice Field into an Organic Polyculture in Malang Regency, Indonesia

Durrotul Inayah*, Irfan Mustafa, Endang Arisoesilaningsih

Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Malang, 65145, Indonesia

Article history:

Submission November 2022 Revised July 2023

Accepted July 2023

ABSTRACT

Farmers in Malang cultivated rice intensively due to water availability, but yields reduced gradually. It might also reduce soil productivity and increase pest attacks.

Sorghum is one of the easiest crops to be cultivated, and Leguminosae are known as pioneer cover crops. Therefore, the conversion field to organic polyculture was needed using sorghum and legumes. The research aims were to evaluate soil fauna dynamics and soil properties in the 3, 6, and 12 months after converting (mac) into an organic polyculture. Micro-climate and soil properties were recorded including air temperature (°C), day length (hours), rainfall (mm), soil water content (%), organic matter content (%), electrical conductivity (mS.m^-1), pH, and bulk density (g.cm^-3). Soil macrofauna was sampled using hand sorting for five plots (20 × 20 × 10 cm) in each field. Identified soil macrofauna was used to determine the density, frequency, Important Value Index (IVI), Shannon Diversity Index (H'), Evenness Index (E), Simpson Dominance Index (D), diversity t-test, and Indicator Value. The Canonical Correspondence Analysis (CCA) was used to analyze the interaction among abiotic properties and macrofauna using PAST 4.05. Results showed that the improvement of soil properties, including soil organic matter and soil macrofauna, was recorded at six mac compared to the intensive rice field, and continuously at 12 mac. The richness, diversity, and evenness of soil macrofauna taxa were higher in the converted field than in the intensive one due to organic polyculture. Moreover, we recorded a better proportion of detritivores and predators in the converted field after 12 months. Based on Indicator Value analysis, the dominant fire ants (Solenopsis sp.) in the intensive rice field might be considered as a potential indicator of unhealthy soil in the intensive rice fields. Whereas in the converted field the dominancy of these ants greatly decreased. We concluded that within six months conversion using the organic polyculture improved soil properties.

Keywords: Intensive rice field conversion, Macrofauna, Organic polyculture, Soil properties

*Corresponding author:

E-mail: [email protected]

Introduction

Intensive rice farming provides high crop yields, but it also promotes the accumulation of chemical residues from synthetic fertilizers and pesticides. It decreases soil properties, including fertility and long-term sustainability, and provides a negative impact on wildlife and humans [1-4].

Besides, nutrient exploitation in the intensive rice field might reduce soil organic matter and the high risk of erosion [5]. The absence of crop rotation might lead to high pathogen attacks. Crop rotation

is very important for enriching field biodiversity due to renewable community structures [6] and maintaining the soil biota diversity by increasing soil fertility through soil chemical attributes [7]

and a stable food web [8]. Synthetic fertilizers and pesticides are also a cause of soil degradation due to the reduction of indigenous microbes, which are important for increasing soil fertility [9, 10].

Farmers in Randuagung, Singosari, and Ma-

lang have intensively cultivated rice since a long

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time ago due to water availability, but yields are continuously decreasing because of reduced soil productivity; therefore, field conversion was needed. The polyculture system was familiarly used to increase soil productivity. Here, sorghum and some Leguminosae were planted organically to convert the intensive rice field within a year.

Sorghum is easy to cultivate and can be harvested twice in one crop [11, 12], and legumes are widely known to enrich the soil microbes and available nutrients [13-17]. According to Lembaga Sertifi- kasi Organik (LSO), the shortest period to convert a field from inorganic to organic with a monoculture system is one year, but the products may not be completely organic due to inorganic chemical residues. The field was converted for a year and needed to be evaluated and compared with the original rice field. In this study, we evaluated the improvement of physicochemical soil properties and biological indicators using soil macrofauna to assess soil productivity. Soil fauna has important roles in soil fertility through the decomposition process, nutrient cycle, organic matter content, porosity, and infiltration [18-20]. Soil fauna have been utilized as bioindicators in many studies because they are easy to identify, sensitive to changes in soil quality, and inexpensive [21-25].

Therefore, this research aims to evaluate the converted field and compare it to the origin-intensive rice field using soil macrofauna as bioindicators.

The results of this study are expected to be

considered for the management of rice fields in the following year.

Material and Methods Study area

This research was conducted from July 2021 to August 2022 at Randuagung, Singosari, Ma- lang, East Java, Indonesia 7°51'27.95"S 112°40'49.64"E (Figure 1), which was divided into two areas. The first area was intensive rice fields and the second was newly converted fields implementing organic and polyculture management through the planting of sorghum and legumes. The agricultural irrigation system is sup- plied by the river and is close to Suko's water spring, which is located in a sacred forest at 560 meters above sea level (AMSL).

Determine the profile of abiotic factors

Secondary microclimate data was obtained from the Pusat Data Base Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) Ka- rangploso, Malang, and consisted of temperature (°C), day length (hours), and rainfall (mm). The edaphic factor was measured with five points at each location including water content (%) using the [26] method, soil organic matter (%) based on [27-29], soil pH [30], conductivity (dS.m

^-1

) [31], and soil bulk density (g.cm

^-3

) using the core method. The soil conductivity analysis was carried out using the [31] method by dissolving soil in

(a)

(b)

(c)

Figure 1. The research location: Randuagung, Singosari, Malang Regency (a), intensive rice field (b), and converted field (c)

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distilled water (1 : 5). The soil bulk density analysis was carried out using the [32] method by deter- mining the soil mass at a certain volume. The soil sampling was conducted before plantation/conversion and three, six, and 12 months after conversion (mac).

Soil macrofauna analysis

Hand sorting was performed at five points at each location using a 20 × 20 cm grid with a depth of 10 cm [33]. Each soil macrofauna found was preserved in a bottle containing 70% alcohol.

Further observations and identification were conducted at the Laboratory of Ecology and Tropical Ecosystem Conservation, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Malang. Soil fauna was identified using [34, 35] and other references.

Data analysis

Data from soil macrofauna were analyzed to determine the Important Value Index (INP), Shan- non Diversity Index (H') [36], Evenness Index (E) [37], Simpson's Dominance Index (D) [36],

Diversity t-test and Indicator Species (PAST 4.05) [38]. Abiotic factors data between locations were descriptively analyzed using Excel and presented in graphs. The interaction between the soil macrofauna and the abiotic factors was analyzed using Canonical Correspondence Analysis (CCA) [39] using PAST 4.05 [38].

Results and Discussion Profile of abiotic factors

The temperature increased from July to Octo- ber 2021 and then fluctuated until February 2022 (Figure 2a) because July to early October 2021 was a dry season, and reached 24.6°C in October.

October was a transition month from the dry season to the rainy season as BMKG predicted [40].

The start of the rainy season in Java Island occurred in October-November, and specifically, the Malang area occurred in the 4

^th

week of October [41]. The clouds that contain water as an effect of the west monsoon prevent heat within the earth [42], causing the temperature to rise. The temperature was continuously high until May and the highest temperature was 24.9°C. The start of the rainy season at the end of October was supported

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(b)

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Figure 2. Variation of monthly microclimate in Singosari District: (a) Air temperature (°C), (b) Rainfall (mm), and (c) Day length (hour).

20 22 24 26

Temperature (°C)

0 500 1000 1500

Rainfall (mm)

0 2 4 6 8 10

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Day length (hour)

Time

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by increasing rainfall (Figure 2b) as BMKG pre- diction [40, 41]. The highest rainfall occurred in December (1153.76 mm) which was earlier than the BMKG prediction regarding the peak of the rainy season in the Malang area in January [43].

The day length decreased towards the rainy season due to cloudiness which blocked sunlight to reach the earth. However, the end of the rainy season in 2022 could not be clearly identified due to fluctuating rainfall, whereas BMKG predicted the start of the dry season in the Malang area was in April- May and the peak of the dry season was in August [44, 45], but the rainfall was still high until Au- gust, and reached 899.07 mm in June. This was a climate anomaly caused by weak La Nina from July to August [46]. The decreased temperature in June-August was due to the peak of the dry season [47].

The water content in the intensive rice fields was higher than in the conversion fields (Figure 3a) due to the different water requirements of the crop. Rice required more water than sorghum and Legumes [48, 49]. The fluctuating water content was also caused by the rainfall. Soil organic matter in July was higher than in the following months (Figure 3b) because the soil samples were taken when the soil was ready for plantation. The converted field was fertilized by farmyard manure

and the intensive rice field content retained root remnants. Soil organic matter in converted rice fields was higher than in intensive rice fields. This condition was due to crop residue management. In the converted field, crop residues were chopped and returned to the soil, while in the intensive rice field the crop residues were used as animal feed.

This condition was also caused by the difference in the plant community. The conversion field was more diverse than the intensive rice field. The high diversity of plants might increase the diversity of biomass including types of organic compounds around the rhizosphere that trigger soil microbial activity and carbon storage [50, 51]. In addition, based on [52] the high diversity of plants might increase extractible soil organic compounds (ESOC) which can attract more diverse soil microbes. According to [53] land with functional plants in the form of Leguminosae, grass, and forbs increased the accumulation of N, K, Ca, and Mg in total nutrient sources (plant and soil biomass).

The soil pH was varied slightly at the observed fields (Figure 3c). The soil pH in the converted field was more acid (6.28-7.13) than in the inten- sive rice fields (6.80-7.28) due to the binding and release of H+ ions in the soil-by-soil organic mat- ter [54]. High organic matter in the soil caused

(a) (b)

(c) (d)

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Figure 3. Soil profile in the rice and converted fields. (a) Water content (%), (b) SOM (soil organic matter in

%), (c) soil pH, (d) EC (electrical conductivity in dS.m^-1), and (e) ρs (soil bulk density in g.cm^-3).

0 20 40 60

July October January Agustus

Water Content (%)

Time

Intensive Field Converted Field

0 5 10 15 20

SOM (%)

Time

5.5 6 6.5 7 7.5

Soil pH

Time

0.0 0.2 0.4 0.6

EC dS.m-1

Time

0 0.2 0.4 0.6 0.8 1

ρs (g.cm-3)

Time

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acidity in the soil, but the soil pH in both fields was still suitable for agriculture (5.5-7.5) [55].

Small variations were also observed in electrical conductivity and soil bulk density (Figure 3d-e).

The electrical conductivity in the converted field was higher than in the intensive rice field. It indicated that the salinity related to nutrient availability in the converted field was higher than in the intensive rice field [56]. The soil bulk density of converted rice fields was higher than that of intensive rice fields. The soil bulk density represents/

correlated with the soil porosity [57], which indi- cates that the soil porosity of intensive rice fields was higher than converted fields. One of the factors that affect soil bulk density was the soil organic matter. The soil organic matter maintains soil stability through the adhesion properties of soil particles [18-20]. Low soil organic matter causes erosion and soil cracks during the dry season, as observed in intensive rice fields.

Profile of soil macrofauna

The number of taxa found in the three months after converting (mac) was less than in the six and 12 macs, it was correlated to the low value of the Shannon Diversity Index, Evenness Index, and high Simpson Dominance Index (Table 1 & Sup- plementary 1). This was influenced by seasonal factors i.e. rainfall and temperature. Low rainfall and high temperatures caused dry and hot condi- tions in the soil, thus providing an uncomfortable habitat for soil fauna [58]. The day length also af- fected the soil moisture through evaporation, whereas optimal soil moisture will support soil fauna in litter decomposition [59].

The diversity and distribution of soil macrofauna were higher in converted fields than in intensive rice fields (Table 1 and Figure 4). This was influenced by organic polyculture, in which the application of farmyard manure was widely known to increase soil fertility through physical,

Table 1. Soil fauna diversity and dominance in 3, 6, and 12 months after converting into polyculture

Type of

Fields Month after Con-

version (mac) Taxa Rich-

ness (Genera) Density Mean

(individual) Shannon Diver-

sity Index Dominance Index

Intensive 3 3 23 0.28^a 0.87^c

6 10 13 1.21^b 0.50^b

12 12 4 1.52^c 0.40^b

Converted 3 5 5 1.20^b 0.38^b

6 14 4 2.12^d 0.16^a

12 18 5 2.20^d 0.16^a

Notes: 3-12 = months after converting, S = taxa richness, H’ = Shannon Diversity Index, E = Evenness index, and D = Simpson Dominance Index.

Figure 4. Profile of soil macrofauna in the converted and intensive rice field. Sol: Solenopsis, 3-12: month after converting.

Sol

Sol Sol Sol

0 20 40 60 80 100 120 140 160 180 200

3 6 12 3 6 12

Intensive Converted

Important Value Index (%)

Solenopsis Symphyla Enchytraeids Paederus Dolichoderus Monomorium

Pheretima Gryllus Rhodopodesmus Neoribates Bilobella Herpyllus

Dinothrombium Gaeolaelaps Cylisticus Lachnosterna Tygarrup Blatella

Gryllotalpa Lepidocyrtus Nesticodes Dystrichothorax

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chemical, and biological properties [60].

According to [61] 15 years of farmyard manure treatment might increase soil organic carbon, total nitrogen, and microbial activity through enzyme assays. The farmyard manure might also increase the abundance and biomass of soil fauna through the soil organic matter [62-67]. The diversity of crops might also contribute by providing several microhabitats for soil fauna with different niches, including food sources, thus maintaining the food web in the ecosystem [68]. This condition is illustrated in Figure 5, that predators in the intensive rice field were higher along the observation time than in the converted field.

Although both fields showed a decrease in predators and an increase in detritivores along observation, the niche composition was more stable in the converted field. It was indicated that the number of pests was high in the intensive rice field as an effect of monoculture and might trigger farmers to add synthetic pesticides as described by

[69, 70]. Fire ants were discovered to be the most abundant predator.

The fire ants (Solenopsis sp.) are a member of the Formicidae and live in the supercolony because of their very high density and lack of terri- toriality with their polygynous and polydomous character. They are an invasive species from South America [71]. Therefore, the presence of fire ants might reduce the diversity of other ants and other soil fauna. The Solenopsis sp. is a predator of in- vertebrates, vertebrates, and plants, and based on [72] the most prey was Hemiptera and Lepidoptera pests, as well as eggs or young of the golden snail.

In addition, based on [73] Solenopsis sp. prey, it was > 20% lepidopteran larvae. Fire ants were mostly found in areas with human disturbance and extreme environments, while this species was not found in natural forests [74]. Therefore, fire ants were a bioindicator in the intensive rice fields, which was strongly confirmed by Indicator Spe- cies analysis (Figure 6). On the other hand, the

Figure 5. Profile of soil macrofauna niches in the converted and intensive rice field. 3-12: months after con-

verting

Figure 6. Potential soil macrofauna indicator based on IndVal analysis. C: converted field, I: intensive rice field, 3-12: months after converting.

0 50 100 150 200

3 6 12 3 6 12

Intensive Converted

Important Value Index (%)

Detritivore Herbivore

Predator Scavenger

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detritivores were higher in the converted field than in the intensive rice field. It indicated that the decomposition was high in the converted field, which was supported by high soil organic matter.

The CCA analysis (Figure 7) showed that converted fields were characterized by soil or- ganic matter content, supported by the high diver- sity and evenness of soil macrofauna especially detritivores. Intensive rice fields were distin- guished by high water content, soil pH, and the Simpson Dominance Index, with fire ants and other predators as the dominant taxa.

It also showed that the diversity of soil macrofauna was also affected by soil organic matter and soil bulk density. Organic matter was produced from litter and plant residue by soil fauna through decomposition (food, mesh, etc.) (75-77).

On the other hand, water content affected the Simpson Dominance Index. The results of this study indicated that field conversion efforts within six macs have been able to increase the diversity of soil macrofauna and then progressively improve in 12 macs even though the soil quality did not show significant improvement (soil organic matter). The results of this study support previous research [53] that cereal crops combined with Le- guminosae were quite effective in improving soil productivity, as shown by the structure of the soil macrofauna.

Conclusion

The organic polyculture system was able to improve soil productivity within 6 months after converting (mac) and to increase in 12 mac by planting sorghum and Leguminosae as an effort to convert intensive rice fields, as demonstrated by the high diversity and evenness of soil macrofauna. Improvement was also shown in the soil organic matter. This was influenced by the farmyard manure and plant crop diversity which created various microhabitats and produced more diverse litter biomass. The diversity of litter was the main factor for various detritivore macrofauna, which were then processed into soil organic matter by decomposers. Both beneficial crops might be used in land reclamation or conversion.

Acknowledgments

The authors would like to thank Universitas Brawijaya for the research grant. The authors also would like to thank Mr. Febri who is owner of organic rice field as a reference site and Mrs.

Achada as owner of intensive rice field. The re- searchers would also thank for valuable feedbacks of reviewers and colleagues of the Laboratory of Ecology and Conservation of Tropical Ecosys- tems, Universitas Brawijaya.

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Supplementary 1

Table 2. Taxa found in the converted and intensive rice field

No. Taxa Niche Reference

1. Bilobella Detritivor [78]

2. Cylisticus Detritivor [79]

3. Enchytraeids Detritivor [80&81]

4. Gryllus Detritivor [82]

5. Lepidocyrtus Detritivor [83]

6. Neoribates Detritivor [84]

7. Pheretima Detritivor [85&86]

8. Rhodopodesmus Detritivor [87&88]

9. Symphyla Detritivor [89&90]

10. Dystrichothorax Herbivore [91]

11. Gryllotalpa Herbivore [92]

12. Lachnosterna Herbivore [93-96]

13. Blatella Omnivor [97]

14. Dinothrombium Predator [98]

15. Dolichoderus Predator [99-101]

16. Gaeolaelaps Predator [102-104]

17. Herpyllus Predator [105]

18. Monomorium Predator [106]

19. Nesticodes Predator [107]

20. Paederus Predator [108]

21. Solenopsis Predator [109]

22. Tygarrup Predator [110]